&lt;title&gt;Netted sensors-based vehicle acoustic classification at Tier 1 nodes&lt;/title&gt;

Jacyna, Garry M.; Christou, C.T.; George, Bryan; Necioglu, B.F.

doi:10.1117/12.607142

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Acoustic Signature Of Vehicles1

Introduction1

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2023

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Cited by 4 publications

(2 citation statements)

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“…[23][24][25][26][27][28][29][30] It was suggested that the dominant components of acoustic signals of a vehicle moving faster than 30 miles per hour (approximately 48 km per hour) are due to tyre friction noise. 31,32 The major elements of moving vehicle signals are observed in the relatively lower frequency part of human perception range, e.g. up to a few kilo hertz.…”

Section: Acoustic Signature Of Vehiclesmentioning

confidence: 99%

Automated Acoustic Identification of Vehicles

EVANS,

CHESMORE

2023

Spring Conference Acoustics 2008

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Section: Acoustic Signature Of Vehiclesmentioning

confidence: 99%

Automated Acoustic Identification of Vehicles

EVANS,

CHESMORE

2023

Spring Conference Acoustics 2008

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“…Information theoretical approaches including the KL method were applied to the data for sensor management [53] as shown in Figure 2. Processing sensor data for target classification using acoustic [54,55] and seismic [56] results have been explored in support of information theoretical sensor placement [57].…”

Section: Introductionmentioning

confidence: 99%

DSmT applied to seismic and acoustic sensor fusion

Blasch

Dezert

Valin

2011

Proceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON)

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In this paper, we explore the use of the DezertSmarandache Theory (DSmT) for seismic and acoustic sensor fusion. The seismic/acoustic data is noisy which leads to classification errors and conflicts in declarations. DSmT affords the redistribution of masses when there is a conflict. The goal of this paper is to present an application and comparison on DSmT with other classifier methods to include the support vector machine(SVM) and DempsterShafer (DS) methods. The work is based on two key references (1) Marco Duarte with the initial SVM classifier application of the seismic and acoustic sensor data and (2) Arnaud Martin in Vol. 3 with the Proportional Conflict Redistribution Rule 5/6 (PCR5/PCR6) developments. By using the developments of Duarte and Martin, we were able to explore the various aspects of DSmT in an unattended ground sensor scenario. Using the receiver operator curve (ROC), we compare the methods for individual classification as well as a measure of overall classification using the area under the curve (AUC). Conclusions of the work show that the DSmT results with a maximum forced choice are comparable to the SVM.

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Classification of Battlefield Ground Vehicles Based on the Acoustic Emissions

Mendel

2010

Innovations in Defence Support Systems – 1

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<title>Netted sensors-based vehicle acoustic classification at Tier 1 nodes</title>

Cited by 4 publications

References 4 publications

Automated Acoustic Identification of Vehicles

Automated Acoustic Identification of Vehicles

DSmT applied to seismic and acoustic sensor fusion

Classification of Battlefield Ground Vehicles Based on the Acoustic Emissions

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