scholarly journals A Novel Stastical Particle Filtering Approach for Non-Linear and Non-Gaussian System Identification

2012 ◽  
Vol 60 (6) ◽  
pp. 53-58
Author(s):  
Dhiraj K.Jha ◽  
Abhijit Verma ◽  
Avinash Kumar ◽  
Prabhat Panda
Keyword(s):  
System Identification ◽  
Particle Filtering ◽  
Non Linear ◽  
Gaussian System ◽  
Non Gaussian ◽  
Filtering Approach

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.

scholarly journals Detection of MDPSK in Fading Channels using Gaussian Particle Filter

2014 ◽  
pp. 76-79
Author(s):  
P. Sudhakar ◽  
P. Gopi Krishna ◽  
Dr. D. Elizabeth Rani
Keyword(s):  
Particle Filter ◽  
Fading Channels ◽  
Posterior Distribution ◽  
Particle Filtering ◽  
Sampling Process ◽  
Non Linear ◽  
Linear Problems ◽  
Filtering Approach

We propose Gaussian particle filtering approach for solving the problem of corrupting the MDPSK signals by fading as well as noise. Particle filtering is a powerful tool for non linear problems but it faces sampling degeneration problem which leads to re‐sampling process. Gaussian Particle Filtering doesn’t need re‐sampling process because it approximates the posterior distribution as Gaussian.GPF is preferable than PF for fading channels.

Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

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