scholarly journals A Bayesian framework with an auxiliary particle filter for GMTI-based ground vehicle tracking aided by domain knowledge

2014 ◽  
Author(s):  
Miao Yu ◽  
Cunjia Liu ◽  
Wen-hua Chen ◽  
Jonathon Chambers
Keyword(s):  
Particle Filter ◽  
Domain Knowledge ◽  
Bayesian Framework ◽  
Vehicle Tracking ◽  
Ground Vehicle ◽  
Auxiliary Particle ◽  
Auxiliary Particle Filter

scholarly journals Ant-Mutated Immune Particle Filter Design for Terrain Referenced Navigation with Interferometric Radar Altimeter

2021 ◽  
Vol 13 (11) ◽  
pp. 2189
Author(s):  
Suktae Kang ◽  
Myeong-Jong Yu
Keyword(s):  
Immune System ◽  
Particle Filter ◽  
Measurement Errors ◽  
Filter Design ◽  
Sar Interferometry ◽  
Low Grade ◽  
Radar Altimeter ◽  
Terrain Referenced Navigation ◽  
Auxiliary Particle ◽  
Auxiliary Particle Filter

This study aims to design a robust particle filter using artificial intelligence algorithms to enhance estimation performance using a low-grade interferometric radar altimeter (IRA). Based on the synthetic aperture radar (SAR) interferometry technology, the IRA can extract three-dimensional ground coordinates with at least two antennas. However, some IRA uncertainties caused by geometric factors and IRA-inherent measurement errors have proven to be difficult to eliminate by signal processing. These uncertainties contaminate IRA outputs, crucially impacting the navigation performance of low-grade IRA sensors in particular. To deal with such uncertainties, an ant-mutated immune particle filter (AMIPF) is proposed. The proposed filter combines the ant colony optimization (ACO) algorithm with the immune auxiliary particle filter (IAPF) to bring individual mutation intensity. The immune system indicates the stochastic parameters of the ACO, which conducts the mutation process in one step for the purpose of computational efficiency. The ant mutation then moves particles into the most desirable position using parameters from the immune system to obtain optimal particle diversity. To verify the performance of the proposed filter, a terrain referenced navigation (TRN) simulation was conducted on an unmanned aerial vehicle (UAV). The Monte Carlo simulation results show that the proposed filter is not only more computationally efficient than the IAPF but also outperforms both the IAPF and the auxiliary particle filter (APF) in navigation performance and robustness.

Adaptive Layered-sampling Auxiliary Particle Filter′s Research and Application in Video Tracking

2010 ◽  
Vol 39 (3) ◽  
pp. 571-576
Author(s):  
邹卫军 ZOU Wei-jun ◽  
龚翔 GONG Xiang ◽  
薄煜明 BO Yu-ming
Keyword(s):  
Particle Filter ◽  
Video Tracking ◽  
Auxiliary Particle ◽  
Auxiliary Particle Filter

scholarly journals The Iterated Auxiliary Particle Filter

2017 ◽  
Vol 112 (520) ◽  
pp. 1636-1647 ◽  
Author(s):  
Pieralberto Guarniero ◽  
Adam M. Johansen ◽  
Anthony Lee
Keyword(s):  
Particle Filter ◽  
Auxiliary Particle ◽  
Auxiliary Particle Filter
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