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将无线传感器网络节点观测区域中的一个混沌信号发送到融合中心,进行信号重构.由于节点的通信带宽受限,信号传输之前需要进行量化,给信号带来量化噪声,使得信号重构工作变得更为棘手.本文提出用平方根容积卡尔曼滤波器对融合中心收集的信号进行重构.首先估计观测信号的概率密度函数,使用最优量化器量化观测信号,在有限的量化比特数下,取得最优的信号量化性能.平方根容积卡尔曼滤波器相对无先导卡尔曼算法具有较少的求容积分点,因此具有计算量小的优点,同时迭代过程采用传递误差矩阵的平方根矩阵,保证迭代过程的稳定性和提高数据估计精度.仿真结果表明,该算法能够有效和快速地重构观测信号,并且比基于无先导卡尔曼滤波的算法更快.
A chaotic signal in the observation area of wireless sensor network node is sent to the fusion center for signal reconstruction.Because the communication bandwidth of the node is limited, the signal needs to be quantized before the signal is transmitted to bring the quantization noise to the signal so that the signal reconstruction work It is more difficult.This paper proposes to reconstruct the signal collected by the fusion center by using a square-root-volume Kalman filter.First, the probability density function of the observed signal is estimated, and the optimal quantizer is used to quantize the observed signal.Under a limited number of quantization bits, The square root-root-volume Kalman filter has less volumetric points than the pilot-less Kalman algorithm, so it has the advantage of small amount of calculation and the iterative process uses the square root matrix of the transfer error matrix to ensure the iteration The stability of the process and the improvement of the data estimation accuracy.The simulation results show that the proposed algorithm can reconstruct the observed signal effectively and quickly and is faster than the algorithm based on no-lead Kalman filtering.