Improved Particle Filtering Algorithm for Maneuvering Target Tracking

2012 ◽  
Author(s):  
Yingxue Jiao ◽  
Jianfang Shi
Keyword(s):  
Target Tracking ◽  
Particle Filtering ◽  
Maneuvering Target Tracking ◽  
Filtering Algorithm ◽  
Maneuvering Target

scholarly journals Maneuvering Target Tracking Algorithm Based on Interacting Multiple Models

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Gannan Yuan ◽  
Wei Zhu ◽  
Wei Wang ◽  
Bo Yin
Keyword(s):  
Target Tracking ◽  
Nonlinear Filtering ◽  
Information Matrix ◽  
Multiple Models ◽  
Quick Response ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Information Filter

Aiming at improving the accuracy and quick response of the filter in nonlinear maneuvering target tracking problems, the Interacting Multiple Models Cubature Information Filter (IMMCIF) is proposed. In IMMCIF, the Cubature Information Filter (CIF) is brought into Interacting Multiple Model (IMM), which can not only improve the accuracy but also enhance the quick response of the filter. CIF is a multisensor nonlinear filtering algorithm; it evaluates the information vector and information matrix rather than state vector and covariance, which can reduce the error of nonlinear filtering algorithm. IMM disposes all the models simultaneously through Markov Chain, which can enhance the quick response of the filter. Finally, the simulation results show that the proposed filter exhibits fast and smooth switching when disposing different maneuver models; it performs better than the IMMCKF and IMMUKF on tracking accuracy.

scholarly journals A Filtering Algorithm for Maneuvering Target Tracking Based on Smoothing Spline Fitting

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yunfeng Liu ◽  
Jidong Suo ◽  
Hamid Reza Karimi ◽  
Xiaoming Liu
Keyword(s):  
Target Tracking ◽  
Curve Fitting ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Spline Fitting ◽  
Interactive Multiple Model ◽  
Kalman Filter Algorithm ◽  
The Common

Maneuvering target tracking is a challenge. Target’s sudden speed or direction changing would make the common filtering tracker divergence. To improve the accuracy of maneuvering target tracking, we propose a tracking algorithm based on spline fitting. Curve fitting, based on historical point trace, reflects the mobility information. The innovation of this paper is assuming that there is no dynamic motion model, and prediction is only based on the curve fitting over the measured data. Monte Carlo simulation results show that, when sea targets are maneuvering, the proposed algorithm has better accuracy than the conventional Kalman filter algorithm and the interactive multiple model filtering algorithm, maintaining simple structure and small amount of storage.

Maneuvering Target Tracking Algorithm Based on Particle PHD Filtering

2014 ◽  
Vol 989-994 ◽  
pp. 2212-2215
Author(s):  
Song Gao ◽  
Chao Bo Chen ◽  
Qian Gong
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Tracking Accuracy ◽  
Maneuvering Target Tracking ◽  
Probability Hypothesis Density ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
The Status ◽  
Simulation Results

As for the problem of maneuvering target tracking in the clutter environment, this paper combines IMM with PHD and realizes it through approach of particle filter. This algorithm avoids the troublesome problem of data association, and takes advantage of probability hypothesis density (PHD) filter in tracking maneuvering targets and interacting multi-model (IMM) algorithm in the field of model switching effectively, in the clutter environment, the status of the targets can be estimated precisely and steadily. This paper compares the proposed filtering algorithm with the classical IMM algorithm in performance, and the simulation results show that, the improved filtering algorithm has good tracking performance and tracking accuracy.

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