一种无线传感器网络中的混沌信号重构算法

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 reconstruction algorithm in wireless sensor networks

A chaotic signal in an observation area of network nodes is sent to a fusion center for reconstruction. As the communication bandwidth is limited, the signal must be quantified before sending to the fusion center, which will add quantization noise to the observed signal, which makes the signal reconstruction more difficult. A chaotic signal reconstruction algorithm is proposed in this paper based on square-root cubature Kalman filter. Firstly the probability density function of the observed signal is estimated, and then the optimal quantizer is used to quantify the observed signal. Under the limited budget of quantization bits, the best performance can be achieved. Compared with the unscented Kalman filter counterpart, our algorithm has fewer cubature points and has the merit of small computation load; meanwhile, it uses the square root of error variance for iteration, this will be more stable and accurate when iterating for parameter estimation. Simulation results show that the algorithm can reconstruct the observed signal quickly and effectively, with consuming less computation time and being more accurate than the one based on unscented Kalman filter.
Keywords：cubature ruleprobability density functionsignal reconstructionchaotic signal