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

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.

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Improved interactive multiple model filter for maneuvering target tracking

Proceedings of the 33rd Chinese Control Conference ◽

10.1109/chicc.2014.6896212 ◽

2014 ◽

Cited By ~ 2

Author(s):

Bo Li ◽

Fuwen Pang ◽

Ce Liang ◽

Xiaohong Chen ◽

Yunfeng Liu

Keyword(s):

Target Tracking ◽

Multiple Model ◽

Maneuvering Target Tracking ◽

Maneuvering Target ◽

Model Filter ◽

Interactive Multiple Model

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Maneuvering Target Tracking Algorithm Based on Interacting Multiple Models

Mathematical Problems in Engineering ◽

10.1155/2015/810613 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 3

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.

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Maneuvering target tracking with improved interactive multiple model algorithm

Proceedings of International Conference on Artificial Life and Robotics ◽

10.5954/icarob.2021.os6-5 ◽

2021 ◽

Vol 26 ◽

pp. 373-376

Author(s):

Meiyu Zhu ◽

Guanyu Chen ◽

Weicun Zhang

Keyword(s):

Target Tracking ◽

Multiple Model ◽

Maneuvering Target Tracking ◽

Maneuvering Target ◽

Interactive Multiple Model ◽

Model Algorithm

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.568-570.1008 ◽

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.

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Turn Rate Estimation Based Adaptive IMM Algorithm for Maneuvering Target Tracking

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.5609 ◽

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.

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