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• W2966165605 abstract "In practical target tracking scenarios, targets of different sizes (or extents) may be near or far away from the sensor, which may result in targets appearing as point sources or as extended targets spanning one or more resolution cells, respectively, depending on distance and sensor resolution. In this paper, a new Gaussian Process (GP) measurement model is proposed to explicitly describe the observation about each basis point of GP by an individual dynamic Poisson measurement rate. By employing this model, a novel algorithm to track multiple point targets and extended targets, simultaneously and seamlessly, in the presence of clutter and missed detections is proposed within the Probabilistic Multi-Hypothesis Tracker (PMHT) framework. The proposed algorithm can adapt to spatio-temporally varying target sizes or extents of extended targets and temporally varying target cardinality. In addition, the posterior Cramer-Rao lower bound (PCRLB) for extended targets, which quantifies the accuracies of estimates of multiple extended target states in scenarios with clutter, is derived. Simulations with a scenario consisting of multiple extended targets and point targets are used to verify the effectiveness of the proposed algorithm and to compare its performance with the extended target PCRLB and with those of existing extended target tracking algorithms." @default.
• W2966165605 created "2019-08-13" @default.
• W2966165605 creator A5059262797 @default.
• W2966165605 creator A5073240631 @default.
• W2966165605 creator A5083431484 @default.
• W2966165605 date "2019-09-15" @default.
• W2966165605 modified "2023-10-16" @default.
• W2966165605 title "Seamless Tracking of Apparent Point and Extended Targets Using Gaussian Process PMHT" @default.
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• W2966165605 doi "https://doi.org/10.1109/tsp.2019.2932873" @default.
• W2966165605 hasPublicationYear "2019" @default.
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