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Filtering in Pairwise Markov Model With Student's t Non-Stationary Noise With Application to Target Tracking
IEEE Transactions on Signal Processing ( IF 5.4 ) Pub Date : 2021-02-26 , DOI: 10.1109/tsp.2021.3062170
Guanghua Zhang , Jian Lan , Le Zhang , Fengshou He , Shaomin Li

Hidden Markov models are widely used for target tracking, where the process and measurement noises are usually modeled as independent Gaussian distributions for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For example, in a typical target tracking application, a radar is utilized to track a non-cooperative target. Measurement noise is correlated over time since the sampling frequency of a radar is usually far greater than the bandwidth of measurement noise. Besides, when target is maneuvering, the process and measurement noises are heavy-tailed and non-Gaussian due to intrinsic data generation mechanism. In this paper, we consider a linear pairwise Markov model (PMM) with Student's t noise to model non-cooperative single target tracking without clutter and missed detections. A PMM is more general than an HMM and can be used to model correlated measurement noise or correlated process and measurement noises. The Student's t distribution is one of the most commonly used heavy-tailed distribution and can be used to address irregular target motion. We mainly focus on solving the filtering problems for the model. First, we develop a filter for the case where noise statistics are accurately known. Second, we further consider the case of non-stationary Student's t noise, and propose a novel robust filter by employing a variational Bayesian method. Finally, the effectiveness of the proposed filters is illustrated via simulation results.

中文翻译:

具有学生t非平稳噪声的成对马尔可夫模型的滤波及其在目标跟踪中的应用

隐马尔可夫模型广泛用于目标跟踪,其中过程和测量噪声通常被建模为独立的高斯分布,以简化数学。但是,独立性和高斯假设在实践中并不总是成立。例如,在典型的目标跟踪应用中,雷达被用来跟踪非合作目标。由于雷达的采样频率通常远大于测量噪声的带宽,因此测量噪声会随时间而变化。此外,在操纵目标时,由于固有的数据生成机制,过程噪声和测量噪声都是重尾且非高斯噪声。在本文中,我们考虑了带有学生t噪声的线性成对马尔可夫模型(PMM),以对不合作且不漏检的单目标跟踪进行建模。PMM比HMM更通用,可用于建模相关的测量噪声或相关的过程和测量噪声。学生的t分布是最常用的重尾分布之一,可用于解决不规则目标运动。我们主要集中于解决模型的过滤问题。首先,我们针对准确知道噪声统计信息的情况开发了一个滤波器。其次,我们进一步考虑非平稳学生t噪声的情况,并通过使用变分贝叶斯方法提出一种新颖的鲁棒滤波器。最后,通过仿真结果说明了所提出的滤波器的有效性。st分布是最常用的重尾分布之一,可用于解决不规则目标运动。我们主要集中于解决模型的过滤问题。首先,我们针对准确知道噪声统计信息的情况开发了一个滤波器。其次,我们进一步考虑非平稳学生t噪声的情况,并通过使用变分贝叶斯方法提出一种新颖的鲁棒滤波器。最后,通过仿真结果说明了所提出的滤波器的有效性。st分布是最常用的重尾分布之一,可用于解决不规则目标运动。我们主要集中于解决模型的过滤问题。首先,我们针对准确知道噪声统计信息的情况开发了一个滤波器。其次,我们进一步考虑非平稳学生t噪声的情况,并通过使用变分贝叶斯方法提出一种新颖的鲁棒滤波器。最后,通过仿真结果说明了所提出的滤波器的有效性。并提出了一种新颖的鲁棒滤波器,它采用了变分贝叶斯方法。最后,通过仿真结果说明了所提出的滤波器的有效性。并提出了一种新颖的鲁棒滤波器,它采用了变分贝叶斯方法。最后,通过仿真结果说明了所提出的滤波器的有效性。
更新日期:2021-03-23
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