Particle PHD filter multiple target tracking in sonar image

Daniel Clark, Ioseba Tena Ruiz, Yvan Petillot, Judith Bell

Research output: Contribution to journalArticlepeer-review

108 Citations (Scopus)


Two contrasting approaches for tracking multiple targets in multi-beam forward-looking sonar images are considered. The first approach is based on assigning a Kalman filter to each target and managing the measurements with gating and a measurement-to-track data association technique. The second approach uses the recently developed particle implementation of the multiple-target probability hypothesis density (PHD) filter and a target state estimate-to-track data association technique. The two approaches are implemented and compared on both simulated sonar and real forward-looking sonar data obtained from an Autonomous Underwater Vehicle (AUV) and demonstrate that the PHD filter with data association compares well with traditional approaches for multiple target tracking. © 2007 IEEE.

Original languageEnglish
Pages (from-to)409-415
Number of pages7
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number1
Publication statusPublished - Jan 2007


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