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一种基于变分贝叶斯优化的迭代非线性滤波器。

An Iterative Nonlinear Filter Using Variational Bayesian Optimization.

机构信息

School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.

Key Laboratory of Information Fusion Technology, Ministry of Education, Xi'an 710072, China.

出版信息

Sensors (Basel). 2018 Dec 1;18(12):4222. doi: 10.3390/s18124222.

DOI:10.3390/s18124222
PMID:30513784
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6308601/
Abstract

We propose an iterative nonlinear estimator based on the technique of variational Bayesian optimization. The posterior distribution of the underlying system state is approximated by a solvable variational distribution approached iteratively using evidence lower bound optimization subject to a minimal weighted Kullback-Leibler divergence, where a penalty factor is considered to adjust the step size of the iteration. Based on linearization, the iterative nonlinear filter is derived in a closed-form. The performance of the proposed algorithm is compared with several nonlinear filters in the literature using simulated target tracking examples.

摘要

我们提出了一种基于变分贝叶斯优化技术的迭代非线性估计器。通过使用证据下界优化来逼近底层系统状态的后验分布，其中考虑了一个惩罚因子来调整迭代步长，以最小化加权 Kullback-Leibler 散度。基于线性化，推导出了一种闭式迭代非线性滤波器。通过模拟目标跟踪示例，将所提出的算法与文献中的几种非线性滤波器进行了性能比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/49f7c64356ea/sensors-18-04222-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/f03a3434167d/sensors-18-04222-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/27ddd471bc5c/sensors-18-04222-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/39a3d2ba290d/sensors-18-04222-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/cf6ba7949c93/sensors-18-04222-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/62072d9b5d48/sensors-18-04222-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/8e6051718533/sensors-18-04222-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/3dd754fa0ce5/sensors-18-04222-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/49f7c64356ea/sensors-18-04222-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/f03a3434167d/sensors-18-04222-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/27ddd471bc5c/sensors-18-04222-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/39a3d2ba290d/sensors-18-04222-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/cf6ba7949c93/sensors-18-04222-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/62072d9b5d48/sensors-18-04222-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/8e6051718533/sensors-18-04222-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/3dd754fa0ce5/sensors-18-04222-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ef2/6308601/49f7c64356ea/sensors-18-04222-g008.jpg

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本文引用的文献

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MCMC-based particle filtering for tracking a variable number of interacting targets.基于马尔可夫链蒙特卡罗的粒子滤波用于跟踪可变数量的相互作用目标。
IEEE Trans Pattern Anal Mach Intell. 2005 Nov;27(11):1805-19. doi: 10.1109/TPAMI.2005.223.