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首页> 外文期刊>International Journal of Adaptive Control and Signal Processing >Self-tuning fusion Kalman filter weighted by scalars and its convergence analysis for multi-channel autoregressive moving average signals
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Self-tuning fusion Kalman filter weighted by scalars and its convergence analysis for multi-channel autoregressive moving average signals

机译：标量加权自整定融合卡尔曼滤波器及其对多通道自回归移动平均信号的收敛分析

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摘要

For the multi-sensor multi-channel autoregressive (AR) moving average signals with white measurement noises and an AR-colored measurement noise, a multi-stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances are obtained by the multidimensional recursive instrumental variable algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. They have the strong consistency. Substituting them into the optimal information fusion Kalman filter weighted by scalars, a self-tuning fusion Kalman filter for multi-channel AR moving average signals is presented. Applying the dynamic error system analysis method, it is proved that the proposed self-tuning fusion Kalman filter converges to the optimal fusion Kalman filter in a realization, so that it has asymptotic optimality. A simulation example for a target tracking system with three sensors shows its effectiveness. Copyright (c) 2014 John Wiley & Sons, Ltd.
机译：对于具有白色测量噪声和AR彩色测量噪声的多传感器多通道自回归（AR）移动平均信号，提出了一种在模型参数和噪声方差部分未知的情况下的多阶段信息融合识别方法。通过多维递归工具变量算法和相关方法获得模型参数和噪声方差的局部估计量，并通过取局部估计量的平均值获得融合估计量。它们具有很强的一致性。将它们代入标量加权的最优信息融合卡尔曼滤波器，提出了一种用于多通道AR移动平均信号的自调谐融合卡尔曼滤波器。应用动态误差系统分析方法，证明了所提出的自整定融合卡尔曼滤波器收敛于最优融合卡尔曼滤波器，具有渐近最优性。具有三个传感器的目标跟踪系统的仿真示例显示了其有效性。版权所有（c）2014 John Wiley＆Sons，Ltd.

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