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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chenjian Ran Zili Deng |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Automation, Heilongjiang University, Harbin 150080, China (Chenjian Ran; Zili Deng) |
| Abstract | For multisensor multi-channel autoregressive moving average(ARMA) signal with white measurement noises and a common disturbance measurement noise, when the model parameters and the noise variances are all unknown, an information fusion multi-stage identification method is presented. It consists of three stages: In the first stage, the local and fused estimates of the autoregressive(AR) parameters are obtained by the multiple recursive instrumental variable (RIV) algorithm. In the second stage, based on the sampled cross-correlation functions, the local and fused measurement noises variances estimates are obtained. In the third stage, applying the Gevers-Wouters algorithm with a dead band, the local and fused estimates of the moving average(MA) parameters and process noise variances are obtained. The fused estimates are obtained by taking the average of the corresponding local estimates. The consistency of the fused estimates is proved. One simulation example is given to verify the consistency of the estimates. |
| Starting Page | 2216 |
| Ending Page | 2221 |
| File Size | 399172 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424481132 |
| ISSN | 21527431 |
| e-ISBN | 9781424481156 |
| DOI | 10.1109/ICMA.2011.5986325 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-08-07 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Noise Noise measurement Autoregressive processes Correlation Sensors Accuracy Estimation consistency Multisensor information fusion multi-channel ARMA signal identification parameter estimation noise variance estimation |
| Content Type | Text |
| Resource Type | Article |
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