Adaptive Gaussian mixture filter for Markovian jump nonlinear systems with colored measurement noises
Adaptive Gaussian mixture filter for Markovian jump nonlinear systems with colored measurement noises
复制标题
用于具有有色测量噪声的马尔可夫跳跃非线性系统的自适应高斯混合滤波器
DOI：
10.1016/j.isatra.2018.05.018
复制
发表时间：
2018
期刊：
影响因子：
7.3
通讯作者：
Xiaoxu Wang
中科院分区：
文献类型：
--
作者：
Yanbo Yang;Yan Liang;Quan Pan;Yuemei Qin;Xiaoxu Wang
本文考虑离散时间马尔可夫跳跃非线性系统的状态估计问题，其中有色测量噪声服从n阶非线性自回归过程，其动机是在具有高速采样或持续扰动的电子对抗下跟踪机动目标。为了消除测量噪声的相关性，将统计线性回归应用到有色测量噪声模型中，利用左零因子通过差分法重构了一个新的测量方程。然后，定义了一个由多步马尔可夫跳跃参数的所有可能值组成的新假设集，并递归地推导出状态的后验概率密度。通过使用高斯混合近似后验概率密度，提出了一种适用于所考虑系统的自适应高斯混合滤波器，其中，通过测量原始和近似的高斯混合的Alpha(或Beta)散度，自适应地修剪具有较小权值的高斯分量，以实现估计精度和运行时间之间的折衷。对不同有色测量噪声情况下的距离门拉离机动目标跟踪进行了仿真，验证了该方法的有效性。
This paper considers the state estimation of discrete-time Markovian jump nonlinear systems with colored measurement noises obeying a nonlinear autoregressive process of order n, which is motivated by tracking the maneuvering target under electronic countermeasures with high speed sampling or persistent perturbations. In order to remove the measurement noises correlation, the left zero divisor is explored to reconstruct a new measurement equation via difference approach, with the help of applying statistical linear regression to the colored measurement noise model. Then, a novel hypothesis set constituted of all possible values of multi-step Markov jumping parameters is defined and the posterior probability density of the state is derived recursively. By using Gaussian mixtures to approximate the posterior probability densities, an adaptive Gaussian mixture filter for the considered system is proposed, where the Gaussian components with small weights are pruned adaptively through measuring the Alpha (or Beta) divergence for the original and approximated Gaussian mixtures, to achieve a tradeoff between the estimation accuracy and running time. A maneuvering target tracking accompanied by range gate pull-off with different colored measurement noises cases is simulated to validate the proposed method.