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基于特征函数滤波的三种状态估计融合方法。

Three State Estimation Fusion Methods Based on the Characteristic Function Filtering.

机构信息

Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.

School of Automation, Guangdong University of Pertrochemical Technology, Maoming 525000, China.

出版信息

Sensors (Basel). 2021 Feb 19;21(4):1440. doi: 10.3390/s21041440.

DOI:10.3390/s21041440
PMID:33669528
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7922971/
Abstract

There are three state estimation fusion methods for a class of strong nonlinear measurement systems, based on the characteristic function filter, namely the centralized filter, parallel filter, and sequential filter. Under ideal communication conditions, the centralized filter can obtain the best state estimation accuracy, and the parallel filter can simplify centralized calculation complexity and improve feasibility; in addition, the performance of the sequential filter is very close to that of the centralized filter and far better than that of the parallel filter. However, the sequential filter can tolerate non-ideal conditions, such as delay and packet loss, and the first two filters cannot operate normally online for delay and will be invalid for packet loss. The performance of the three designed fusion filters is illustrated by three typical cases, which are all better than that of the most popular Extended Kalman Filter (EKF) performance.

摘要

针对一类强非线性测量系统，存在三种状态估计融合方法，基于特征函数滤波器，分别是集中式滤波器、并行滤波器和序贯滤波器。在理想通信条件下，集中式滤波器可以获得最佳的状态估计精度，而并行滤波器可以简化集中式计算复杂度并提高可行性；此外，序贯滤波器的性能非常接近集中式滤波器，远优于并行滤波器。然而，序贯滤波器可以容忍非理想条件，例如延迟和丢包，前两种滤波器在延迟情况下无法正常在线运行，并且在丢包情况下将失效。通过三个典型案例说明了三种设计的融合滤波器的性能，均优于最流行的扩展卡尔曼滤波器（EKF）的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75fc/7922971/1e8dadcb595d/sensors-21-01440-g016.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75fc/7922971/b5235c784964/sensors-21-01440-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75fc/7922971/c697e321f5dc/sensors-21-01440-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75fc/7922971/d9bb80162596/sensors-21-01440-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75fc/7922971/056db532f56b/sensors-21-01440-g014.jpg
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