Abstract
In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.
Highlights
Target tracking uses noisy observations received by sensors at discrete time instances to sequentially estimate the target state of interest evolving over time
After receiving sets of raw measurements, the local sensor carried out the local pseudo tracking using the proposed local integrated probabilistic data association (LIPDA) method, which tracks the measurement state rather than the target kinematic state
The refined bearing measurements transmitted by each sonar are sequentially fused using the proposed DIPDA-forward prediction fusion and decorrelation (FPFD) algorithm, among them, the information transferred by sonar s3 arrives in the fusion center with out of sequence
Summary
Target tracking uses noisy observations received by sensors at discrete time instances to sequentially estimate the target state of interest evolving over time. [28] suggested a new methodology termed as the forward prediction fusion and decorrelation (FPFD) for tackling the OOSM problem without relying on the retrodiction technique, wherein, a tracklet is created and predicted forward and decorrelated from the actual track in the information space It was proved in [28] that the FPFD method performs as well as the retrodiction-based approaches, while requiring less data storage in most case. After receiving sets of raw measurements, the local sensor carried out the local pseudo tracking using the proposed local integrated probabilistic data association (LIPDA) method, which tracks the measurement state rather than the target kinematic state (since target kinematic state is unobservable by a single BO sensor) Such a design enables eliminating most of the false tracks via the track management using the recursively computed PTE, resulting in tangibly reduced communication bandwidth and computation complexity, the accuracy of target measurement can be further improved.
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