Lightning detection and data acquisition system Number:6,788,043 from the United States Patent and Trademark Office (PTO) owispatent A lightning detection and data acquisition system. A plurality of remote programmable sensor is utilized to detect cloud to ground and IC lightning strikes. Analog representations of the lightning strikes are converted to digital signals. The digital signals are classified according to user changeable criteria. The classified digital signals are compressed and optionally decimated. The compressed information is transmitted to a central location where it is decompressed and used to correlate the location, magnitude, and travel path of the detected lightning strikes.<H acquisition, detection, Lightning, detec, 6788043.html, Patent, pto, 6788043

Title: Lightning detection and data acquisition system

Abstract: A lightning detection and data acquisition system. A plurality of remote programmable sensor is utilized to detect cloud to ground and IC lightning strikes. Analog representations of the lightning strikes are converted to digital signals. The digital signals are classified according to user changeable criteria. The classified digital signals are compressed and optionally decimated. The compressed information is transmitted to a central location where it is decompressed and used to correlate the location, magnitude, and travel path of the detected lightning strikes.

Patent Number: 6,788,043 Issued on 09/07/2004 to Murphy, et al.

Inventors: Murphy; Martin J. (Tucson, AZ), Cummins; Kenneth L. (Tucson, AZ), Pifer; Alburt E. (Tucson, AZ)
Assignee: Vaisala Oyj (Helsinki, FI)

Appl. No.: 10/074,863

Filed: February 13, 2002

Current U.S. Class: 324/72 ; 324/122; 324/123R; 342/460; 342/465; 73/170.24

Current International Class: G01R 29/08 (20060101)

Field of Search: 324/72,76.11,76.12,76.13,76.82,87,76.25,457-458 342/460,465 73/170.24 702/4

References Cited [Referenced By]

U.S. Patent Documents


4115732	September 1978	Krider et al.
4198599	April 1980	Krider et al.
4245190	January 1981	Krider et al.
4455613	June 1984	Shoemaker
4543580	September 1985	Bent et al.
4674062	June 1987	Premerlani
4792806	December 1988	Bent et al.
4873483	October 1989	Ostrander
4876551	October 1989	Climent et al.
4914444	April 1990	Pifer et al.
5036334	July 1991	Henderson et al.
5295071	March 1994	Kuzma et al.
5537318	July 1996	Moses et al.
6164130	December 2000	Pabst et al.
6215294	April 2001	Coleman
6246367	June 2001	Markson et al.
6492929	December 2002	Coffey et al.
6625399	September 2003	Davis

Primary Examiner: Deb; Anjan K.
Attorney, Agent or Firm: Birdwell & Janke, LLP

Claims

We claim:

1. A lightning detection system, comprising: a source of an electrical detection signal representative of an electromagnetic field from a lightning flash comprising a series of lightning discharges; an analog-to-digital converter, responsive to said electrical detection signal, for producing a digital detection signal representative of said electromagnetic field; and a digital processor, responsive to said digital detection signal, for determining the type of at least one of said lightning discharges that produced said electromagnetic field based on characteristics of said digital detection signal, said digital processor continually processing said digital detection signal so as to eliminate dead time between said lightning discharges.

2. The lightning detection system of claim 1, further comprising a non-linear amplifier, responsive to said electrical detection signal, for producing an amplitude compressed electrical detection signal having a reduced dynamic amplitude range prior to application to said analog-to-digital converter.

3. The lightning detection system of claim 2, wherein said non-linear amplifier is a logarithmic amplifier.

4. The lightning detection system of claim 2, wherein said non-linear amplifier is a piece-wise linear amplifier.

5. The lightning detection system of claim 1, wherein said digital processor employs said digital detection signal representative of said electromagnetic field to identify maxima and minima of the waveform of said digital detection signal, and identifies zero crossings of said digital detection signal.

6. The lightning detection system of claim 5, herein said digital processor distinguishes between cloud-to-ground and intra-cloud lightning discharges.

7. The lightning detection system of claim 1, wherein said digital processor produces digital data characterizing said series of lightning discharges derived from said digital detection signal, and said system further comprises a data transmission component for transmitting said digital data over a communications channel.

8. The lightning detection system of claim 7, wherein said transmission component includes a data compression component for reducing the amount of said digital data so as to decrease the time or bandwidth required to transmit a said digital data.

9. The lightning detection system of claim 8, wherein said data compression component minimally transmits, for said series of discharges, a sufficient quantity of said digital data to identify the amplitude of the largest pulse produced thereby and the time when said largest pulse occurred.

10. The lightning detection system of claim 7, comprising a plurality of sources at different locations, wherein said data transmission component includes a data decimation component for synchronously decimating said digital data when needed to accommodate the bandwidth of said communications channel.

11. The lightning detection system of claim 1, further comprising a non-linear amplifier, responsive to said electrical detection signal, for producing an amplitude compressed electrical detection signal having a reduced amplitude dynamic range for application to said analog-to-digital converter for converting said amplitude compressed electrical detection signal to a corresponding digital detection signal.

12. The lightning detection system of claim 11, wherein said non-linear amplifier is a logarithmic amplifier.

13. The lightning detection system of claim 11, wherein said non-linear amplifier is a piece-wise linear amplifier.

14. The lightning detection system of claim 11, wherein said digital processor employs said digital detection signal to identify maxima and minima of the waveform of said amplitude compressed digital detection signal, and identifies zero crossings of said digital detection signal.

15. The lightning detection system of claim 14, wherein said digital processor distinguishes between cloud-to-ground and intra-cloud lightning discharges.

16. The lightning detection system of claim 11, wherein said digital processor produces digital data characterizing said series of lightning discharges derived from said digital detection signal and said system further comprises a data transmission component for transmitting said characterizing digital data over a communications channel.

17. The lightning detection system of claim 16, wherein said transmission component includes a data compression component for reducing the amount of said digital data so as to decrease the time or bandwidth required to transmit said digital data over said communications channel.

18. The lightning detection system of claim 17, wherein said data compression component minimally transmits, for said series of discharges, a sufficient quantity of said digital data to identify the amplitude of the largest pulse produced thereby and the time when said largest pulse occurred.

19. The lightning detection system of claim 16, comprising a plurality of sources at different locations, wherein said data transmission component further comprises a data decimation component for synchronously decimating said characterizing digital data when needed to accommodate the bandwidth of said communications channel.

20. The lightning detection system of claim 1, further comprising a circuit for producing, as said electrical detection signal, a signal representative of the derivative of said electromagnetic field.

21. The lightning detection system of claim 20, further comprising an amplifier, responsive to said electrical detection signal, for producing an amplitude compressed electrical detection signal having a reducing the dynamic amplitude range for application to said analog-to-digital converter for converting said amplitude compressed electrical detection signal to a corresponding digital detection signal.

22. The lightning detection system of claim 21, wherein said amplifier is a logarithmic amplifier.

23. The lightning detection system of claim 22, wherein said amplifier is a piece-wise linear amplifier.

24. The lightning detection system of claim 20, wherein said digital processor includes an integration element for digitally integrating said digital detection signal and thereby producing an integrated digital detection signal, said processor using both said digital detection signal representative of the derivative of said electromagnetic field and said integrated digital detection signal to determine the type of said at least one of said lightning discharges.

25. The lightning detection system of claim 24, wherein said digital processor employs said digital detection signal representative of the derivative of said electromagnetic field to identify maxima and minima of the waveform of said integrated digital detection signal, and identifies zero crossings of said integrated digital detection signal from said integrated digital detection signal itself.

26. The lightning detection system of claim 25, wherein said digital processor distinguishes between cloud-to-ground and intra-cloud lightning discharges.

27. The lightning detection system of claim 1, further comprising a compression circuit for dynamic range compression of said electrical detection signal prior to application thereof to said analog-to-digital converter.

28. The lightning detection system of claim 27, wherein said digital processor is adapted for operating on said digital detection signal to reverse the dynamic range compression produced by said analog compression module.

29. A method for detecting lightning, comprising: producing in response to an electromagnetic field from a lightning flash comprising a series of lightning discharges an electrical detection signal representative of said electromagnetic field; producing, in response to said electrical detection signal, a digital detection signal representative of said electromagnetic field; and determining the type of at least one of said lightning discharges that produced said electromagnetic field based on characteristics of said digital detection signal while continually processing said digital detection signal so as to eliminate dead time between said lightning discharges.

30. The lightning detection method of claim 29, further comprising producing from said electrical detection signal an amplitude compressed electrical detection signal having a reduced amplitude dynamic range prior to producing said digital detection signal.

31. The lightning detection method of claim 30, wherein said amplitude compressed electrical signal is produced by logarithmic amplification.

32. The lightning detection method of claim 30, wherein said amplitude compressed electrical signal is produced by piece-wise linear amplification.

33. The lightning detection method of claim 29, further comprising identifying maxima and minima and zero crossings of said digital detection signal.

34. The lightning detection method of claim 33, further comprising distinguishing between cloud-to-ground and intra-cloud lightning discharges based on said maxima and minima and zero crossings.

35. The lightning detection method of claim 29, wherein said determining the type of said at least one lightning discharge includes deriving digital data from said digital detection signal, and said method further comprises transmitting said digital data over a communications channel.

36. The lightning detection method of claim 35, wherein said transmitting includes reducing the amount of said digital data so as to decrease the time or bandwidth required to transmit said digital data over said communications channel.

37. The lightning detection method of claim 36, further comprising minimally transmitting, for said series of discharges, a sufficient quantity of said digital data to identify the amplitude of the largest pulse in said digital data and the time when said largest pulse occurred.

38. The lightning detection method of claim 35, comprising producing a plurality of electrical detection signals from sources at different locations and further comprising synchronously decimating said digital data where needed to accommodate the bandwidth of said communications channel.

39. The lightning detection method of claim 29, further comprising producing an amplitude compressed electrical detection signal having a reduced amplitude dynamic range prior to producing said digital detection signal.

40. The method of claim 39, further comprising deriving digital data from said digital detection signal, and transmitting said digital data over a communications channel.

41. The lightning detection method of claim 40, further comprising reducing the amount of said digital data so as to decrease the time or bandwidth required to transmit said digital data over said communications channel.

42. The lightning detection method of claim 41, further comprising minimally transmitting, for said series of discharges a sufficient quantity of said digital data to identify the amplitude of the largest pulse produced thereby and the time when said largest pulse occurred.

43. The lightning detection method of claim 40, comprising producing a plurality of electrical detection signals from sources at different locations and further comprising synchronously decimating said digital data when needed to accommodate the bandwidth of said communications channel.

44. The method of claim 39, further comprising logarithmically amplifying said electrical detection signal to produce said amplitude compressed electrical detection signal.

45. The method of claim 39, further comprising piece-wise linearly amplifying said electrical detection signal to produce said amplitude compressed electrical detection signal.

46. The method of claim 39, further comprising identifying maxima and minima of said digital detection signal and identifying zero crossings of said digital detection signal.

47. The method of claim 46, further comprising distinguishing between cloud-to-ground and intra-cloud lightning discharges.

48. The lightning detection method of claim 29, wherein said electrical detection signal represents the derivative of said electromagnetic field.

49. The lightning detection method of claim 48, further comprising digitally integrating said digital detection signal and thereby producing an integrated digital detection signal, and using both said digital detection signal representative of the derivative of said electromagnetic field and said integrated digital detection signal to determine the type of said at least one of said lightning discharges.

50. The lightning detection method of claim 49, further comprising using said digital detection signal representative of the derivative of said electromagnetic field to identify maxima and minima of the waveform of said integrated digital detection signal, and identifying zero crossings of said integrated digital detection signal from said integrated digital detection signal.

51. The lightning detection method of claim 50, further comprising distinguishing between cloud-to-ground and intra-cloud lightning discharges.

52. The lightning detection method of claim 48, further comprising amplifying said electrical detection signal so as to reduce the dynamic amplitude range of said electrical detection signal prior to producing said digital detection signal.

53. The lightning detection method of claim 52, wherein said amplifying is accomplished by logarithmic amplification.

54. The lightning detection method of claim 52, wherein said amplifying is accomplished by piece-wise linear amplification.

55. The method of claim 29, further comprising compressing the dynamic range of said electrical detection signal prior to producing said digital detection signal.

56. The method of claim 55, further comprising reversing said step of compressing the dynamic range of said electrical detection signal in the digital domain.

Description

BACKGROUND OF THE INVENTION

This invention relates to lightning detection and data acquisition systems, and in particular to systems that provide continuous lightning detection and are programmable to allow for user-selectable evaluation criteria.

Lightning detection and data acquisition systems are used to detect the occurrence and determine the location of lightning discharges, and gather other data about the discharges. In traditional lightning detection systems, a plurality of sensors are placed tens to hundreds of kilometers apart to remotely detect the electric and magnetic fields of lightning discharges. Such discharges may be between a cloud and the ground ("CG") or within a cloud ("IC"). Information from the sensors is transmitted to a central location, where analysis of the sensor data is performed. Typically, at least the time of occurrence and location of the discharges are determined from data provided by a plurality of sensors.

Remote sensors of lightning detection and data acquisition systems typically detect electric and magnetic fields of both CG and IC lightning flashes, which are composed of many discharges. It is often important to be able to distinguish between the two types of flashes. To that end, remote sensors often look at the low-frequency ("LF") and very-low-frequency ("VLF") emissions from lightning discharges. The electrical signals produced by LF and VLF ("LF/VLF") detectors are ordinarily integrated prior to analysis to produce a waveform representation of the electric or magnetic discharge field, as the antenna inherently responds to the time derivative of the field. Analyzing signals representative of either an electric or magnetic field to distinguish CG and IC discharges is referred to as performing waveform analysis. There are several criteria for distinguishing between CG and IC events. One well known method for distinguishing lightning signals both in the LF and in the VLF range is to examine the time that passes from a peak in a representative signal to the instant it crosses a zero amplitude reference point. This is referred to as a peak-to-zero ("PTZ") method of analysis. A relatively short PTZ time is a good indication that an IC discharge has occurred. Another well known method of distinguishment is referred to as a bipolar test wherein the representative signal is examined for a first peak and a subsequent peak of opposite polarity which is greater than a predetermined fraction of the first peak. Such an occurrence is another good indication of an IC discharge. Yet another test for IC discharges is the presence of subsequent peaks of the same polarity in a representative signal greater than the initial peak. This is predicated on the fact that some IC discharges have a number of small and fast leading electromagnetic pulses prior to a subsequent larger and slower pulse. In the absence of such criteria indicating that the discharge is an IC discharge, it is ordinarily assumed to be a CG discharge. Even with the application of all established criterion for distinguishing between CG and IC events, some events are still misclassified.

An alternative method of lightning detection is to monitor very high frequency ("VHF") radiation from lightning discharges. However, VHF detection systems must be able to process information at extremely high data rates, as VHF pulse emissions in IC lightning occur approximately one tenth of a millisecond apart. Additionally, VHF systems can only detect lightning events that have direct line of sight to the sensor. One such system is currently in use by NASA at Kennedy Space Center in Florida. However, this system is further restricted to line of sight between the sensors and the central analyzer as it uses a real-time microwave communication system. Additionally, the VHF system in use by NASA has proven to be expensive to install and maintain.

Previous lightning detection and data acquisition systems for detecting low frequency electric field signals have been designed around a combination of two location methods, time-of-arrival ("TOA") and magnetic direction finding, with time-domain field waveform analysis. In most of these systems, the sensors are predominately analog devices. Using analog devices in lightning sensors requires the utilization of "track and hold" circuits to detect a qualifying event, capture a representative signal, and perform waveform analysis on it. Due to an accumulation of delay periods in these "track and hold" circuits, these sensors have a large "re-arm" time, or "dead-time", during which the sensors do not record subsequent lightning events. Even more modern lightning detection and data acquisition systems that are substantially digital have some dead time. For example, the sensors in some such systems have a "dead-time" of 5 to 10 milliseconds, and even the most current digital sensors have a "dead-time" of up to one millisecond. The latter are capable of detecting only a limited fraction of IC lightning discharges. This is due in part to the fact that several IC lightning discharges could occur in a single millisecond. CG lightning flashes, however, tend to have fewer discharges with relatively large periods of times between individual discharges. If a previous generation sensor is designed to monitor both CG and IC electric field signals, a significant portion of time is occupied processing IC discharge events at the expense of recording CG events. Another aspect associated with sensor dead times and the TOA location method is the uncertainty in assuring that multiple remote sensors will respond to the same IC lightning event. Due to attenuation suffered by electromagnetic waves as they travel long distances over the earth, remote small amplitude events become difficult to detect. If different sensors produce time-of-arrival information from different events, the computed discharge location will have significant error.

Analog sensors operating at LF/VLF frequencies are difficult to tune for both CG and IC lightning discharges. The median amplitude of a CG field signal is about an order of magnitude greater than the median amplitude of an IC field signal. Optimizing the gain of one of these sensors to detect IC events often causes the sensor to become saturated with the much greater energy of nearby CG lightning discharges. Therefore, it is customary to adjust the gain to accommodate both types of field signals, reducing a sensor's ability to detect IC events. As distant IC lightning discharges become attenuated by propagation over the ground, they become difficult to distinguish from background environmental noise.

In order for the lightning detection system to provide useful information in a timely manner, there must exist a method of transmitting sensor information to a central location. This central location must collect information from numerous remote sensors which is then correlated to establish the location, magnitude, and time of occurrence of lightning discharges. Existing detection systems generally have low-bandwidth communication systems, limiting the amount of information that a sensor can transmit to the central analyzer. In many existing lightning detection networks, the sensors are connected to a central location by low-speed telephone modems, usually 2400 to 9600 bits per second. In the past, this communication restriction was not overly critical, as the large dead-time of previous generation analog sensors limited the amount of information that could be collected and sent to the central analyzer.

Once the sensor information arrives at a central location, it must be analyzed. The information from each sensor is compared against incoming information from other sensors. This correlation process attempts to find corresponding data to determine the location, magnitude, and time of occurrence of lightning discharges. However, current correlation techniques are not sufficient to handle large amounts of information when the time between discharges is more than an order of magnitude shorter than the travel time between sensors. In fact, if a lightning detection system made use of advanced technologies to transmit and receive an increased amount of information, current central analyzers would be unable to process the information efficiently with current correlation techniques.

The state of the art of lightning detection and data acquisition systems is generally represented, in part, by several patents. First, Krider et al. U.S. Pat. Nos. 4,198,599 and 4,245,190 describe a network of gated wideband magnetic direction finding sensors. These sensors are sensitive to return strokes in CG lightning flashes. In U.S. Pat. No. 4,198,599, discrimination and classification is accomplished by examining the shape of the time-domain field waveform. A short rise time (time from threshold to peak) results in a representative signal being placed in an analog track and hold circuit while further analysis is performed. These sensors are designed with CG discharges being of primary interest. Any IC lightning discharges that are detected are discarded. However, both CG and IC events that meet the short rise time criteria and a simple test of event duration result in a significant amount of sensor dead-time.

Second, Bent et al. U.S. Pat. Nos. 4,543,580 and 4,792,806 disclose networks of sensors that measure TOA of electric field signals and employ this information to locate lightning. These sensors do not discriminate between IC and CG discharges. However, these sensors suffer the similar dead time issue as the magnetic direction sensors of the Krider patents. When a number of IC discharge pulses occur in a short time, there is no assurance that multiple sensors will respond to same IC discharge event.

Another patent of interest is Markson et al. U.S. Pat. No. 6,246,367 wherein a lightning detection system utilizes an analog-to-digital converter ("ADC") to provide continuous processing of representative field signals. This eliminates the dead time issue inherent in previous generation sensors. Markson describes using a bipolar comparator to distinguish between positive and negative polarity versions of a particular pulse that is inferred to be the first broadband radiation pulse in either a CG or an IC flash. Markson also uses a data correlation process and time-of-arrival difference location method. Markson explicitly uses a high pass filter to block most low frequency components of representative field signals, which are not necessarily useful for detecting the initial pulse in the flash. Limitations of the Markson patent are the specific use of the HF frequency range and detection and processing of only the first pulse in each flash.

Accordingly, there has been a need for improvement of lightning detection and data acquisition systems in several respects. First, an improved signal conditioning method is needed. CG events are normally an order of magnitude larger than IC events at LF, due to the channel length and amount of current which flows during a CG return stroke. As mentioned previously, increasing the gain, or equivalently reducing the event threshold, results in CG events saturating an analog detection and evaluation system or the pick-up of significant amounts of noise. Reducing the gain, or equivalently increasing the event threshold, results in inefficient detection that masks IC events. There is a need to reduce the effect of this magnitude difference between CG and IC signals while removing unwanted noise components. An interesting aspect of both electric field and magnetic field antennae is that they produce a signal which is proportional to the time derivative of the electromagnetic field they are detecting. These differentiating antennae actually reduce the magnitude disparity between IC and CG differential representative signals. However, current generation sensors invariably impose integration methods to convert the differentiated field signal to one representative of the electromagnetic field without making use of the fact that the antenna itself reduces dynamic range requirements. Additionally, there is a need for an improved classification method for distinguishing between lightning types.

Another need in the industry is the ability to program remote sensors with new or different waveform analysis techniques. There is also a need for improved data compression and data decimation techniques to accommodate more IC as well as CG information. Additionally, new data correlation techniques are needed to handle increased information processing rates. These correlation techniques need to handle both time-of-arrival and direction information.

Thus, a need exists for a complete lightning detection and data acquisition system that combines new methods of signal conditioning, a user changeable system for event classification, new methods of data compression, and new data correlation techniques to efficiently detect CG and IC events and determine their location, magnitude, and time of occurrence.

SUMMARY OF THE INVENTION

The present invention meets the aforementioned needs by utilizing a plurality of remote programmable sensors (RPS) disposed in different geographic locations to detect, classify, package, and transmit in compressed form information regarding both CG and IC lightning discharges. The information is collected at a central analyzer location where it is decompressed and correlated in order to determine the location, magnitude, and time of occurrence of the lightning discharges. An antenna designed to detect the electromagnetic field signal from a lightning discharge and produce a derivative representative field signal is used. The derivative signal has the benefit of reducing the amplitude disparity between CG and IC field signals. A filter is used to increase the signal to noise ratio by passing the low frequency portions of the differentiated signal while discarding high frequency noise without integrating the principal components of the signal. Non-linear amplification further reduces the amplitude disparity between CG and IC signals by providing greater amplification for lower amplitude signals. The amplified signals are then processed by an ADC to convert an amplified differential signal into a digital representation. This conversion allows a signal to be processed and stored digitally. The digital representation is then integrated by a digital processor to provide a signal representative of the electric or magnetic field. The digital differentiated field signal and the digital signal representative of the field itself are used by the digital processor to classify the lightning event as either a CG or IC event. The analog-to-digital conversion coupled with digital storage permits continuous detection and evaluation of lightning discharges, which eliminates the "dead time" inherent in previous generation lightning detection systems.

The present invention uses a novel data compression process to transmit data over low bandwidth communication channels. Numerous digital signal pulses representative of lightning discharges are grouped together in pulse trains. The largest pulse is designated as the reference pulse and its amplitude, time, and direction (if available) are included in a data record. Other pulses in the pulse train are represented by a fractional amplitude of the reference pulse and a time-stamp relative to the time of the preceding or following pulse. This greatly reduces the information that must be transmitted to define all the pulses in the pulse train accurately. If the amount of transmitted information still exceeds the bandwidth of an associated communications channel, then the RPS sensors in the lightning detection system can be programmed to transmit synchronized portions of the information, so that all sensors will report information about the same lightning events.

Once received by the central analyzer, the information is unpacked and the original pulse amplitude, time, and direction (if available) information is reconstructed. The unpacked pulse information is used to correlate lightning strike information from a plurality of sensor locations. This information is used to determine the magnitude, location, and time of occurrence of the lightning discharge.

Accordingly, it is a principal object of the present invention to provide a novel and improved lightning detection and data acquisition system and method.

It is another object of the present invention to provide a lightning detection and data acquisition system and method with improved capability of distinguishing CG and IC lightning events.

It is a further object of the present invention to provide a lightning detection and data acquisition system that reduces the amplitude disparity between CG and IC lightning representative field signals.

It is an additional object of the present invention to provide a lightning detection and data acquisition system and method that provides continuous detection and processing of electromagnetic field signals caused by lightning discharges.

It is yet another object of the present invention to provide a lightning detection and data acquisition system and method for compression, decimation, and transmission of digital representations of lightning electromagnetic field signals.

It is yet a further object of the present invention to provide a lightning detection and data acquisition system and method for improved correlation of information from a plurality of remote programmable sensors to determine the location, magnitude, and time of occurrence of lightning strikes.

It is a further object of the invention to provide a lightning detection and data acquisition system in which the configuration of the sensors may be set or altered by remote access.

The foregoing and other objects, features, and advantages of the invention will be more readily understood upon consideration of the following detailed description of the invention, taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of the physical arrangement of an exemplary system for acquiring data about cloud-to-ground and intra-cloud discharges according to the present invention.

FIG. 2 is an illustration of a typical LF/VLF field waveform and corresponding time derivative waveform produced by a cloud-to-ground discharge.

FIG. 3 is an illustration of a typical LF/VLF field waveform and corresponding time derivative waveform produced by an IC discharge.

FIG. 4 shows empirically derived cumulative distributions of range-normalized signal amplitude distributions of cloud-to-ground and IC discharges.

FIG. 5 is a functional block diagram of a preferred embodiment of a lightning detection and data acquisition system according to the present invention.

FIG. 6 is a structural block diagram of a preferred embodiment of a lightning detection and data acquisition system according to the present invention.

FIG. 7 is a block diagram of an antenna filter network according to the present invention.

FIG. 8 is a graph of two exemplary frequency responses of the antenna filter network of FIG. 7.

FIG. 9 is a graph of the gains of two non-linear amplifiers.

FIGS. 10A-10H is a flow chart of the operation of a preferred embodiment of a detection and data acquisition method for individual lightning discharge pulses according to the present invention.

FIG. 11 shows the time domain response of a digital filter according to the present invention for numerical integration of signals representative of the time derivative of the field.

FIG. 12 shows the frequency domain response of a digital filter according to the present invention for numerical integration of signals representative of the time derivative of the field.

FIG. 13 illustrates the analysis performed on a general field waveform by a system according to the present invention.

FIG. 14 is a flow chart illustrating the process of grouping individual pulses into pulse trains according to the invention.

FIG. 15 is a flow chart illustrating a "pre-trigger kibosh" test

FIG. 16 is a table depicting the time and amplitude of six electromagnetic pulses which, cumulatively, form a pulse train.

FIG. 17 is a table showing decimal and hexadecimal representations of the first pulse of FIG. 16.

FIG. 18 illustrates binary and hexadecimal representations of the largest pulse of FIG. 16 compressed according to the invention.

FIG. 19 illustrates binary and hexadecimal representations of all pulses other than the largest pulse of the pulse train of FIG. 16.

FIGS. 20A-20D are a flowchart illustrating a compression process according to the invention.

FIGS. 21A-21C are a flowchart illustrating a de-compression process according to the invention.

FIG. 22 is a time graph depicting the arrival of electromagnetic pulses at different remote programmable sensors as reconstructed through the de-compression process of FIGS. 21A-21C.

FIG. 23 is a table with the pulses of FIG. 22 sorted by amplitude and sensor location according to the invention.

FIG. 24 is a time-adjusted graph depicting a correlation process according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

Referring to FIG. 1, a preferred embodiment of the lightning detection and data acquisition system 10 is illustrated. Remote Programmable Sensors ("RPS") 12 are distributed tens to hundreds of kilometers apart. The remote sensors are used to detect electromagnetic fields generated by lightning discharges from clouds 22 as either CG lightning 14 or IC lightning 16. Communication link 18 allows the remote sensors 12 to send information to a central analyzer 20, where the locations, magnitudes, and times of occurrence of the lightning discharges are determined.

CG lightning discharges are generally ten times larger in magnitude than IC events in the VLF/LF frequency range. To prevent saturation of the analog components of the remote sensor 12, a means for reducing the amplitude disparity between CG representative signals and IC representative signals is provided. The sensors 12 also possess a means for increasing the signal-to-noise ratio of the representative signals. Additionally, the sensors 12 convert the analog representative signals into digital signals which are then classified as either CG or IC lightning. Once the signals have been classified, the sensor determines whether groups of signals are sufficiently close together in time to be considered a train of pulses. For such pulse trains, the sensor packages the signal information into compressed data words and transmits the data words to the central analyzer 20. Isolated pulses are transmitted alone with a richer set of characterizing features. If the communication link is insufficient to handle all the information, then the remote sensor 12 decimates the information, only sending synchronous portions of the information.

The central analyzer 20 is used to receive the data words sent from the remote sensors 12 and decompress the words to obtain the lightning discharge information. Applying correlation techniques, the information from a plurality of sensors 12 is used to determine the magnitude, location, and time of occurrence of lightning discharges 14, 16.

Lightning flashes occur between opposite polarity charge accumulations. The lightning flash begins with small breakdown events as the air between the charge accumulations is ionized, forming conducting channels. In a CG flash, once a channel has been formed from the cloud to the ground, large amounts of current flow between the cloud and the ground. The discharges carrying these large current flows are called return strokes. A typical CG lightning flash will have four return strokes. These strokes are typically tens of milliseconds apart. A waveform 30 of the field generated by a CG stroke is shown in FIG. 2. This figure illustrates an electromagnetic pulse with a first negative peak 32, a first negative trough 34, a second negative peak 36, a zero crossing point 38, a first positive peak 40, a first positive trough 42, and a second positive peak 44. A second signal 46 demonstrates the electrical signal representative of the field wave after detection by an antenna 12 which is responsive to the time derivative of the field.

The number of discharges in an IC flash is approximately ten times greater than the number of strokes in a CG flash. On the other hand, the median amplitude of electromagnetic fields caused by CG lightning strokes is approximately ten times greater than that caused by IC discharges. The time spacing between pulses in an IC discharges is also much less than for CG lightning. The result is that IC discharges often occur as pulse trains. The largest amplitude electromagnetic field pulse generally occurs in the middle of these pulse trains. A waveform 50 of the field generated by an IC discharge is shown in FIG. 3. This figure illustrates an electromagnetic field pulse train made up of numerous prominent pulses 52 and several small to moderate pulses 54. A second signal waveform 56 demonstrates the electrical signal representative of the time derivative of the field after detection by the (differentiating) antenna 12.

FIG. 4 shows empirically determined cumulative distributions of range-normalized signal amplitudes of IC and CG discharges. Range-normalization results in signal amplitude values that are independent of the actual distance between the sensor and the discharge. A first curve 70 in FIG. 4 is a range-normalized representation of CG events. The domain is signal strength in LLP units range normalized to 100 km from the sensor location. Referring to the first curve 70, 50 percent of CG lightning events have an amplitude larger than 120 LLPUs and 50 percent are below 120 LLPUs. Approximately 80 percent of CG events have amplitudes less than 180 LLPUs and 20 percent are greater than 180 LLPUs. A second curve 72 represents the range-normalized distribution of the large and prominent discharges within IC flashes, while a third curve 74 illustrates the range-normalized distribution of all IC discharges. Approximately 70 percent of all IC events are less than 1 LLP. This amplitude disparity requires a novel approach to lightning detection and processing if both CG and IC events are to be detected by the same sensor in the LF/VLF frequency range.

Turning to FIG. 5, a block diagram illustrates the functional aspects of a lightning detection and data acquisition system according to the present invention. A differentiating antenna 92 is used to detect either an electric field or a magnetic field generated by a lightning discharge. The antenna 92 outputs an analog signal representative of the detected field wave ("electrical detection signal") and sends it to the remote programmable sensor 94. The first stage of the remote sensor 94 is a signal conditioning circuit 96 used to reduce the amplitude disparity between CG representative signals and IC representative signals, reduce noise, and convert the conditioned electrical detection signal to a digital representation. Once the representative signal has been conditioned, it is passed to the event classification stage 98 where the digital representative signal is evaluated to determine what type of event caused the electromagnetic field (either a CG or IC discharge). The remote sensor can be programmed based on user-selectable criteria resulting in only signals of interest being accumulated. The accumulated signals of interest are processed by data compression software 100. If necessary, they are also processed by the data decimation stage 102 and then transmitted to the central analyzer 104 using any digital data transmission means. The central analyzer utilizes data decompression 105 and data correlation 106 followed by location determination 108 to determine the magnitude, location, and time of occurrence of the lightning event.

Referring to FIG. 6, a structural block diagram of a preferred embodiment of a remote programmable sensor 110 is illustrated. The analog front end 112 accepts an analog representative signal from an electromagnetic antenna, filters the representative signal using the filter and amplification component 124 and passes the representative signal to an ADC 114 and an amplitude comparator 128 using a cross-point switch 126. The comparator 128 is used to determine whether the amplitude of the analog representative signal exceeds a previously determined value, indicating the beginning of a "pulse". Upon determination that a pulse has begun, a Global Positioning System ("GPS") device 130 is used to provide a time-stamp which is stored in a time tag first-in-first-out ("FIFO") 132 in a Field Programmable Gate Array ("FPGA") 116. The ADC 114 is used to provide continuous digitization of the signal provided by the cross-point switch 126. The ADC produces a digital signal with 12 bit resolution sampled at 20 MHz. The digitized signal is sent to a digital summer 134 inside the FPGA 116 mentioned above. The summer 134 is used to add groups of four digital samples producing a 5 MHz sample with a 14 bit resolution. These 14 bit digital samples are placed in a signal FIFO 136 in the FPGA 116. A clock signal provided by the FPGA is used to control the flow of digital samples from the signal FIFO 136 to a ring buffer 138 residing in a synchronous dynamic random access memory device ("SD-RAM") 120. Access to the ring buffer 138 occurs by way of a dynamic memory access controller ("DMA") 140, part of a digital signal processor ("DSP") 118. The DMA controller 140 also transfers event time-stamps from the time tag FIFO 132 in the FPGA 116 to a time tag buffer 142 in the SD-RAM 120. A central processor unit ("CPU") 144 inherent in the DSP 118 is used to evaluate data stored in the ring buffer 138 and time-stamp information stored in the time tag buffer 142. Data representing signals of interest are placed in a results buffer 146 in the SD-RAM 120. The information then passes through a DSP-to-PC interface 148 to a host personal computer 122 where it is packaged for transmission.

The signal conditioning and classification aspects of the remote programmable sensor 110 is addressed hereafter, and is illustrated by FIGS. 7, 8, 9, and the flow chart in FIGS. 10A-10H.

Analog Processing

The basic components of the antenna filter network 300 are shown in FIG.7. An antenna 301 provides a differentiated signal to a low pass filter 302 (four-pole or more) and high-pass filter 304. The filters are followed by an optional non-linear amplification stage 306. Both electric field and magnetic field antennae produce signals that depend on the time derivatives of the field signals they monitor. Thus, this antenna 301 is referred to as a differentiating antenna. This differentiating property of the antenna 301 is important, as it has been discovered that differentiating electromagnetic signals reduces the amplitude disparity between CG and IC representative signals by a factor of two to four. Previous generation lightning detection systems have not made use of this antenna property. In fact, most lightning detection systems integrate the antenna output to get a true representation of the electromagnetic field.

The four-pole low pass filter 302 is used to provide a good transient response to the differentiated signal. The output of the low pass filter 302 is sent to a high pass filter 304 with a cut-off frequency around 300 hertz. The purpose of this filter is to remove any signals that are probably man-made such as 50/60 hertz power-line noise. In the frequency range between these two filters, no filtering is applied. This preserves the differentiated nature of the signal throughout the band where the signals of interest exist. This tuning of the filters effectively creates an analog leaky integrator that is tunable to allow frequencies below 0.5-1 MH to pass through without being integrated. An optional component of the invention is the use of non-linear amplifiers 306 to further reduce the amplitude disparity between CG and IC representative signals. A first curve 310 of FIG. 8 shows the frequency response of the leaky filter network, including the differentiating antenna, in the preferred embodiment. A second curve 312 illustrates the frequency response of the leaky filter network wherein the time constant has been adjusted to remove excessive man-made radio frequency noise.

The non-linear amplifier 306 of the preferred embodiment amplifies low amplitude signals disproportionately more than high amplitude signals. Two types of non-linear amplifiers that are well suited to this application are the logarithmic amplifier and the piece-wise linear amplifier. Both of these types of amplifiers can reduce the amplitude disparity between CG and IC signals by 12-24 dB. FIG. 9 illustrates the input-output characteristics of these non-linear amplifiers. A first curve 330 shows the response of a logarithmic amplifier while a second curve 332 illustrates the response of a piece-wise linear amplifier.

Referring to FIG. 10A, a differentiated representative analog signal is received from the antenna 92 of FIG. 5. Steps 150 and 151 are low-pass filtering necessary to remove high frequency noise from the representative signal, followed by high-pass filtering used to remove power line noise. Optional non-linear amplification 152 is used to amplify low-amplitude signals disproportionately more than high amplitude signals, reducing the amplitude disparity between CG and IC signals.

Data gathering

Following non-linear amplification 152 in FIG. 10A, using the cross-point switch 154, analog information is sent to threshold comparison 156 and digitization 162. If a representative signal exceeds a pre-determined amplitude value, a "threshold crossing time" is established in step 158 using the GPS device 130 of FIG. 6 and placed into the time tag FIFO 132 per step 160. Simultaneous with threshold comparison 156, 12 bit resolution continuous digitization 162 at a rate of 20 MHz occurs at the ADC 114 of FIG. 6. In LF/VLF sensors, every four samples are added together in step 166 using the digital summer 134 of the FPGA 116. The result, a 5 MHz data sample stream with 14 bits of resolution, is stored per step 168 in the signal FIFO 136 of the FPGA 116. The process of moving samples through the FIFO 136 until they reach the end is shown by step 170. Control logic provided by the FPGA 116 and the DMA controller 140 are used to control the flow of digital samples from the signal FIFO 136 to the ring buffer of the SD-RAM 120; this is shown by step 172 of FIG. 10B. Implicit in the address of the data sample within the ring buffer is the time stamp to the fraction of a second. A separate clock signal in the DSP 118 is used to regulate reading data samples from the ring buffer 138 into the CPU 144 of the DSP per steps 174. In practice, the transfer of data into the ring buffer at step 172 is always a few samples ahead (in time) of the transfer of data out of the ring buffer and into the DSP CPU at step 174. This time lag is represented graphically by the "future buffer" 173 in FIG. 10B to symbolize the fact that this lag allows the DSP to examine a few samples on either side of the sample currently being examined at step 176. Step 178 provides the next time-stamp from the time tag buffer 142 to the CPU 144. Step 176 is the determination of whether the current data sample occurred at or after the threshold crossing time indicated by the time-stamp. If so, step 180 is used to determine whether the amplitude of the digitized signal has been below a threshold established for the end of an event. If so, the event which produced the pulse beginning with the time-stamp retrieved by step 178 is deemed to have ended, i.e., the pulse is over. Step 182 is used to advance the time tag buffer to the next event of interest and proceed to pulse classification, step 184. If the result of step 180 is that the pulse is not deemed complete and we are just beginning a pulse, then steps 186 and 188 are used to find the time of the first data sample within the present pulse that had an absolute magnitude above the noise level. This time is not the same as the time-stamp retrieved in step 178, as the time-stamp was recorded only after the current pulse exceeded a pre-determined threshold magnitude (set higher than the noise floor).

Steps 189 and 190 are used to generate a representative field signal by numerically adding the current data value to a weighted sum of all previous data values of the current pulse, where the weighting is a function of the age of the samples. This process constitutes a digital integrator to reconstruct the field signal over the band of interest using the differentiated field signal that has been passed through to this point. FIG. 11 shows the time domain response 314 of the digital integrator according to the present invention. FIG. 12 shows the frequency domain response 316 of the digital integrator according to the present invention. At the completion of step 192 of FIG. 10C, the data sample now has three components: a time-stamp, the value of the derivative field signal, and the value of the representative field signal itself. A signal representative of an entire field pulse 293 after the digital integration is illustrated in FIG. 13.

Peak and Trough and Zero-Crossing Determination

Steps 194 and 196 are used to look for peaks and troughs in the representative field signal by searching the derivative signal for zero crossing points. In practice, the differentiated signal will contain some noise and will need to be smoothed 193 (digitally filtered) in order to retain prominent peaks and troughs without flagging all small high-frequency sign changes in the time derivative as significant peaks and troughs in the field signal. In a preferred embodiment, this smoothing will involve several samples on either side of the current sample. A first peak 294, a first trough 295, a second peak 296, and a zero crossing point 297 of the representative field signal 293 are shown in FIG. 13. The number of peaks in the current pulse is recorded in step 200. In accordance with step 202, if the field representative signal has changed polarity, then a zero-crossing time is recorded in step 204 and a counter is incremented in step 206 of FIG. 10D. Once the data sample has been tested for peaks, troughs, and zero-crossing times, the next data sample is read into the DSP's CPU 144 in step 208 of FIG. 10D and the process returns to step 174 of FIG. 10B.

The process steps illustrated by FIGS. 10A, 10B, 10C, and 10D are used to detect signals of interest and gather information. Specifically, at the conclusion of step 208, the following information has been determined: (1) the number of peaks in the current pulse of the field representative signal; (2) the number of troughs in the current pulse; (3) the time and amplitude of each peak and trough of the current pulse; (4) the number and times of zero crossings in the current pulse.

Pulse Classification

Once the pulse is determined to have terminated at step 180 of FIG. 10B, then the process proceeds from step 184 to step 242 of FIG. 10E where pulse classification begins. Pulse classification begins by establishing a default value for the pulse as a IC event in step 244. The overall pulse duration is evaluated in step 246. If the pulse is too short in duration or the largest amplitude of any peak within the pulse is too small, the event is presumed to be noise and is discarded in step 248, the counts of zero crossings, peaks, and troughs are zeroed at step 250, and the process returns to step 174 of FIG. 10B. If the pulse has sufficient duration, then it is evaluated for an excessive number of zero crossings in step 252. Too many zero crossings are another indication of noise, in which case the pulse is discarded in step 248 and the process returns to step 174 of FIG. 10B. A novel pulse classification process is illustrated by step 256, where the pulse is evaluated for a short time from the largest peak of the first polarity to the largest peak of opposite polarity. The result of this test must be temporarily saved (steps 257 and 258) because after this test, all pulses proceed to the bipolar amplitude ratio test at step 260 of FIG. 10F. This pulse classification parameter is given by the time between the first peak 294 and the second peak 296. Pulses with a short time differential between these peaks are classified as CG pulses or leader pulses.

Bi-Polar Amplitude Ratio Test

Step 260 is used to determine if the largest of any subsequent opposite polarity peaks (299 of FIG. 13) is greater than a pre-determined fraction of the largest peak of the initial polarity 294. In the event that the opposite polarity peak time difference (step 256 of FIG. 10E) was short, the bipolar amplitude ratio distinguishes between bipolar IC discharge pulses and leader pulses, which tend to be nearly unipolar. Thus, after step 260, the result of the opposite-polarity peak time test must be examined (steps 261, 263). If the bipolar amplitude ratio 260 was large and the opposite polarity peak time result was true (step 263), the pulse is classified as a bipolar IC pulse (step 265). If the bipolar amplitude ratio is small and the opposite polarity peak time result was true (step 261), the pulse is classified as due to a leader (step 264). In the preferred embodiment, leader pulses are considered a special case of IC discharge pulses. Beyond steps 264 and 265, no further testing is