Sometimes radar targets cross and become unresolved; this is a concern, but with a reasonable track depth and an appropriate merged-measurement model the concern is considerably mitigated. Sonar targets, however, can become merged (in the same beam) for considerably longer, particularly with bearing-only measurements. In such cases the crossing times can be 100 scans long, and no reasonable depth exists for an multi-frame tracker that can "see" both ends of the merged period. Further, there is a demonstrable tendency for estimated targets to repel each other as they are being tracked. In this paper we explore the hypothesis-oriented multi-hypothesis tracker (HO-MHT), an MHT approach that uses the new "rollout" optimization insight and the to give an appropriate and cost-effective means to rank hypotheses, and also the PMHT tracker that operates on batches of scans with linear computational complexity in most quantities. We show results in terms of estimation error (RMSE), consistency (NEES) and computational effort in both linear and beam-space tracking scenarios.
Target localization f r o m bearings observations in a multi-target and multi-sensor environment is a difficult problem. I n this paper we present TSBEAR (Track Segment Bearings-only Association and Ranging), a n e w algorithm f o r bearings only localization with multiple targets. Groups of bearings-only track segments f r o m multiple targets and f r o m several own ship legs and sensors are associated and used to localize the targets. TSBEAR i s novel since it requires neither enumeration n o r pruning and the estimation and association steps are performed jointly. I t i s demonstratedwith simulated data and its performance to single target algorithms is discussed.
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