IMM Robust Cardinality Balance Multi-Bernoulli Filter for Multiple Maneuvering Target Tracking with Interval Measurement

YANG Biao; ZHU Shengqi; HE Xiongpeng

doi:10.1049/cje.2021.08.009

Volume 30 Issue 6

Nov. 2021

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Article Navigation > Chinese Journal of Electronics > 2021 > 30(6): 1141-1151

YANG Biao, ZHU Shengqi, HE Xiongpeng, “IMM Robust Cardinality Balance Multi-Bernoulli Filter for Multiple Maneuvering Target Tracking with Interval Measurement,” Chinese Journal of Electronics, vol. 30, no. 6, pp. 1141-1151, 2021, doi: 10.1049/cje.2021.08.009

Citation:

YANG Biao, ZHU Shengqi, HE Xiongpeng, “IMM Robust Cardinality Balance Multi-Bernoulli Filter for Multiple Maneuvering Target Tracking with Interval Measurement,” Chinese Journal of Electronics, vol. 30, no. 6, pp. 1141-1151, 2021, doi: 10.1049/cje.2021.08.009

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IMM Robust Cardinality Balance Multi-Bernoulli Filter for Multiple Maneuvering Target Tracking with Interval Measurement

doi: 10.1049/cje.2021.08.009

National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China

Funds:

This work is supported by the National Key R&D Program of China (No.2016YFE0200400), the National Natural Science Foundation of China (No.61771015), the Key R&D Program of Shaanxi Province (No.2017KW-ZD-12), the Innovative Research Group of National Natural Science Foundation of China (No.61621005), and the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project)(No.B18039).

Received Date: 2021-01-24
Rev Recd Date: 2021-04-05

Available Online: 2021-09-23

Publish Date: 2021-11-05

Abstract

Abstract

This paper presents a novel Interacting multi-model (IMM) Robust Cardinality balance multitarget multi-Bernoulli (R-CBMeMBer) filter to solve the maneuvering target tracking problem in the case of interval measurement, unknown detection probability and unknown clutter density. In essence, IMM R-CBMeMBer filter is an extended application of R-CBMeMBer filter. In the IMM R-CBMeMBer filter, the target state is first extended to distinguish clutter from the real target. The detection probability and model probability of the target can be adaptively updated. Then, generalized likelihood function and IMM algorithm are introduced to interactively predict and update the state of the target in the IMM R-CBMeMBer filtering process. In addition, a particle application of the IMM R-CBMeMBer filter is given, and a numerical experiment is designed under nonlinear conditions. Meanwhile, Doppler information of the target is employed to estimate the velocity of each maneuvering target. Numerical experiments also verify that the IMM R-CBMeMBer filter can effectively estimate the target position, target velocity, target detection probability and clutter number in the condition of unknown detection probability, unknown clutter rate and interval measurement.

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References(24)

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