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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nebelecky, C.K. Crassidis, J.L. Singla, P. |
| Copyright Year | 2014 |
| Description | Author affiliation: Res. Scientist, Inf. Fusion Group, CUBRC, Inc., Buffalo, NY, USA (Nebelecky, C.K.) || Dept. of Mech. & Aero. Eng., SUNY - Univ. at Buffalo, Amherst, NY, USA (Crassidis, J.L.; Singla, P.) |
| Abstract | This paper presents a new form of the multiple model adaptive estimation algorithm for improved state tracking in systems with unknown system models. The proposed approach differs from existing multiple model methods in the manner in which the covariance and Kalman gains of the individual filters are calculated. By using the fused model estimate, recursions for the actual estimation error covariances are derived which account for the deviation of the hypothesized model from the fused model. Using these covariances to determine the Kalman gain leads to improved tracking estimates through fusion of model and measurement uncertainty. The proposed algorithm has been compared against the standard multiple model adaptive estimation and interacting multiple model algorithms in two simulated examples, resulting in improved, and comparable tracking performance, respectively. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 762751 |
| Page Count | 8 |
| File Format | |
| ISBN | 9788490123553 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-07-07 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | International Society of Information Fusion |
| Subject Keyword | Kalman filters Adaptation models Estimation error Noise Computational modeling Target tracking Mathematical model target tracking Multiple-model adaptive estimation filtering |
| Content Type | Text |
| Resource Type | Article |
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