无线传感网中分布式信号检测的多维特征值算法优化研究

在大规模无线传感网的分布式信号检测中，针对相关性较高并有一定冗余度的数据集，在保证数据采集可信任的情况下，通过高效算法提高精度是重要的研究方向。 提出一种分散功率算法DPM，用于分布式计算样本协方差矩阵的最大特征值，通过将平均共识和迭代功率法相结合，在相对少量样本和有限次数迭代的条件下，实现了协方差矩阵最大特征值的较快收敛速度和较高精度估计。对比MECD算法和DST算法，仿真结果表明，新算法有效减少了信号样本数和迭代次数，收敛速度较快，可获得更高的检测精度。

Optimization of multidimensional eigenvalue algorithm for distributed signal detection in wireless sensor networks

In the distributed signal detection of large scale wireless sensor networks, data sets feature high correlation and some redundancy, so when ensuring data acquisition is trusted, it is an important research direction to improve accuracy of high efficiency algorithms. We propose a decentralized power algorithm for the distributed calculation of the maximum eigenvalue of the sample covariance matrix. By combining the average consensus and the iterative power methods, the fast convergence rate and the higher accuracy estimation of the maximum eigenvalue of the covariance matrix are realized under the condition of relatively small sample and a finite number of iterations. Compared with the MECD algorithm and the DST algorithm, simulation results show that the proposed algorithm can effectively reduce the number of signal samples and the number of iterations, the convergence speed is faster, and the detection accuracy can be improved.