Abstract
Acoustic vector sensor (AVS) is an effective tool to tracking acoustic sources. However, for the problem of tracking multiple wideband sources using distributed AVS array (DAVS), there are still unsolved issues which include measurements-to-targets association and targets tracking under incorrect or unknown statistics of measurement noise. Joint probabilistic data association (JPDA) is an effective algorithm to solve data association between measurements and targets and JPDA based cubature information filter (MTCIF) is designed for nonlinear system. Meanwhile, noise statistics estimator (NSE) based on modified Sage-Husa maximum posterior (SHMP) is constructed to cope with incorrect or unknown statistics of measurement noise. Then, a two-step distributed information fusion based on weighted average consensus (WAC) is built for DAVS to improve the stability and accuracy of state estimator and NSE. Numerical simulations demonstrate the effectiveness of the proposed algorithms.
Highlights
Developed on the basis of acoustic pressure sensor, Acoustic vector sensor (AVS) is of low mass and small volume [1]
Joint probabilistic data association (JPDA) based MTCIF is used for multi-target tracking to solve the problem of measurementsto-targets association on the basis of acquired measurement direction of arrival (DOA)
The prior knowledge of measurement noise is usually kept unknown in the real application and initial statistics of measurement noise are incorrect, which always affects the precision of state estimation
Summary
Developed on the basis of acoustic pressure sensor, AVS is of low mass and small volume [1]. A distributed multi-target tracking algorithm was proposed in [8] based on Kalman consensus filtering (KCF) and joint probabilistic data association (JPDA). In order to solve the problem of nonlinearity in multi-target tracking, the distributed cubature information filtering based on JPDA and weighted consensus was proposed in [9] and [10] respectively. A two-step information fusion, including information fusion of state estimation and information fusion of statistics of measurement noise, is designed based on WAC to improve the accuracy and stability of NSE and state estimator. B) In consideration of the stability of NSE, a modified SHMP based NSE is design for MTCIF to estimate unknown statistics of measurement noise.
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