IMM-UKF based airborne radar and ESM data fusion for target tracking

Zuo, Yani; Wu, Falin; Liu, Yushuang; Zhang, Zhidong; Zhang, Yachong

doi:10.1109/icemi46757.2019.9101826

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Then, the adaptive model matching for the maneuvering target will be done to improve the measurement accuracy of the target. The domestic and overseas scholars combine the IMM algorithm with the Nonlinear Kalman filter [7][8][9][10][11] and the particle filter [12][13] so as to boost the accuracy for state observation by improving the filter. By combining the maneuvering characteristics of aerial vehicle, the paper utilizes the update information for the mismatched model error compression ratio to adjust the Markov state transition matrix so as to swiftly switch the models.…”

Section: Introductionmentioning

confidence: 99%

Study on the trajectory measurement filter algorithm for an adaptive IMM aerial vehicle

Zheng²,

Zhao³

2023

International Conference on Precision Instruments and Optical Engineering (PIOE 2022)

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A motion state model set for maneuvering target has been established to solve the problem of the increased trajectory measurement error in the maneuvering process of aerial vehicle. The low accuracy of filter caused by the incomplete description of motion state for the single model has been solved by utilizing the interacting multiple model (IMM) algorithm. The adaptive factor has been introduced to the Unscented Kalman Filter (UFK) to resolve the inaccurate observation error prior by adjusting the process noise covariance matrix in real time. The adaptive probability conversion factor based on compression ratio has been added to the IMM algorithm, enhancing its convergence rate. At last, a new AIMM-AUKF trajectory measurement filter algorithm has been formed so that the high accuracy measurement for the trajectory of random maneuvering target has been achieved and the validity the proposed algorithm has been verified through simulation.

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Section: Introductionmentioning

confidence: 99%

Study on the trajectory measurement filter algorithm for an adaptive IMM aerial vehicle

Zheng²,

Zhao³

2023

International Conference on Precision Instruments and Optical Engineering (PIOE 2022)

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The Unscented Kalman Filter Based Fusion Tracking Method with Multi-error Model

Zhang

Gao

Sun

2021

2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

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Data Sensor Fusion for Surveillance Applications: Evaluation of Extended Kalman Filter vs. Unscented Kalman Filter

M. Sogamoso,

A. Fernández,

J. Suarez

2024

Applications and Optimizations of Kalman Filter and Their Variants

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This chapter introduces the foundations of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for data sensor fusion applications in surveillance applications. After explaining how to model the drone under the constant turn rate and velocity (CTRV) dynamics, the EKF and UKF techniques for Lidar and Radar data sensor fusion are applied to enable better 3D object detection and reconstruction from point cloud data is evaluated under a performance comparison. Through root mean square error (RMSE) and mean square error (MSE) the filters are designed to meet the specifications of the model based on the position of the gathered points (X and Y axis) in the space state model. By degrading the measurements with Gaussian and Rayleigh noise, the results evaluate and compare both techniques to show the tolerance and performance in terms of stability, scalability, and application to drone monitoring in surveillance activities.

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IMM-UKF based airborne radar and ESM data fusion for target tracking

Cited by 3 publications

References 8 publications

Study on the trajectory measurement filter algorithm for an adaptive IMM aerial vehicle

Study on the trajectory measurement filter algorithm for an adaptive IMM aerial vehicle

The Unscented Kalman Filter Based Fusion Tracking Method with Multi-error Model

Data Sensor Fusion for Surveillance Applications: Evaluation of Extended Kalman Filter vs. Unscented Kalman Filter

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