Detecting or estimating the state of the moving target accurately on the road helps predict the behavior of the moving target, and this is beneficial for self-vehicles’ trajectory planning and safe navigation. The difficulty of state estimation lies in how to estimate the state of the target in real-time and accurately, and it will not be divergence when the sensor has missed detection, false detection and time-varying measurement noise. In response to this difficulty, it is proposed to use multi-source information fusion and Kalman filter to make up for the missed detection of the sensor, use the nearest neighbor correlation algorithm to match the detection and prediction to reduce the impact of false detection, and use the adaptive measurement noise estimation method to estimate the current value of the measurement noise in real-time to improve the stability and accuracy of the filter when the measurement noise is time-varying. The proposed method relies on the smart car platform for experiments. The experimental results show that the position and velocity estimation accuracy of the proposed algorithm are all better than the comparison method.


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    Title :

    Motion State Estimation Based on Multi-sensor Fusion and Noise Covariance Estimation


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Jiang, Chao (author) / Wang, Zhiling (author) / Liang, Huawei (author) / Zhang, Shijing (author) / Tan, Shuhang (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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