Efficient estimation of system states of a poorly modeled 2-D target tracking system using evolutionary strategy based particle filter algorithm

2016 ◽  
Author(s):  
Suvendu Chattaraj ◽  
Abhik Mukherjee
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Tracking System ◽  
Efficient Estimation ◽  
Evolutionary Strategy ◽  
System States ◽  
Particle Filter Algorithm

The Particle Filter Algorithm Research for Target Tracking Based on Information Fusion

2012 ◽  
Vol 628 ◽  
pp. 440-444 ◽  
Author(s):  
Juan Li ◽  
Hui Juan Hao ◽  
Mao Li Wang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Information Fusion ◽  
Particle Filtering ◽  
Tracking System ◽  
Distributed Sensor ◽  
Multi Target Tracking ◽  
Particle Filter Algorithm ◽  
Gaussian System ◽  
Non Gaussian

This paper researches the particle filters Algorithms for target tracking based on Information Fusion, it combines the traditional Kalman filter with the particle filter. For multi-sensor and multi-target tracking system with complex application background, which is nonlinear and non-gaussian system, the paper proposes an effective particle filtering algorithm based on information fusion for distributed sensor, this algorithm contributes to the solution of particle degradation problems and the phenomenon of particle lack, and achieve high precision for target tracking.

Target Tracking Based on Optimized Particle Filter Algorithm

2013 ◽  
Vol 8 (5) ◽  
Author(s):  
Junying Meng ◽  
Jiaomin Liu ◽  
Juan Wang ◽  
Ming Han
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Particle Filter Algorithm

Multi-Target Tracking Based on Optimized Particle Filter Algorithm

2012 ◽  
Vol 11 (1) ◽  
pp. 630-633
Author(s):  
Junying Meng ◽  
Jiaomin Liu ◽  
Juan Wang ◽  
Ming Han
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Multi Target Tracking ◽  
Particle Filter Algorithm

scholarly journals Improved Interacting Multiple Model Particle Filter Algorithm

2018 ◽  
Vol 36 (1) ◽  
pp. 169-175 ◽  
Author(s):  
Qiaoran Liu ◽  
Xun Yang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Proposal Distribution ◽  
Model Particle ◽  
Interactive Multiple Model ◽  
Particle Filter Algorithm

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

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