&lt;title&gt;Vehicle weight estimates using a buried three-axis seismometer&lt;/title&gt;

Gramann, Richard A.

doi:10.1117/12.336955

Cited by 2 publications

(3 citation statements)

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“…The current traction mechanism algorithm averages the SNRs across two low frequency bands, 0-20 Hz and 20-40 Hz. Analysis has shown that the peak energy resides in the lower band (0-20 Hz) for wheeled vehicles and resides in the 20-40 Hz band for tracked vehicles [3]. Therefore, the position of the peak energy is determined on a scan-by-scan basis while an event is in progress.…”

Section: Traction Mechanismmentioning

confidence: 99%

“…Signatures for tracked vehicles differ from those for wheeled vehicles in that several peaks are present in these low frequencies due to track slap, often referred to as track lines. The presence of the track lines results in a broader low frequency distribution [3]. The current traction mechanism algorithm averages the SNRs across two low frequency bands, 0-20 Hz and 20-40 Hz.…”

Section: Traction Mechanismmentioning

confidence: 99%

“…It has been shown that these SNR levels are a function of vehicle velocity and type (tracked vs. wheeled) [3]. However, because there is not a direct measure of the vehicle velocity, an estimate is computed based on the rate of change of the broadband SNR.…”

Section: Relative Weightmentioning

confidence: 99%

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Detection and classification of time-critical targets using seismic sensors

Mayoral

McCool

Gramann

2002

Unattended Ground Sensor Technologies and Applications IV

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In the late 1990s, ARL:UT developed a real-time system capable of detecting and classifying a variety of vehicles using a single 3-axis seismic sensor. At that time, the detection and classification software was designed to classify vehicles for a variety of features, including the number of cylinders in the engine, number of axles, engine type, traction mechanism, and relative weight. Using a single 3-axis seismic sensor, buried approximately 3 feet deep, the demonstration system showed that it was possible to detect and classify a single time-critical target, moving with constant bearing and speed. Since then, modifications have been made to the detection and classification software in support of a transition of the algorithms into unattended MASINT sensors (UMS). This paper describes the next evolution of the detection and classification system, which focuses on generating highly accurate traction mechanism and relative weight classifications. The current build of the software has proven capable of detecting vehicles and generating high confidence classifications for a wide range of vehicles and test scenarios, and performs well for both buried and hand-emplaced sensors. Included in the paper is a description of the upgrades made to the classification algorithms, event switching, and graphical user interface (GUI), an outline of the processing and analysis of seismic data, and a summary of the software performance for a wider range of vehicles, test scenarios, and sensor configurations.

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Section: Traction Mechanismmentioning

confidence: 99%

Section: Traction Mechanismmentioning

confidence: 99%