Maneuvering Target Tracking Algorithm Based on Improved "Self-Adaptive Turning Rate" Model

2013 ◽  
Vol 278-280 ◽  
pp. 1682-1686
Author(s):  
Xiu Ling He ◽  
Jing Song Yang
Keyword(s):  
Target Tracking ◽  
Rate Model ◽  
Simulation Test ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Modeling Methods ◽  
Actual Application ◽  
Model Set ◽  
Tracking Model ◽  
Self Adaptive

Multi-model (interactive multi-model )tracking is an effective method for maneuvering target tracking, and turning model is widely studied and applied in multi-model tracking modeling and model set establishment. A detailed study is carried out on the self-adaptive turning model modeling method for maneuvering target tracking. Also a summary is made about the method of establishing tracking model by calculating the turning rate. The shortcomings of literature [4、6]'s self-adaptive turning model in actual application is also pointed out, with two modeling methods adopting average turning rate presented accordingly. The simulation test proves the necessity of improvement and the validity of the new model.

Switching Multisensor Improved Current Statistical Model for Maneuvering Target Tracking

2013 ◽  
Vol 385-386 ◽  
pp. 585-588
Author(s):  
Qi Fang He ◽  
Yan Bin Li ◽  
Hang Lv ◽  
Guang Jun He
Keyword(s):  
Statistical Model ◽  
Target Tracking ◽  
Tracking System ◽  
Sensor Management ◽  
Maneuvering Target Tracking ◽  
System Parameters ◽  
Maneuvering Target ◽  
Tracking Model ◽  
Transient Error ◽  
Random Change

For an actual maneuvering target tracking system, the random change of system parameters and structure can often occur. So the whole system is dynamic, and there is uncertainty in system parameters and structure. In the paper, theory of system with random changing structures and technology of sensor management is introduced to build a tracking model with random changing structures. For the considering of random changing, the transient error brought by structure random changing is overcome.

A Research on the Weight of Interactive Multiple Model in Maneuvering Target Tracking

2014 ◽  
Vol 568-570 ◽  
pp. 1008-1011
Author(s):  
Ming Yong Liu ◽  
Yang Li ◽  
Xiao Jian Zhang
Keyword(s):  
Target Tracking ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Target Model ◽  
Maneuvering Target ◽  
Interactive Multiple Model ◽  
Model Set ◽  
Change Mechanisms ◽  
Model Weight

The establishment of the target model is the key of maneuvering target tracking. The previous research on interactive multiple model, which is applied on tracking extensively, focused on the design of the model set and fusion with other algorithms, while there is less study on change mechanisms of the model weight. In light of this, the impetus behind this paper is to do some analysis which based on the model weight of different trajectories, reveal the change rule. Finally, the validity of the proposed approach is demonstrated by simulation.

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.

scholarly journals FEATURE AIDED SWITCHING MODEL SET APPROACH FOR MANEUVERING TARGET TRACKING

2012 ◽  
Vol 45 ◽  
pp. 251-268 ◽  
Author(s):  
Jianpeng Fan ◽  
Yilong Zhu ◽  
Shijie Fan ◽  
Hongqi Fan ◽  
Qiang Fu
Keyword(s):  
Target Tracking ◽  
Maneuvering Target Tracking ◽  
Switching Model ◽  
Maneuvering Target ◽  
Model Set

Turn Rate Estimation Based Adaptive IMM Algorithm for Maneuvering Target Tracking

2011 ◽  
Vol 383-390 ◽  
pp. 5609-5614
Author(s):  
Ye Tian ◽  
Hong Jiang ◽  
Quan Xin Ding ◽  
Guo Wei Liang
Keyword(s):  
Target Tracking ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Rate Estimation ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
Acceptable Model ◽  
Interactive Multiple Model ◽  
Model Set ◽  
Turn Rate

A turn rate estimation based adaptive interactive multiple model algorithm is put forward to solve model-set mismatch problem of target tracking algorithm applying to high maneuvering target. By considering both the estimation and the estimated variance of target’s turn rate, model-set is selected according to a rule based on the coefficient of variance of turn rate estimation. When turn rate estimation is acceptable, model-set is constructed according to turn rate estimation to reduce competition among models. When turn rate estimation is unacceptable, standard IMM algorithm model-set is applied to increase coverage of model-set. Simulation shows this algorithm improves tracking performance especially for high maneuvering targets.

Curve Model of Adaptive Interaction Model Algorithm Tracking Method

2015 ◽  
Vol 738-739 ◽  
pp. 344-349
Author(s):  
Xiu Ling He ◽  
Jing Song Yang
Keyword(s):  
Target Tracking ◽  
Estimation Method ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Adaptive Kalman Filter ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Curve Model ◽  
Tracking Model ◽  
Model Algorithm

Firstly, through the principle analysis and simulation experiment, the maneuvering target tracking algorithm of curve model interacting multiple model tracking algorithm was given. Because the algorithm is simple structure and high cost efficiency, it becomes generally applicable algorithm for curve tracking model. But, the target mobility is very high in practice, Single target tracking model is no longer applicable curve tracking model. To improve the accuracy of tracking, the adaptive grid interacting multiple model (AGIMM) algorithm was given. The algorithm has two fatal weaknesses in the practical application. First, the process of maneuvering target tracking, when the model changes and gradient, the tracking precision is not high ;Second, because the changing model structure is very large model sets, the algorithm is complexity and system processing speed is very slow, which cannot be widely used. In order to improve the algorithm and its scope of application, The paper proposed the adaptive Kalman filter adaptive interacting multiple model algorithm (AKFAIMM).The algorithm introduced the parameter in the adaptive Kalman filter, and adjusted parameter in maneuvering target tracking, the parameter was adjusted Continuously in the curve motion model, it could greatly improve the tracking precision and the application of the model. Second, to improve the algorithm complexity. The paper improved on turning curve. The angular velocity estimation method replaced centripetal acceleration estimation method. The estimation method reduced the number of model set and reduced greatly of computation.

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