Abstract
An algorithm is developed for footstep, vehicle, and rain detection using seismic sensors operating in a wireless sensor network. Each standalone seismic sensor is coupled with a wireless node, and alarm conditions were evaluated at the sensor rather than at the gateway. The algorithm utilizes slow and quick adaptive thresholds to eliminate static and dynamic noise to check for any disturbance. Duration calculation and filters were used to identify the correct alarm condition. The algorithm was performed on preliminary field tests, and detection performance was verified. Footstep alarm condition up to 8 meters and vehicle presence alarm condition up to 50 meters were observed. Presence of rain did not create any alarm condition. Detection based on kurtosis was also performed and shortcomings of kurtosis especially for vehicle detection were discussed, proposed algorithm has minimal load on the sensor board and its data processing unit; thus, it is energy efficient and suitable for wireless sensor alarm networks.
Highlights
Unauthorized human detection is an important and, mostly, an integral part of any security system
Algorithm constants was mostly arrived at trial error at the beginning, but, after extensive trial tests, probability of detection and false alarm rates were used to finalize their values for optimum performance
Real-time detection using seismic sensor data was developed to identify the presence of footsteps, vehicle, and rain
Summary
Unauthorized human detection is an important and, mostly, an integral part of any security system. In building or immediate vicinity of the structure can be monitored with cameras or security personnel, but perimeter of the building, especially wide open area security systems, requires sensors for intruder detection. FFT may provide promising traffic detection, an alternate analog signal processing may be necessary due to demanding power requirements of FFT evaluation using digital circuits. Another widely accepted detection method is “kurtosis”, which measures extreme deviations from mean signal [6]. Certain types of noise can generate deviations similar to human steps Another popular intruder detection method is based on “Copula” theory [7, 8].
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