Using evolutionary computation for seismic signal detection: a homeland security application
V William Porto, Lawrence J. Fogel, David B. Fogel · 2004
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. Time-frequency 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.