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Sensors 2018, 18(4), 1153;

δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Author to whom correspondence should be addressed.
Received: 5 February 2018 / Revised: 4 April 2018 / Accepted: 4 April 2018 / Published: 10 April 2018
(This article belongs to the Section Intelligent Sensors)
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The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment. View Full-Text
Keywords: δ-GLMB filter; amplitude information; neighboring cells; multi-target tracking δ-GLMB filter; amplitude information; neighboring cells; multi-target tracking

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Liu, C.; Sun, J.; Lei, P.; Qi, Y. δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells. Sensors 2018, 18, 1153.

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