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Title: Multisensor Multiobject Tracking With Improved Sampling Efficiency
Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA) and implemented using random samples or “particles”. The multimodal probability density functions provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA with improved sample efficiency, we make use of particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results, obtained by processing synthetic and real data, demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.  more » « less
Award ID(s):
2146261
PAR ID:
10564930
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Transactions on Signal Processing
Volume:
72
ISSN:
1053-587X
Page Range / eLocation ID:
2036 to 2053
Subject(s) / Keyword(s):
Multiobject tracking, particle flow, factor graphs, sum-product algorithm.
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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