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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hao Gang Jia Wenjing Deng Zili |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Automatica, Heilongjiang Univ., Harbin, China (Hao Gang; Jia Wenjing; Deng Zili) |
| Abstract | For the multisensor system with unknown noise statistics, and with the measurement matrices having the same factor, based on the weighted least squares (WLS) method, a weighted fusion measurement equation is obtained, and it together with the state equation to constitute a equivalent weighted measurement fusion system. Based on the on-line identification of the moving average (MA) innovation model parameters for weighted measurement fusion system, using the modern time series analysis method, a self-tuning weighted measurement fusion Kalman filter is presented. It is proved that it converges to globally optimal measurement fusion Kalman filter with known noise statistics, so that it has asymptotic global optimality. A simulation example for a tracking system with 4 sensors shows its effectiveness. |
| Sponsorship | IEEE Control Syst. Soc. SICE, ICASE, Harbin Inst. of Technol |
| Starting Page | 393 |
| Ending Page | 398 |
| File Size | 5287559 |
| Page Count | 6 |
| File Format | |
| ISBN | 7810778021 |
| DOI | 10.1109/CHICC.2006.280580 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-08-07 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | TECH COMMITTEE ON CONTROL THEORY |
| Subject Keyword | Weight measurement Technological innovation Time series analysis convergence Time measurement Sensor systems Noise measurement noise variance estimation asymptotic global optimality Statistics Equations Multisensor systems Least squares methods self-tuning Kalman filter weighted measurement fusion identification multisensor modern time series analysis method |
| Content Type | Text |
| Resource Type | Article |
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