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Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

1
School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China
2
School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(12), 2665; https://doi.org/10.3390/s19122665
Received: 9 May 2019 / Revised: 9 June 2019 / Accepted: 11 June 2019 / Published: 13 June 2019
(This article belongs to the Section Physical Sensors)
The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms. View Full-Text
Keywords: multiple extended target filter; partitioning algorithm; extended target tracking multiple extended target filter; partitioning algorithm; extended target tracking
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Han, Y.; Han, C. Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter. Sensors 2019, 19, 2665.

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