Feature-aided localization and tracking of ground vehicles using passive acoustic sensor networks

Ravindra, Vishal Cholapadi; Bar‐Shalom, Yaakov; Damarlay, Thyagaraju

doi:10.1109/camsap.2009.5413291

Cited by 7 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another direction of development lies in the integration of other types of sensors in order to enable enhanced environment perception by means of data fusion [3]. On the other hand, application of several microphones in an array configuration would permit to localize the vehicles' positions in the monitored area [18].…”

Section: Discussionmentioning

confidence: 99%

A Multistage Procedure of Mobile Vehicle Acoustic Identification for Single-Sensor Embedded Device

Astapov¹,

Riid²

2013

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

A Multistage Procedure of Mobile Vehicle Acoustic Identification for Single-Sensor Embedded Device

Astapov¹,

Riid²

2013

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

“…Such augmentation is usually referred to as feature-aided data association or tracking, and a large variety of methods have been proposed and analyzed. Recent works include [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Performance prediction of feature-aided track-to-track association

Mori

Chang

Chong

2014

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

This paper describes analytic and semianalytic methods for predicting performance of track-to-track association, in terms of correct association probability, by an optimal assignment algorithm. The focus of this paper is to quantify how much feature or attribute information may improve association performance over the standard kinematic-only track-to-track association. Our goal is to obtain an analytical formula to predict the association performance as a function of a set of key parameters that quantify the quality of feature information. The result extends our previous development of an exponential law for predicting association performance, by including the effects of the additional generally non-Gaussian feature or attribute information.

show abstract

“…Hence, effective intrusion detection is a great challenge because of the processing capability and battery life resource limitations of sensor nodes that make up most economically viable WSNs [2]. Acoustic sensor has the advantages of low cost, low power consumption, and non-line of sight measurement which is widely adopted for a long time military terrain reconnaissance [3,4,5,6,7,8,9]. …”

Section: Introductionmentioning

confidence: 99%

Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array

Guo

Huang

et al. 2017

Sensors

View full text Add to dashboard Cite

Automated surveillance of remote locations in a wireless sensor network is dominated by the detection algorithm because actual intrusions in such locations are a rare event. Therefore, a detection method with low power consumption is crucial for persistent surveillance to ensure longevity of the sensor networks. A simple and effective two-stage algorithm composed of energy detector (ED) and delay detector (DD) with all its operations in time-domain using small-aperture microphone array (SAMA) is proposed. The algorithm analyzes the quite different velocities between wind noise and sound waves to improve the detection capability of ED in the surveillance area. Experiments in four different fields with three types of vehicles show that the algorithm is robust to wind noise and the probability of detection and false alarm are 96.67% and 2.857%, respectively.

show abstract

Feature-aided localization and tracking of ground vehicles using passive acoustic sensor networks

Cited by 7 publications

References 4 publications

A Multistage Procedure of Mobile Vehicle Acoustic Identification for Single-Sensor Embedded Device

A Multistage Procedure of Mobile Vehicle Acoustic Identification for Single-Sensor Embedded Device

Performance prediction of feature-aided track-to-track association

Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array

Contact Info

Product

Resources

About