Zero false alarm seismic detection and identification systems

Pakhomov, Alex; Goldburt, Tim

doi:10.1117/12.793347

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…The periodicity within the autocorrelation is of greatest interest so the scale of the autocorrelation is not relevant. As such, the autocorrelation can maintain the necessary information while being simplified to (2) where is the previously determined energy signal and is the mean value of the energy over the window.…”

Section: A Autocorrelation Cadence Determinationmentioning

confidence: 99%

“…Through various methods, the rate and consistency in which the target creates impulses can be monitored. A human has a cadence that is largely separable from animals due to the distinction that humans are bipedal and most potential animal interferers are quadrupedal [1][2]. However, there is generally some crossover between animal and human cadences.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of human footsteps utilizing multi-axial seismic fusion

Schumer

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper introduces a method of enhancing an unattended ground sensor (UGS) system's classification capability of humans via seismic signatures while subsequently discriminating these events from a range of other sources of seismic activity. Previous studies have been performed to consistently discriminate between human and animal signatures using cadence analysis. The studies performed herein will expand upon this methodology by improving both the success rate of such methods as well as the effective range of classification. This is accomplished by fusing multiple seismic axes in real-time to separate impulsive events from environmental noise. Additionally, features can be extracted from the fused axes to gather more advanced information about the source of a seismic event. Compared to more basic cadence determination algorithms, the proposed method substantially improves the detection range and correct classification of humans and significantly decreases false classifications due to animals and ambient conditions.

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Section: A Autocorrelation Cadence Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of human footsteps utilizing multi-axial seismic fusion

Schumer

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Extending systems lifetime without battery replacement is one of the key targets of any WSN. Several researches in WSN focusing on power efficiency were reported in [12][13][14][15][16]. Sensor board with wireless unit was described in [16] but the design lacks details on the amount of amplification, filtering, and noise reduction in the electronics.…”

Section: Introductionmentioning

confidence: 99%

Hardware Design of Seismic Sensors in Wireless Sensor Network

Koç¹,

Yeğin

2013

International Journal of Distributed Sensor Networks

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Seismic sensors are invaluable for intruder detection and perimeter security. In a typical wireless sensor network application of seismic sensors, the units are battery powered and low power consumption becomes critical while fulfilling system requirements. Although many systems utilize 24-bit ADC for seismic signal processing, we employed 12-bit ADC for low power consumption. Because of this relatively low resolution ADC, preamplifier and filters require careful hardware design. We employ bidirectional T-type filtering, noise reduction, and distributed filtering between gain stages before the signal is input to ADC. The proposed design was verified with measurements. Seismic data signals due to footsteps at varying distances were successfully measured.

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“…By measuring seismic data, acoustically audible signals and ultrasonic data, it is possible to discriminate human signatures, e.g., against dog signatures. Pakhomov and Goldburt () and Pakhomov et al () studied the different signatures of seismic signals for different walking gaits and different kinds of vehicles. Their detection and identification algorithm has a very low false alarm rate.…”

Section: Introductionmentioning

confidence: 99%

Towards an automatic seismic localization of human footsteps

Heider

Jetschny

Bohlen

2013

Near Surface Geophysics

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Today, many companies or government institutions are required to monitor outdoor areas of office buildings, frontiers or restricted areas. In this paper we present an automated algorithm to monitor and to localize human footsteps with the help of their excited seismic waves. The preliminary goal of the data processing is the calculation of traveltime differences for each receiver pair of an arbitrary array of geophones. The data are filtered with a band-pass filter, which improves the signalto-noise ratio significantly. A short term average / long term average (sta / lta) picker determines the time of the first breaks. After cutting out time windows around the picked arrival times, the traveltime differences are than calculated for each receiver pair using cross-correlation. Finally, a grid search algorithm is used to invert the traveltime differences into locations of the footstep. Therefore, the propagation velocity of the Rayleigh wave has to be approximated. In one field measurement reliable and precise localizations were achieved. Footsteps on a fixed position were localized within an area of 30 m x 30 m with less than one metre deviation and paths on a straight line were reconstructed successfully.

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Zero false alarm seismic detection and identification systems

Cited by 7 publications

References 22 publications

Analysis of human footsteps utilizing multi-axial seismic fusion

Analysis of human footsteps utilizing multi-axial seismic fusion

Hardware Design of Seismic Sensors in Wireless Sensor Network

Towards an automatic seismic localization of human footsteps

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