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Open AccessArticle

Joint Detection and DOA Tracking with a Bernoulli Filter Based on Information Theoretic Criteria

by Guangpu Zhang 1,2,3,4, Ce Zheng 1,2,3, Sibo Sun 1,2,3,*, Guolong Liang 1,2,3,4 and Yifeng Zhang 1,2,3
1
Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
2
Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China
3
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
4
Qindao Haina Underwater Information Technology Co., Ltd., Qindao 266500, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(10), 3473; https://doi.org/10.3390/s18103473
Received: 11 September 2018 / Revised: 12 October 2018 / Accepted: 14 October 2018 / Published: 15 October 2018
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
In this paper, we study the problem of the joint detection and direction-of-arrival (DOA) tracking of a single moving source which can randomly appear or disappear from the surveillance volume. Firstly, the Bernoulli random finite set (RFS) is employed to characterize the randomness of the state process, i.e., the dynamics of the source motion and the source appearance. To increase the performance of the detection and DOA tracking in low signal-to-noise ratio (SNR) scenarios, the measurements are obtained directly from an array of sensors and allow multiple snapshots. A track-before-detect (TBD) Bernoulli filter is proposed for tracking a randomly on/off switching single dynamic system. Secondly, since the variances of the stochastic signal and measurement noise are unknown in practical applications, these nuisance parameters are marginalized by defining an uninformative prior, and the likelihood function is compensated by using the information theoretic criteria. The simulation results demonstrate the performance of the filter. View Full-Text
Keywords: DOA; Bernoulli filter; track before detect DOA; Bernoulli filter; track before detect
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MDPI and ACS Style

Zhang, G.; Zheng, C.; Sun, S.; Liang, G.; Zhang, Y. Joint Detection and DOA Tracking with a Bernoulli Filter Based on Information Theoretic Criteria. Sensors 2018, 18, 3473.

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