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Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array
by
Zu, Xingshui
, Liu, Huawei
, Yuan, Xiaobing
, Guo, Feng
, Zhao, Qin
, Huang, Jingchang
, Li, Baoqing
in
Algorithms
/ Health surveillance
/ intrusion detection
/ Intrusion detection systems
/ small-aperture microphone array
/ time delay estimation
/ wind noise
/ WSN
2017
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Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array
by
Zu, Xingshui
, Liu, Huawei
, Yuan, Xiaobing
, Guo, Feng
, Zhao, Qin
, Huang, Jingchang
, Li, Baoqing
in
Algorithms
/ Health surveillance
/ intrusion detection
/ Intrusion detection systems
/ small-aperture microphone array
/ time delay estimation
/ wind noise
/ WSN
2017
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Do you wish to request the book?
Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array
by
Zu, Xingshui
, Liu, Huawei
, Yuan, Xiaobing
, Guo, Feng
, Zhao, Qin
, Huang, Jingchang
, Li, Baoqing
in
Algorithms
/ Health surveillance
/ intrusion detection
/ Intrusion detection systems
/ small-aperture microphone array
/ time delay estimation
/ wind noise
/ WSN
2017
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Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array
Journal Article
Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array
2017
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Overview
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.
Publisher
MDPI AG,MDPI
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