IMM Algorithm Based on H∞ Filter for Maneuvering Target Tracking

被引：0

作者：

Liu, Meiqin ^{[1
,2
]}

Fan, Zhen ^{[2
]}

Wang, Xie ^{[2
]}

Zhang, Senlin ^{[2
]}

机构：

[1] Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China

[2] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China

来源：

PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) | 2017年

基金：

中国国家自然科学基金;

关键词：

State Estimation; Noise Statistics Estimate; H-infinity Filter; Interacting Multiple Model Algorithm; Target Tracking; NOISE; COVARIANCE; SYSTEMS; FUSION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Correct knowledge of noise statistics is essential for an effective estimator in maneuvering target tracking. In practice, however, the noise statistics are usually unknown or not perfectly known. To deal with the estimation problem in linear discrete-time systems with Markov jump parameters, where the measurement noise covariance is unknown, a novel approach is presented in this paper. This approach is based on the interacting multiple model (IMM) framework. An H-infinity filter is employed to construct a noise statistics estimator to obtain the information which is necessary for the IMM algorithm. In our approach, even the priori knowledge of noise statistics is not needed. The noise statistics loss problem is solved while the merits of IMM algorithm is reserved. The effectiveness of the proposed approach is demonstrated in comparison with single-model H-infinity filter through Monte Carlo simulation for maneuvering target tracking.

引用

页码：5385 / 5391

页数：7

共 23 条

[1] USE OF A KINEMATIC CONSTRAINT IN TRACKING CONSTANT-SPEED, MANEUVERING TARGETS [J].

ALOUANI, AT ;

BLAIR, WD .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1993, 38 (07) :1107-1111

[2]

[Anonymous], 2012, PROC 7 IEEE INT C IN

[3] A Robust Vision-Based Sensor Fusion Approach for Real-Time Pose Estimation [J].

Assa, Akbar ;

Janabi-Sharifi, Farrokh .

IEEE TRANSACTIONS ON CYBERNETICS, 2014, 44 (02) :217-227

[4]

Banavar RN., 1992, A Game Theoretic Approach to Linear Dynamic Estimation

[5] TRACKING A MANEUVERING TARGET USING INPUT ESTIMATION VERSUS THE INTERACTING MULTIPLE MODEL ALGORITHM [J].

BARSHALOM, Y ;

CHANG, KC ;

BLOM, HAP .

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 1989, 25 (02) :296-300

[6] Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter [J].

Bavdekar, Vinay A. ;

Deshpande, Anjali P. ;

Patwardhan, Sachin C. .

JOURNAL OF PROCESS CONTROL, 2011, 21 (04) :585-601

[7]

Blom H. A. P., 1984, Proceedings of the 23rd IEEE Conference on Decision and Control (Cat. No. 84CH2093-3), P656

[8] THE INTERACTING MULTIPLE MODEL ALGORITHM FOR SYSTEMS WITH MARKOVIAN SWITCHING COEFFICIENTS [J].

BLOM, HAP ;

BARSHALOM, Y .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1988, 33 (08) :780-783

[9] Robust estimation with unknown noise statistics [J].

Durovic, ZM ;

Kovacevic, BD .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1999, 44 (06) :1292-1296

[10] Kalman Filter With Recursive Covariance Estimation-Sequentially Estimating Process Noise Covariance [J].

Feng, Bo ;

Fu, Mengyin ;

Ma, Hongbin ;

Xia, Yuanqing ;

Wang, Bo .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2014, 61 (11) :6253-6263

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