Maneuvering Target Tracking Using the Optimal Stochastic Jump Filtering Algorithm

2007 ◽  
Author(s):  
Hong-qiang Wang ◽  
Yang-Wang Fang ◽  
You-Li Wu ◽  
Xiao-Bin Zhou ◽  
Xian-Wei Zeng
Keyword(s):  
Target Tracking ◽  
Maneuvering Target Tracking ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Stochastic Jump

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.

Parallel Filtering Algorithm in Three Dimensions for IMM Maneuvering Target Tracking

2014 ◽  
Vol 651-653 ◽  
pp. 2362-2367 ◽  
Author(s):  
Xi Tao Zhang ◽  
An Qing Zhang
Keyword(s):  
Computational Complexity ◽  
Target Tracking ◽  
Three Dimensions ◽  
Maneuvering Target Tracking ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Model Set ◽  
Simulation Results

According to the physical truths those are the complexity of special target maneuvering and the inconformity of maneuvering degrees in three dimensions, the problems of model mismatching and inaccuracy in traditional IMM were analysed, then a parallel filtering algorithm in three dimensions for IMM maneuvering target tracking is presented. The model set of this algorithm consists of the CV and the modified CS model, which can adaptively tracking target under different maneuvering levels; the parallel IMMs in three dimensions can update model probabilities respectively according its maneuvering reality,which ensures the accuracy of model probabilities. The simulation results indicate that the proposed algorithm gets higher tracking precision and decrease 1/3 computational complexity than traditional IMM. That is to say, it has a good practical prospect in maneuvering target tracking in space.

Algorithm for Maneuvering Target Tracking Based on MATLAB

2013 ◽  
Vol 427-429 ◽  
pp. 1585-1588
Author(s):  
Jiang Ming Kuang ◽  
Shuang Zhang
Keyword(s):  
Nonlinear System ◽  
Target Tracking ◽  
Kalman Filtering ◽  
Tracking Error ◽  
National Defense ◽  
Extended Kalman Filtering ◽  
Maneuvering Target Tracking ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Stable Performance

the theory for maneuvering target tracking is significant to national defense and civil application. The filtering algorithm is one of important components in maneuvering target tracking. After the model of the maneuvering target is built, state vectors in the model are forecast and estimated through relevant filtering algorithms. The Unscented Kalman filtering is a novel filtering algorithm specially used for the nonlinear system, which is characterized by easy implementation, good generality, stable performance and so forth. Compared with the traditional Extended Kalman Filtering algorithm, the filtering algorithm can achieve less tracking error and higher tracking precision.

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