Modified Particle Filtering Algorithm for Single Acoustic Vector Sensor DOA Tracking

• Published in: Sensors (Basel, Switzerland) Vol. 15; no. 10; pp. 26198 - 26211
• Main Authors: Li, Xinbo, Sun, Haixin, Jiang, Liangxu, Shi, Yaowu, Wu, Yue
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16.10.2015; MDPI
• Subjects: acoustic vector sensor; Acoustics; Algorithms; Batch processing; DOA tracking; Filtering; Filtration; importance function; Mathematical analysis; Mathematical models; Music; particle filtering; Sensors; Signal processing; Spread spectrum; Vectors (mathematics); China; DOA tracking; acoustic vector sensor; importance function; particle filtering
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s151026198

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.