Data association and passive tracking for multiple targets based on Gaussian sum particle filter

2006 ◽  
Author(s):  
Feng Xue ◽  
Zhong Liu ◽  
Zhangsong Shi
Keyword(s):  
Particle Filter ◽  
Data Association ◽  
Multiple Targets ◽  
Gaussian Sum ◽  
Passive Tracking

scholarly journals Underwater terrain-aided navigation method based on improved Gaussian sum particle filtering

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141882157
Author(s):  
Pengyun Chen ◽  
Jianlong Chang ◽  
Yujie Han ◽  
Meini Yuan
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Particle Filter ◽  
Basis Function ◽  
Particle Filtering ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Gaussian Sum ◽  
Matching Performance ◽  
Navigation Method

To solve the nonlinear Bayesian estimation problem in underwater terrain-aided navigation, a terrain-aided navigation method based on improved Gaussian sum particle filter is proposed. This method approximates the Bayesian function using multiple Gaussian components, and the components can be obtained by radial basis function neural network. This method has no resampling process, the particle depletion of particle filtering is eliminated in principle. The simulation shows that the proposed method has good matching performance, which is suitable for autonomous underwater vehicle navigation.

New Method of Moving Targets Passive Tracking by Single Moving Observer Based on Measurement Data Fusion

2012 ◽  
Vol 239-240 ◽  
pp. 942-945
Author(s):  
Jie Gui Wang
Keyword(s):  
Data Fusion ◽  
Computer Simulations ◽  
Data Association ◽  
Measurement Data ◽  
Synthetic Data ◽  
Difficult Problem ◽  
Moving Targets ◽  
Passive Tracking ◽  
Multiple Measurements ◽  
New Location

Moving targets passive tracking by single moving observer is a difficult problem. A new location method based on measurement data fusion is proposed in this paper. Firstly, the adaptive passive tracking initiation algorithm is introduced. Secondly, a new data association algorithm is proposed, based on the data fusion of multiple measurements, the decision of synthetic data association is made. Finally, with the help of computer simulations, the proposed algorithms are proven to be correct and effective.

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