GMPHD based on measurement conversion sequential filtering for maneuvering target tracking

被引：0
作者：
Hou Z. [1 ]
Cheng T. [1 ]
Peng H. [1 ]
机构：
[1] School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 08期
关键词：
Gaussian mixture probability hypothesis density; maneuvering target tracking; multiple model; multiple target tracking; nonlinear measurement;
D O I：
10.12305/j.issn.1001-506X.2022.08.11
中图分类号：
学科分类号：
摘要：
For multiple maneuvering targets tracking by Doppler radar in clutter, a multiple model Gaussian probability hypothesis density algorithm based on decorrelated unbiased converted measurement sequential filter is proposed. For the nonlinearity of the measurements, the position measurements are converted to unbiased measurements, and the Doppler measurement is converted to debiased pseudo measurement, and the tracking accuracy is improved by sequential filtering. For the maneuverability of the target, the idea of multiple model is introduced into Gaussian mixture probability hypothesis density (GMPHD), where the Gaussian components related to the model are predicted and updated. Simulation results demonstrate that the proposed algorithm can achieve effective maneuvering multi-target tracking in clutter. Compared with unscented Kalman multiple model GMPHD, the tracking accuracy is increased by 38.15%, and the algorithm efficiency is greatly improved. Compared with unscented Kalman best-fitting Gaussian approximation GMPHD, the efficiency is slightly increased, and the tracking accuracy is improved by 36.47%. © 2022 Chinese Institute of Electronics. All rights reserved.
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收藏
页码：2474 / 2482
页数：8
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