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A Novel Adaptive Noise Covariance Matrix Estimation and Filtering Method: Application to Multiobject Tracking

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成果类型:
期刊论文
作者:
Jiang, Chao;Wang, Zhiling;Liang, Huawei;Wang, Yajun
通讯作者:
Jiang, C
作者机构:
[Jiang, Chao; Jiang, C] Univ South China, Hengyang 421001, Peoples R China.
[Jiang, Chao; Jiang, C] Hefei Inst Phys Sci, Chinese Acad Sci, Hefei 230031, Peoples R China.
[Jiang, Chao; Jiang, C] Univ Sci & Technol China, Hefei 230026, Peoples R China.
[Liang, Huawei; Wang, Zhiling] Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China.
[Liang, Huawei; Wang, Zhiling] Anhui Engn Lab Intelligent Driving Technol & Appl, Hefei 230031, Peoples R China.
通讯机构:
[Jiang, C ] U
Univ South China, Hengyang 421001, Peoples R China.
Hefei Inst Phys Sci, Chinese Acad Sci, Hefei 230031, Peoples R China.
Univ Sci & Technol China, Hefei 230026, Peoples R China.
语种:
英文
关键词:
adaptive estimation;Calibration;Correlation;Covariance matrices;Estimation;Filtering;Kalman filtering;multiobject tracking;Noise measurement;process and measurement noise covariance matrices;Technological innovation
期刊:
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
ISSN:
2379-8858
年:
2024
卷:
9
期:
1
页码:
626-641
基金类别:
National Key Research and Development Program of China (Grant Number: 2020AAA0108103) Key Science and Technology Project of Anhui (Grant Number: 202103a05020007)
机构署名:
本校为第一且通讯机构
院系归属:
电气工程学院
摘要:
This article presents a novel online adaptive method for estimating the process and measurement noise covariance matrices in Kalman filters (KFs) to address the challenge of varying noise characteristics in practical applications. Specifically, the proposed method decomposes the noise covariance matrix into an element distribution matrix and a noise intensity and employs an improved Sage filter to estimate the element distribution matrix. Additionally, a calibration and correction method is introduced to accurately determine and adaptively correct the online bias of the noise intensity. The un...

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