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2018 | OriginalPaper | Chapter

6. Bearing-Only Multiple Target Tracking with the Sequential PHD Filter for Multi-Sensor Fusion

Authors : Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong

Published in: Non-Cooperative Target Tracking, Fusion and Control

Publisher: Springer International Publishing

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Abstract

It is a widely studied problem to locate multiple emitters from passive angle measurements. The traditional multi-sensor passive multi-objective state estimation problem is a data association problem. In mathematics, the representation of data association problems leads to the generalization of the S-dimensional (S-D) assignment problem. Unfortunately, the complexity of solving an S-D assignment problem for S ≥ 3 is a nondeterministic polynomial (NP) hard problem. Multistage Lagrangian relaxation is a practical solution to solve the multidimensional assignment problem. However, its computational complexity rapidly increases with the sensor’s growth. In addition, satisfactory results cannot be achieved in dense clutter. In this chapter, we use the sequential Probability Hypothesis Density (PHD) filter for passive sensors in two different ways to solve the localization problem of multiple emitters. Simulation results verify the effectiveness of the algorithm. Compared with S-D assignment method, the proposed method can achieve better performance and with smaller computational complexity in environment with dense clutter.

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previous chapter Multi-Target Tracking with Gaussian Mixture CPHD Filter and Gating Technique
next chapter Joint Detection, Tracking, and Classification Using Finite Set Statistics and Generalized Bayesian Risk
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Metadata
Title
Bearing-Only Multiple Target Tracking with the Sequential PHD Filter for Multi-Sensor Fusion
Authors
Zhongliang Jing
Han Pan
Yuankai Li
Peng Dong
Copyright Year
2018
Book
Non-Cooperative Target Tracking, Fusion and Control

Print ISBN: 978-3-319-90715-4

Electronic ISBN: 978-3-319-90716-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-90716-1_6

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