Signal Processing ( IF 4.4 ) Pub Date : 2021-08-11 , DOI: 10.1016/j.sigpro.2021.108290 Zihao Jiang 1 , Weidong Zhou 1 , Chen Chen 1 , Liang Hou 1
To better model one-step randomly delayed measurements (ORDM) with unknown time-varying latency probability (UTLP) in linear systems with heavy-tailed measurement noise (HMN), a novel Normal-Gamma-Beta mixture (NGBM) distribution is presented. By introducing a Bernoulli random variable, the probability density function of the proposed NGBM distribution can be reformulated as a Gaussian hierarchical form. Based on this, a novel robust Kalman filter is designed using the variational Bayesian technique. A target tracking simulation verifies the potential of the proposed robust filter, which has higher filtering accuracy than existing cutting-edge filters and can adaptively estimate the UTLP. Furthermore, it is concluded that when HMN and ORDM concurrently exist, the HMN has more influence on the accuracy of the filter than the ORDM.
中文翻译:
一种具有未知时变延迟概率自适应估计的新型鲁棒卡尔曼滤波器
为了在具有重尾测量噪声 (HMN) 的线性系统中更好地模拟具有未知时变延迟概率 (UTLP) 的一步随机延迟测量 (ORDM),提出了一种新的正态-伽马-Beta 混合 (NGBM) 分布。通过引入伯努利随机变量,所提出的 NGBM 分布的概率密度函数可以重新表述为高斯分层形式。基于此,使用变分贝叶斯技术设计了一种新颖的鲁棒卡尔曼滤波器。目标跟踪仿真验证了所提出的鲁棒滤波器的潜力,它比现有的尖端滤波器具有更高的滤波精度,并且可以自适应地估计 UTLP。进一步得出结论,当HMN和ORDM同时存在时,HMN比ORDM对滤波器精度的影响更大。