宽带稀疏信号调制变换欠采样频谱感知

项目名称： 宽带稀疏信号调制变换欠采样频谱感知

项目编号： No.61501356

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 齐佩汉

作者单位： 西安电子科技大学

项目金额： 21万元

中文摘要： 欠采样频谱感知方法可以解决宽带频谱感知模拟采样和数字处理的速率瓶颈问题，是其发展的新方向。课题针对该方法存在的盲感知能力缺失、感知时效低以及感知性能差的问题，结合宽带调制变换欠采样机理，从以下方面展开研究：(1)稀疏性判定。研究基于支路功率对消的稀疏预判决技术，并通过支集零范数与最大重构信号约束个数的对比，联合确定宽带信号的稀疏性。(2)准确支集输出。提出基于特征值检测正交匹配追踪的支集确定算法，将相关随机矩阵白化变换成独立随机矩阵，以矩阵最大特征值与迹的比值为检验统计量，推导出优化算法迭代停止的准确条件，为分析复杂电磁环境下欠采样感知的性能极限提供理论依据。(3)收敛速度提升。利用残差瞬时与平均功率比值的分布快速判决支集改变，然后以子带短时能量的相关性估计连续周期同被占用的支集，再对剩余支集执行迭代判决，可显著减少迭代次数，改善感知实时性。课题的研究对实现实时稳健欠采样盲感知有支撑意义。

中文关键词： 频谱感知；频谱压缩感知；稀疏采样；支集恢复

英文摘要： Sub-Nyquist wideband spectrum sensing methods which could solve the bottlenecks of high sampling rate and processing speed for broadband signals are drawing more and more attention in both academia and industry. Rregarding to the ubiquitous problems of sub-Nyquist wideband spectrum sensing, such as lack of blind spectrum sensing ability, poor instantaneity and inferior spectrum sensing performance, the following problems will be studied with the sub-Nyquist approach in wideband modulated converter scheme: (1)Sparsity. With the sparsity pre-decision algorithm based on the branch power cancellation and the comparison result between zero norm of support and the maximal signal number of reconstruction, the sparsity of broadband signal is jointly determined. (2)Exact support recovery. Based on eigenvalue detection algorithm and orthogonal matching pursuit, a support recovery method which treats the ratio of maximal eigenvalue to the trace of correlation matrix as test statistics is discussed, and the accurate stop condition of optimization algorithmis is deduced via correlation matrix whitening, which could provide theoretical basis for analyzing the performance of sub-Nyquist wideband spectrum sensing in complex electromagnetic environment. (3)Convergence rate acceleration. The change of support can be quickly detected by utilizing the probability distribution of the ratio of the instantaneous power to the average power of residual, and then the support occupied in sequential sensing period will be picked out by correlation analysis of subband energy. As support recovery method is executed to the remaining support, the number of iteration of optimization algorithm will be reduced noteworthily. These researches provide the　foundation to implement blind, robust and real-time sub-Nyquist wideband spectrum sensing.

英文关键词： Spectrum Sensing;Compressed Sensing;Sparsity Sampling;Support Recovery