Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

Huai, Liangliang; Li, Bo; Yun, Peng; Song, Chao; Wang, Jiayuan

doi:10.3390/rs15184513

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…The classical interacting multiple model algorithm consists of the following four main parts: (1) Model interaction; (2) The parallel filtering of models; (3) The updating of model probability; and (4) Model estimation fusion [14]. The IMM algorithm assumes that the true motion model of the target is obtained by summing the individual model sets of the respective weights they occupy [15][16][17].…”

Section: Review Of the Classical Imm Algorithmmentioning

confidence: 99%

An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance

Xu,

Xiao,

et al. 2024

Remote Sensing

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A pulse-Doppler (PD) radar has the advantage of strong anti-interference ability, and it is often used as a solution for maneuvering target tracking. In the application of target monitoring and tracking in PD radars, the interacting multiple model algorithm (IMM) has become the main and preferred choice due to its flexibility and high accuracy. However, the probability transfer matrix in classical IMM algorithms generally depends on constant prior knowledge, and if a PD radar is tracking a strong maneuvering target, it is inevitable to encounter some limitations, such as the possibility of target tracking trajectory deviation, and even a loss of the target. The Markov probability transfer matrix is proposed with an adaptive modification ability in real time to overcome the above problems in this paper. Additionally, for improving the speed of switching between the models, the fuzzy control system for secondary updating of model probability is adopted. By this means, the tracking accuracy of maneuvering targets is enhanced. Compared with the classical IMM algorithm, the corresponding simulation results for the PD radar indicate that the overall tracking accuracy of the proposed adaptive IMM algorithm is improved by 19.6%. In conclusion, the continuity and accuracy of the target trajectory can be effectively improved with the proposed adaptive IMM algorithm in PD radar cases.

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Section: Review Of the Classical Imm Algorithmmentioning

confidence: 99%

An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance

Xu,

Xiao,

et al. 2024

Remote Sensing

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UAV Tracking Moving Target Mission Planning Based on TW-SAC Algorithm

Song,

Cui,

Luo

et al. 2024

2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)

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Aircraft Flight Autonomous Decision-Making Method Based on Target Predicted Trajectory and Markov Decision Process

Zhou,

Tang,

Ren

2024

Actuators

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In this paper, in order to enhance the autonomous operation capabilities of aircraft and ensure their operational safety and efficiency, we propose an autonomous decision-making framework based on target motion state prediction combined with the Markov Decision Process, namely IMM-MDP architecture. Firstly, our own aircraft utilizes the IMM algorithm to achieve the state prediction of the target; building upon the existing algorithmic structure, the proposed framework improves model sets within the IMM algorithm by incorporating climb and turn models and refines the motion modes of aircraft during the cruise phase, gathering corresponding sub-models to predict different motion modes and enhance the prediction accuracy of the target. Secondly, we adopt the Markov Decision Process as the autonomous decision-making method for the own aircraft, proposing a method by which to calculate the optimal decision sequence based on the prediction scenarios of the IMM algorithm; that is, in both multi-step prediction and single-step prediction scenarios, the payoff values of different action strategies at each decision moment are calculated to obtain the optimal decision sequence. The experimental results show that the IMM algorithm with the improved model set, described in this paper, is more accurate than the IMM algorithm and prediction results of the current statistical model, as described in the literature. We also set up a multi-aircraft operation scenario, comparing the IMM-MDP decision framework proposed in this paper with the Monte Carlo and MPC decision models, thus demonstrating that the proposed framework provides better decisions while ensuring safety. Due to the target state prediction and updating, this method also demonstrates better real-time performance and practicality.

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Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

Cited by 4 publications

References 28 publications

An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance

An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance

UAV Tracking Moving Target Mission Planning Based on TW-SAC Algorithm

Aircraft Flight Autonomous Decision-Making Method Based on Target Predicted Trajectory and Markov Decision Process

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