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首页> 外文期刊>Mathematical Problems in Engineering >Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion
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Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

机译：基于多尺度无味卡尔曼滤波方法的自适应融合设计用于多传感器数据融合

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摘要

In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.
机译：为了提高测量数据的可靠性，多传感器数据融合技术在提高测量数据的准确性方面取得了很大的进步。本文利用无味卡尔曼滤波器（UKF）的实时，递归和最佳估计特性，以及多尺度小波变换分解在数据分析中的独特优势，可以有效地集成来自多个传感器的观测数据。通过将UKF与多尺度信号分析相结合，提出了一种新的基于多尺度UKF的多传感器数据融合算法。首先，将基于模型的UKF引入到多个传感器中，然后使用小波将模型在多个尺度上分解为粗尺度。接下来，使用来自相应传感器的降噪观测数据调整从细到粗的分解信号，并用小波重构以获得融合信号。最后，使用自适应加权融合算法对处理后的数据进行融合。仿真和实验结果比较表明，与单独使用数据融合算法相比，该方法可有效提高测量系统的抗干扰能力，并确保传感器测量系统的可靠性和准确性。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第23期|854085.1-854085.10|共10页
  • 作者

    Wang Huadong; Dong Shi;

  • 作者单位

    Zhoukou Normal Univ, Sch Comp Sci & Technol, Zhoukou 466001, Peoples R China;

    Zhoukou Normal Univ, Sch Comp Sci & Technol, Zhoukou 466001, Peoples R China|Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China;

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