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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Clark, Martin Vinter, Richard |
| Copyright Year | 2006 |
| Description | Author affiliation: EEE Dept., Imperial College London, London, SW7 2BT; member, Data and Information Defence Technology Centre. (Clark, Martin; Vinter, Richard) |
| Abstract | In this paper a new algorithm is proposed for tracking problems, in which the state evolves according to a linear difference equation and the measurement is a nonlinear function of a noise corrupted version of the state. The algorithm recursively generates Gaussian approximations of the conditional distribution of the target state given the current and past measurements. It differs from other `moment matching' algorithms, such as the extended Kalman filter and its refinements, because it is based on an exact calculation of the mean and covariance of the updated conditional distribution. A special case of the algorithm, applicable to bearings-only tracking problems, is called the shifted Rayleigh filter. Simulations indicate that the shifted Rayleigh filter can match the accuracy of high order particle filters while significantly reducing the computational burden, even in some scenarios where the extended Kalman filter gives poor estimates or fails altogether. It is expected that the new algorithms will offer similar advantages for other kinds of tracking algorithms, including those involving range-only measurements. |
| Starting Page | 108 |
| Ending Page | 112 |
| File Size | 920231 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424405794 |
| DOI | 10.1109/NSSPW.2006.4378831 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-09-13 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Additive noise Matched filters Target tracking Difference equations Signal processing algorithms Nonlinear filters Displacement measurement Particle filters Noise measurement Yttrium |
| Content Type | Text |
| Resource Type | Article |
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