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Jul 22, 2004 - Keywords – Seismic signal processing, perimeter security, evolutionary .... Marine squadron of 17 or 21, with various formations and marching ...

CIHSPS2004 - IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety Venice, Italy, 21-22 July 2004

Using Evolutionary Computation for Seismic Signal Detection: A Homeland Security Application Vincent W. Porto, Lawrence J. Fogel, and David B. Fogel Natural Selection, Inc. 3333 N. Torrey Pines Ct., Suite 200 La Jolla, CA 92037 USA [email protected] [email protected] [email protected] by U.S. Marines from Camp Pendleton, California. For this effort, the algorithms were developed from the response signals of the USMC Tactical Remote Sensor System (TRSS) Seismic Intrusion Detector (SID) sensors. This paper describes the methods and results from selected experiments conducted within this rubric.

Abstract – Many organizations and governments have the need to monitor areas for intrusions and, once detected, to identify the type of potential intruder(s) present. Applications include perimeter security at installations such as airports and critical infrastructure, as well as military situation awareness in monitoring demilitarized zones, or other areas where activity of interest may occur. Seismic signal detectors can be used in many of these applications. Timefrequency response (TFR) signals are generated and must be classified as being generated by particular targets of interest. Experiments were conducted using real data collected at Marine Corps Base, Camp Pendleton, California, USA. Seismic signal detectors were used to monitor signals generated by individual people, groups of people, and vehicles of different types. Evolutionary computation was combined with neural networks to analyze the TFR signals and classify the acquired data. The results indicated the practical application of classifying signals based on their seismic signature.

II. BACKGROUND A. Military Remote Sensing Operations Remote sensor operations expand the commander’s view of the battlefield. Remote sensors provide a means to economically conduct continuous surveillance of vast areas and contribute key information to the intelligence collection effort. A remote sensor system, consisting of individual sensors, communications, relays, and monitoring devices, provide the capability to conduct remote sensing operations. The sensors, relays, and monitoring devices are employed in an integrated network, providing general surveillance, early warning, or in some cases limited target acquisition over selected areas of the battlefield. Key considerations in employing remote sensors are the nature of the target(s), the characteristics of the area of operations, the time and resources available for emplacing the sensor network, and the location and connectivity of the sensor monitoring sites. Tactical Reconnaissance Sensor System – Phase V (TRSSV) sensors (Figure 1) were used in this effort. These are militarized versions of advanced commercial security intrusion detection devices. Each TRSS-V sensor is encased in an Encoder Transmitter Unit (ETU) and forms part of a surveillance system for monitoring activity in selected areas of the battlefield. TRSS sensor suites include seismic, magnetic, and infrared (IR). These sensors operate in a constant false alarm rate (CFAR) mode in which an alarm is sounded whenever a predetermined threshold signal level is exceeded. Thus they do not classify or identify targets; they only detect the presence of “something,” i.e., people or vehicles. The seismic sensor (ETU-SIDs) remains quiet until a stimulus or stimuli excites it above its predetermined threshold, at which point the sensor sends out a radiotransmitted alarm to a monitoring center.

Keywords – Seismic signal processing, perimeter security, evolutionary computation, neural networks

I.

INTRODUCTION

The objective of this research was to evaluate the application of evolutionary computation (EC) techniques to the development of complex, robust mathematical algorithms enabling the detection and classification of a range of military targets using an array of seismic sensors. Previous attempts by the U.S. Army at developing and demonstrating classification algorithms for seismic sensors had not produced reasonable results.1 The ultimate goal of this research is to develop algorithms to support Army instantiations of Microelectromechanical Systems (MEMS) sensors. In order to drive the developmental algorithms, data from military sensors were needed. Selected test stimuli were tactically important, e.g., Marines (an individual, a four-man fire team, and a Marine squad) and tactical and commercial vehicles (HMMWV, light and heavy sport utility vehicles, and a military five-ton truck). Some of the vehicle tests included military tactical trailers. The project was supported 1

This observation is based on comments made by U.S. Army, CECOM, Sensors Branch Manager, on October 31, 1998. A well-funded program did not produce useful results in over three years of research and development.

0-7803-8381-8/04/$20.00 (c)2004 IEEE

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artifacts, sensor internal noise, interference from nearby automobile traffic, aircraft, and rotorcraft, and other unwanted artifacts. Special signal processing filtering algorithms were designed to reduce this background noise to tolerable levels.

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B. Application of Computational Intelligence

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The time-frequency response (TFR) generated by an array of such sensors provides the potential sensory information to determine the source of received signals. Computational intelligence techniques can be used to optimize classifiers of such sensed signals. In particular, attention can be given to evolutionary algorithms applied to alternative data structures that are used to map sensed inputs to desired outputs. Such structures include crisp and fuzzy rules, decision trees, finite state machines, neural networks, and other mathematical constructs. Regardless of the choice of classifier, unless it is a linear discriminant function or simple variant thereof, finding the optimal form of the classifier is a nonlinear optimization problem. Evolutionary computation has proved useful in many such problems [1-4], particularly in optimizing the complex tradeoffs associated with neural networks in processing real-time continuous data and generating categorical outputs [5].

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(b) Figure 2. Graphical layout of out of the test sites used. (a) The 16 seismic sensors are placed mainly in a circular pattern with additional sensors placed inside the circle. Each sensor on the outer ring is 135 feet from the center of the circle. The inner sensors are 50 feet from the center of the circle. The sensor field is partitioned by different types of terrain. To the left, tree, bushes, and heavy growth are prevalent. To the right, grass covers soft soil. These varying terrain compositions pose a challenge to classifying sensed signals. The straight line paths indicate the motion of various targets to be detected and classified. (b) A similar array set up on different terrain with trees and large bushes around the perimeter of the ring.

III. METHODS Field tests were conducted over a period of a year to collect data that were representative of typical signals from a suite of stimuli. Each of these test was conducted at the Marine Corps Base at Camp Pendleton, California, USA. Test sites were selected to cover a variety of terrain and geological environments. Figure 2(a-b) depicts typical array configurations on different terrain along with several stimuli paths. Figure 3 shows the time-series amplitude responses for different stimuli. Table 1 lists the varied stimuli that were used during these site tests. The majority of the data collected contained background noise from a large variety of sources. These ranged from micro-seismic events, power-supply

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then partitioned into mutually exclusive testing and training sets. Neural networks (Figure 4) were chosen for the classification task and designed to have one output class for each potential truth class, as well as an extra output node for a noise class (indicating the absence of a signal). Truth output vectors associated with each data exemplar were binary, i.e., a desired output of unity for the truth class, and zero for all other classes. Figure 5(a-b) shows TFRs for a HUMMV before and after processing, respectively. In these figures the vertical axis is time and the horizontal axis represents frequency. Spectral power is color-coded. Figure 6(a-b) shows the sensed response from a six-by-six truck at two different sensors in an array. Neural networks were chosen for classification due to their proven ability to create robust nonlinear mappings of inputoutput response data. Evolutionary computation (EC) was used to design and train these (supervised) neural network classifiers, because EC overcomes problems encountered with more traditional optimization methods (e.g., gradientbased techniques) that often provide suboptimal solutions because of the presence of local optima. Moreover, EC can be used to address problems for which traditional methods require that (1) the problem be reduced to a linearized domain prior to solution, (2) the interdependence of system components are neglected, and (3) require excessive computational time. The neural network topology, nodal functions, and interconnection weights were all optimized simultaneously by the EC algorithm during the training process. After training, the topology, functions, and weights were fixed (i.e., for these experimental tests, no further on-line learning was desired or permitted). Subsequently, each set of temporally sequenced test data exemplars was then presented to the network for classification. Outputs from the final set of neural output nodes were passed through a threshold prior to reporting results. Each experimental data run generated a time-series of exemplars. Thus, a method of accumulating all of these temporal results into a consistent classification decision was devised. For each experiment, a histogram was generated from the network outputs, with one bin per each output class. The histograms accumulated neural decisions for each of the potential output classes over the temporal set of data for each experiment. Ideally, the histogram should show one bin containing all neural outputs for the truth class, and nothing in the complimentary set of bins at each time slice. Figure 7 shows activation histograms for a heavy SUV signal. Classifier activations for each output category are coded by color (yellow for SUV activations), with time on the horizontal axis. Vertical amplitudes indicate classifier confidence.

Figure 3. Time series for various stimuli as sensed by two sensors.

Table 1. Types of signals used in the experiment

Single Marine, varied marching rates Four-Marine fire team, diamond and column formulations, varied marching rates Marine squadron of 17 or 21, with various formations and marching rates M-1038 HMMWV, four-wheel drive vehicle (5,500-lb.) M-923 six-ton truck, six-wheel drive (in excess of 15,000-lb.) Small civilian sport utility vehicle (3,675-lb. SUV) – Chevy Blazer Large civilian sport utility vehicle (5,250-lb. SUV) – Ford Expedition Armored amphibious vehicle (AAV) Cargo vehicle-supply (CVS) Light armored vehicle (LAV) In supervised learning paradigms, classification algorithms are first trained with input-output stimulus-response pairs. Once sufficient training has been performed, the algorithm is then ready for use with novel data. In the research reported here, the input features that were selected for training consisted of multiple sets of temporally sequenced, spectrally filtered, time-frequency-amplitude data. This strategy was devised in order to facilitate transitions to a real-time data processing system. Several minutes of data were processed to create multi-dimensional time-frequency response (TFR) ‘grams’. A sliding window approach was implemented wherein as new data were incorporated into the gram, the oldest data were dropped. Data from each of the (validated) experiments were processed in this manner, creating multiple time-overlapped exemplars for each test run. These data were

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Nonlinear Processing Nodes

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Figure 4. The general form of neural network used for the classification experiments. The number of output nodes was set equal to the number of potential target classes plus an addition “noise” class.

(a)

(a) (b) Figure 6. The time frequency response sensed from two sensors (a, b) as a six-by-six truck passes by.

(b) Figure 5. (a) The unprocessed time-frequency response observed by a single sensor for a HUMMV. (b) The time-frequency response for the HUMMV after preprocessing. Figure 7. A sample histogram of time outputs from the neural network for a HUMMV. The yellow bars indicate the neural network classifying the HUMMV correctly.

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locations and with potentially unreliable sensor performance or network communications between sensors. Once a target is acquired, evolutionary computation techniques can also be applied to optimize object tracking and cross-sensor correlation as potential threats pass the signal array. Further research is necessary to develop these techniques and sensor devices into miniaturized, field-ready prototypes. The overall results from this project, however, demonstrate the promise of these techniques and importantly, provide a significant increase in situation awareness over CFAR-only capabilities of the existing ETU-SID devices.

Due to noise, multi-path, overlapping exemplar classes, etc., all real-world classifier implementations typically generate results with some degree of ambiguity (with other output class bins being non-null). Using this histogram approach as a first stage in determining the neural output class, the distributions were analyzed for consistency. In the majority of experiments, the histogram distributions presented fairly distinct evidence favoring one output class over the other possible classes. In these cases, this output class was used as the final decision for the assessed class of the experimental run. Data collected from the site test were partitioned into different test/training sets to facilitate double-blind testing. Portions of the data were first used to train the algorithms. Further testing also presented the algorithms with many (blind) stimuli that had not previously been encountered.

REFERENCES [1] [2]

IV. RESULTS

[3]

The results of two field trials are indicated in Table 2(a-b). The data presented are from trials conducted in February and March, 1999. For this classification problem, there were five categories to identify: a single Marine, a fire team (multiple Marines), a HUMMV, a five-ton truck, and background noise. Thresholding was used for each output node corresponding to a category such that it was possible for no classification call to be made in some cases (even of noise). After collection, the available data were partitioned into training and test sets. The experimental design required one experiment of training on one set and testing on the other, followed by training on the previous test set and then testing on the previous training set. Table 2(a-b) indicates the generally successful performance of the evolved neural networks. Most categories of targets were classified correctly and noise was always classified correctly. The overall correct classification performance was 76 percent. Note that misclassifications of personnel were limited typically to judging between a single person or a fire team in case (a). In case (b), a significant percentage of fire team cases were misclassified as HUMMV.

[4] [5]

D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, Piscataway, NJ, 1995. T. Baeck, D.B. Fogel, and Z. Michalewicz (eds.) Handbook of Evolutionary Computation, Oxford Press, NY, 1997. Z. Michalewicz and D.B. Fogel, How to Solve It: Modern Heuristics, Springer, Berlin, 2000. G.B. Fogel and D.W. Corne (eds.) Evolutionary Computation and Bioinformatics, Morgan Kaufman Pubs., San Francisco, CA, 2002. X. Yao, “Evolving Artificial Neural Networks,” Proc. IEEE, Vol. 87, pp. 1423-1447, 1999.

Table 2. The classification results of the best-evolved neural networks by category. Top table (a) presents the results of testing on a first training set. The bottom table (b) presents the results of testing on the previous training set after training on the previous test set

(a) Truth Class Classifier Output Noise Single Marine Fire Team HUMMV 5-Ton Truck (b)

V. CONCLUSIONS

Truth Class

The results of these studies indicate that the combination of effective feature extraction and evolutionary techniques to optimize classifiers can augment the detection and signal classification abilities of existing seismic sensors. Neural networks provided a convenient data structure for the classifiers and their parameters were optimized effectively by the evolutionary algorithm. The real-world conditions faced posed significant challenges, particularly in terms of processing signals in light of low signal-to-noise ratios and fusing information across multiple sensors. Nevertheless, the results demonstrated the utility of the approach. Future efforts that extend these experiments should be aimed at developing classifiers based on sensors at unknown

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