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Quantized Filtering Schemes for Multi-Sensor Linear State Estimation: Stability and Performance Under High Rate Quantization

机译:多传感器线性状态估计的量化滤波方案:高速率量化下的稳定性和性能

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In this paper we consider state estimation of a discrete time linear system using multiple sensors, where the sensors quantize their individual innovations, which are then combined at the fusion center to form a global state estimate. We prove the stability of the estimation scheme under sufficiently high bit rates. We obtain asymptotic approximations for the error covariance matrix that relates the system parameters and quantization levels used by the different sensors. Numerical results show close agreement with the true error covariance for quantization at high rates. An optimal rate allocation problem amongst the different sensors is also considered.
机译:在本文中,我们考虑使用多个传感器的离散时间线性系统的状态估计,其中传感器对自己的创新进行量化,然后在融合中心进行组合以形成全局状态估计。我们证明了在足够高的比特率下估计方案的稳定性。我们获得误差协方差矩阵的渐近近似值,该矩阵与系统参数和不同传感器使用的量化水平相关。数值结果表明,对于高速率量化,其真实误差协方差非常接近。还考虑了不同传感器之间的最优速率分配问题。

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