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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Guohui Li Hong Yang |
| Copyright Year | 2010 |
| Description | Author affiliation: School of Electronic Engineering, Xi'an University of Post and Telecommunications, 710061, China (Guohui Li; Hong Yang) |
| Abstract | In order to improve tracking estimation accuracy of square-root unscented Kalman particle filter (SRUKFPF), a new particle filter algorithm of update SRUKF based on iterated measurements is proposed. The algorithm produces the important density function of particle filter using maximum posteriori estimate of iterated square-root unscented Kalman filter, and amends the state covariance using Levenberg-Marquardt method, so that the observed information of particle is effectively used. This is more consistent with the posterior probability distribution of true state. Simulation results show that estimation performance of the proposed algorithm is much better than standard particle filter (PF), unscented particle filter (UPF) and square root unscented Kalman particle filter (SRUKFPF). |
| Starting Page | 222 |
| Ending Page | 225 |
| File Size | 96100 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424488834 |
| e-ISBN | 9781424488865 |
| DOI | 10.1109/YCICT.2010.5713085 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-11-28 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Target tracking Accuracy Estimation Filtering algorithms Density functional theory Particle filters Kalman filters Levenberg-Marquardt method Nonlinear estimation Particle filter |
| Content Type | Text |
| Resource Type | Article |
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