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非高斯噪声下的参数自适应高斯混合CQKF算法

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非高斯噪声下的参数自适应高斯混合CQKF算法
A Parameter Adaptive Gaussian Mixture CQKF Algorithm Under Non-Gaussian Noise
投稿时间:2017-05-29
DOI:10.15918/j.tbit1001-0645.2018.10.015
中文关键词:高斯混合模型容积卡尔曼滤波算法参数自适应方法初值优化
English Keywords:CKFGaussian mixture modelparameter adaptive methodrang parameterized
基金项目:国家自然科学基金资助项目(61153002,61473039)
作者单位E-mail
孟东北京理工大学自动化学院, 北京 100081
缪玲娟北京理工大学自动化学院, 北京 100081miaolingjuan@bit.edu.cn
邵海俊北京理工大学自动化学院, 北京 100081
沈军北京理工大学自动化学院, 北京 100081
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中文摘要:
研究非高斯噪声环境下的高斯混合滤波方法,进行纯方位跟踪系统的目标跟踪。利用改进的参数自适应方法,调整位移参数的大小,从而修正了高斯混合模型,提出了在非高斯噪声下的参数自适应高斯混合CQKF算法;基于非高斯噪声下的离散系统模型,分析了高斯混合CQKF算法中建模过程的局限性,并结合初值优化方法,提出了利用参数自适应方法修正高斯混合滤波模型的方法,从而克服了高斯混合滤波的局限性,提高了滤波精度。仿真实验表明在非高斯噪声下参数自适应高斯混合CQKF算法比原算法有更高的滤波精度。
English Summary:
A Gaussian mixture filtering method under non-Gaussian noise environment was studied, and the target tracking of pure azimuth tracking system was carried out. Firstly, a modified parameter adaptive method was used to adjust the size of the displacement parameter, so the Gaussian mixture model could be modified. The parameter adaptive Gaussian mixture CQKF algorithm (PGM-ACQKF) under non-Gaussian noise was proposed. Then based on the discrete system model under non-Gaussian noise, the limitations of the modeling process in the Gaussian mixture CQKF (GM-CQKF) was analyzed. Combining with the initial optimization method, a method to modify the Gaussian mixture model was proposed based on parameter adaptive method. Thus the limitations of GM-CQKF could be overcome and the filtering accuracy could be improved. The simulation results show the effectiveness of the proposed algorithm, which proves that the PGM-ACQKF has higher filtering accuracy than the original algorithm under non-Gaussian noise.
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