School of Mechano-Electronic Engineering, Xidian University, PR China.
School of Automation, Northwestern Polytechnical University, PR China; Key Laboratory of Information Fusion Technology, Ministry of Education, PR China.
ISA Trans. 2018 Sep;80:111-126. doi: 10.1016/j.isatra.2018.05.018. Epub 2018 Jun 1.
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.
本文考虑了具有服从阶 n 非线性自回归过程的有色测量噪声的离散时间马尔可夫跳跃非线性系统的状态估计，这是受高速采样或持续干扰下对机动目标进行跟踪的启发。为了消除测量噪声相关性，通过差分方法探索左零除数，通过将统计线性回归应用于有色测量噪声模型来重建新的测量方程。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，并递归推导出状态的后验概率密度。通过使用高斯混合来近似后验概率密度，提出了一种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高斯混合的 Alpha（或 Beta）散度，自适应地修剪具有小权重的高斯分量，以在估计精度和运行时间之间取得折衷。模拟了伴随不同有色测量噪声情况的距离门拖引的机动目标跟踪，以验证所提出的方法。
