Seismic footstep signal characterization

Abstract

Seismic footstep detection based systems for homeland security applications are an important additional layer to perimeter protection and other security systems. This article reports seismic footstep signal characterization for different signal to noise ratios. Various footstep signal spectra are analyzed for different distances between a walking person and a seismic sensor. We also investigated kurtosis of the real footstep signals under various environmental and modeled noises. We also report on the results of seismic signal summation from separate geophones. A seismic signal sum spectrum obtained was broader than that obtained from a single sensor. The peak of the seismic signal sum was broader than that from the footstep signal of the single sensor. The signal and noise spectra have a greater overlap for a seismic signal sum than that from a single sensor. Generally, it is more difficult to filter out the noise from the sum of the seismic signals. We show that the use of the traditional approach of spectrum technology and/or the statistical characteristics of signal to noise of reliable footstep detection systems is not practical.

A Monte-Carlo Approach for Tracking Mobile Personnel

“…Standard beamforming approaches [2] rely on the assumption that the received wavefront is planar. These methods work well when the target is at a large range from the sensor but perform poorly when the range is small and the wavefront is circular, which is the case generally encountered considering standard detection ranges for human footfall signals [13].…”

Footstep Detection in Noisy Environment via Non-Linear Spectral Subtraction and Cross-Correlation

“…신호의 포락선(Envelope)을 이용하여 발자국의 운 율을 계산하고 그 통계적 특성을 이용하여 사람을 탐 지하는 것이다 [7][8][9][10][11][12][13][14] . 이 방법은 한 사람이 등속으로 이…”

An Efficient Intruder Detection using the Seismic Sensor

“…진동 감시정찰 시스템은 차 량, 발전기, 바람과 같은 진동 간섭(interference)에 의 해 그 성능이 심각하게 감쇠되는데 이러한 진동 간섭 을 상쇠시키고자 하는 연구 [5] 도 있다. [6] . 사람발자국 이외에 기차, 차량, 헬기, 배 진동신호 도 각자 고유 특징을 가지고 있으며, 이들 정보를 이 용하여 구분할 수 있다 [7] .…”