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CPHD-DOA Tracking of Multiple Extended Sonar Targets in Impulsive Environments

  • ENST Bretagne

Research output: Contribution to journalArticlepeer-review

115 Citations (Scopus)

Abstract

In this paper, we propose a novel phased-array track before detect (TBD) filter for tracking multiple distributed (extended) targets from impulsive observations. Since the targets are angularly spread, we track the centroid Direction Of Arrival (DOA) of the target-generated (or backscattered) signal. The main challenge stems from the random target signals that, conditional to their respective states, constitute non-deterministic contributions to the system observation. The novelty of our approach is twofold. First, we develop a Cardinalized Probability Hypothesis Density (CPHD) filter for tracking multiple targets with non-deterministic contributions, more specifically, Spherically Invariant Random Vector (SIRV) processes. This is achieved by analytically integrating the SIRV and angularly distributed target signals in the update step. Thus, ensuring a more efficient implementation than existing solutions, that generally consider augmenting the target state with the target signal. Secondly, we provide an improved auxiliary particle CPHD filter and clustering methodology. The auxiliary step is carried out for persistent particles, while for newly birthed particles an adaptive importance distribution is given. This is in contrast with existing solutions that only consider the auxiliary step for birthed particles. Simulated data results showcase the improved performance of the proposed filter. Results on real sonar phased-array data are presented for underwater 3D image reconstruction applications.

Original languageEnglish
Article number7339699
Pages (from-to)1147-1160
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume64
Issue number5
DOIs
Publication statusPublished - 1 Mar 2016
Externally publishedYes

Keywords

  • CPHD filter
  • DOA tracking
  • extended target
  • multivariate Laplace distribution
  • track before detect

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