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Robust multi-sensor generalized labeled multi-Bernoulli filter
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DOI：10.1016/j.sigpro.2021.108368.png)
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Abstract
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This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sensors. In particular, we propose to combine the multi-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter to estimate target tra-jectories and robust Cardinalized Probability Hypothesis Density (CPHD) filters to estimate the clutter rates. The target detection probability is augmented to the filtering state space for joint estimation. Ex-perimental results show that the proposed robust filter exhibits near-optimal performance in the sense that it is comparable to the optimal MS-GLMB operating with true clutter rate and detection probabil-ity. More importantly, it outperforms other studied filters when the detection profile and clutter rate are unknown and time-variant. This is attributed to the ability of the robust filter to learn the background parameters on-the-fly. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Multi-sensor GLMB filter
Robust tracking
Bearing-only sensors
Bootstrapping method
Labeled random finite sets
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