A Novel Robust Kalman Filter With Non-stationary Heavy-tailed Measurement Noise <ce:cross-ref refid='fn1'> <ce:sup loc='post'>?</ce:sup> </ce:cross-ref>

Guangle Jia; Yulong Huang; Mingming B. Bai; Yonggang zhang

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A Novel Robust Kalman Filter With Non-stationary Heavy-tailed Measurement Noise ?

机译：一种具有非静止重尾测量噪声的新型鲁棒卡尔曼滤波器？

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A novel robust Kalman filter based on Gaussian-Student’s t mixture (GSTM) distribution is proposed to address the filtering problem of a linear system with non-stationary heavy-tailed measurement noise. The mixing probability is recursively estimated by using its previous estimates as prior information, and the state vector, the auxiliary parameter, the Bernoulli random variable and the mixing probability are jointly estimated utilizing the variational Bayesian method. The excellent performance of the proposed robust Kalman filter, compared with the existing state-of-the-art filters, is illustrated by a target tracking simulation results under the case of non-stationary heavy-tailed measurement noise.

机译：提出了一种基于高斯学生的T混合物（GSTM）分布的新型鲁棒卡尔曼滤波器，以解决具有非静止重尾测量噪声的线性系统的过滤问题。通过使用其先前的估计作为先前信息，以及状态向量，辅助参数，伯努利随机变量和混合概率利用变分贝叶斯方法来递归地估计混合概率。与现有的最先进的滤波器相比，所提出的鲁棒卡尔曼滤波器的优异性能由非静止重尾测量噪声的情况下的目标跟踪仿真结果示出。

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《IFAC PapersOnLine》 |2020年第2期|共6页
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Guangle Jia; Yulong Huang; Mingming B. Bai; Yonggang zhang;
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Robust Kalman filterGaussian-Student’s t mixturenon-stationary heavy-tailed measurement noisevariational Bayesian;

机译：强大的卡尔曼滤波器gaussian-学生的t mixturenon-instary重尾测量noisevariedal贝叶斯;

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1. Model Predictive Control meets robust Kalman filtering * * This work has been partially supported by the FIRB project “Learning meets time” (RBFR12M3AC) funded by MIUR. [J] . Alberto Zenere, Mattia Zorzi IFAC PapersOnLine . 2017,第1期

机译：模型预测控制满足鲁棒的卡尔曼滤波 * * FIRB项目“学习遇见时间”（RBFR12M3AC ）由MIUR资助。
2. Kalman Filtering with Unknown Sensor Measurement Losses * * This work was supported by the National Natural Science Foundation of China (41576101), and Tsinghua University Initiative Scientific Research Program. [J] . Jiaqi Zhang, Keyou You IFAC PapersOnLine . 2016,第22期

机译：具有未知传感器测量损耗的卡尔曼滤波 * < ce：footnote id =“ fn1”> * 这项工作得到了国家自然科学基金的支持（41576101）和清华大学主动科学研究计划。
3. Pseudomeasurement Kalman filter in underwater target motion analysis & Integration of bearing-only and active-range measurement * * A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, IITP RAS, Moscow. [J] . A. Miller, B. Miller IFAC PapersOnLine . 2017,第1期

机译：水下目标运动分析中的伪测量卡尔曼滤波器以及仅方位角和活动范围测量的集成 * * AA莫斯科IITP RAS俄罗斯科学院Kharkevich信息传输问题研究所。
4. Robust weighted fusion Kalman filter with multiplicative noises, uncertain noise variances, and missing measurements [C] . Wenqiang Liu, Zili Deng 2016 IEEE International Conference on Electronic Information and Communication Technology . 2016

机译：鲁棒加权融合卡尔曼滤波器，具有乘法噪声，不确定的噪声方差和丢失的测量
5. ROBUST ADAPTIVE KALMAN FILTERING WITH UNKNOWN INPUTS (SIGNAL-PROCESSING). [D] . MOGHADDAMJOO, ALIREZA. 1986

机译：鲁棒的自适应卡尔曼滤波，输入未知（信号处理）。
6. Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises [O] . Ying Fan, Yonggang Zhang, Guoqing Wang, 2018

机译：基于最大熵的无味粒子滤波器用于带有重尾测量噪声的协同导航
7. SymposiumSubspecialty Certification: Current Status of Orthopaedic Subspecialty Certification* [O] . Keith H. Bridwell 2006

机译：Symposium 亚专业认证：矫形亚专业证书的当前状态 * / cross-ref>

A Novel Robust Kalman Filter With Non-stationary Heavy-tailed Measurement Noise ?

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