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Sensors 2015, 15(10), 26198-26211; doi:10.3390/s151026198

Modified Particle Filtering Algorithm for Single Acoustic Vector Sensor DOA Tracking

1
,
2,†
,
1,†
,
1,†
and
3,*
1
School of Communication Engineering, Jilin University, Renmin Street No. 5988, Changchun 130022, China
2
School of Electronic and Information Engineering, Changchun University, Weixing Road, No. 6543, Changchun 130022, China
3
School of Mechanical Science and Engineering, Jilin University, Renmin Street No. 5988, Changchun 130022, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 6 August 2015 / Revised: 4 October 2015 / Accepted: 9 October 2015 / Published: 16 October 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1172 KB, uploaded 19 October 2015]   |  

Abstract

The conventional direction of arrival (DOA) estimation algorithm with static sources assumption usually estimates the source angles of two adjacent moments independently and the correlation of the moments is not considered. In this article, we focus on the DOA estimation of moving sources and a modified particle filtering (MPF) algorithm is proposed with state space model of single acoustic vector sensor. Although the particle filtering (PF) algorithm has been introduced for acoustic vector sensor applications, it is not suitable for the case that one dimension angle of source is estimated with large deviation, the two dimension angles (pitch angle and azimuth angle) cannot be simultaneously employed to update the state through resampling processing of PF algorithm. To solve the problems mentioned above, the MPF algorithm is proposed in which the state estimation of previous moment is introduced to the particle sampling of present moment to improve the importance function. Moreover, the independent relationship of pitch angle and azimuth angle is considered and the two dimension angles are sampled and evaluated, respectively. Then, the MUSIC spectrum function is used as the “likehood” function of the MPF algorithm, and the modified PF-MUSIC (MPF-MUSIC) algorithm is proposed to improve the root mean square error (RMSE) and the probability of convergence. The theoretical analysis and the simulation results validate the effectiveness and feasibility of the two proposed algorithms. View Full-Text
Keywords: DOA tracking; particle filtering; importance function; acoustic vector sensor DOA tracking; particle filtering; importance function; acoustic vector sensor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, X.; Sun, H.; Jiang, L.; Shi, Y.; Wu, Y. Modified Particle Filtering Algorithm for Single Acoustic Vector Sensor DOA Tracking. Sensors 2015, 15, 26198-26211.

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