在分析基于正交频分复用认知系统的局部多窗口联合奇异值分解频谱感知算法优劣的基础上,针对奇异值分解运算量大,对该算法提出了改进。提出了矩阵的F范数代替奇异值分解的方法,以提高算法的实时性。经仿真分析表明,改进后的算法既保证了原有算法中多窗口法(MTM)谱估计的近最优性,提高了算法的实时性,又具有较好的检测性能。 On the basis of analyzing the advantages and disadvantages of the local multi-window joint SVD algorithm based on Orthogonal Frequency Division Multiplexing (WFN) cognitive system, this algorithm is improved for the large amount of operation of singular value decomposition. The F-norm of the matrix is ​​proposed to replace the singular value decomposition in order to improve the real-time performance of the algorithm. The simulation results show that the improved algorithm not only guarantees the near optimality of the multi-window method (MTM) spectrum estimation in the original algorithm, but also improves the real-time performance of the algorithm and has better detection performance.