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.

 

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