基于最优延时和多天线的并行检测算法

发布时间：2018-07-25 14:03

【摘要】：循环平稳特征检测是一种在低信噪比时也能准确发现信号的盲检测技术,但其算法计算周期较长.针对此问题提出了一种改进的并行渐进最优卡方检测算法,通过识别循环谱特性和遍历循环自相关确定最优的循环频率和延时,用循环自相关以及渐进协方差矩阵构建检验统计量,并对二维向量和谱分量实部和虚部进行合并及迭代过程并行处理,仿真得到了检测概率和检测时间.同时,根据最大似然比统计量的有偏估计和渐进分布特性导出了检测概率的表达式,结果表明:最优延时的理论与仿真吻合得很好,多天线技术充分利用了空间分集,在干扰较强时仍然具有较高的检测概率,且并行处理过程能有效减少检测时间.
[Abstract]:Cyclic stationary feature detection is a blind detection technique which can detect signals accurately at low signal-to-noise ratio (SNR), but its algorithm has a long calculation period. To solve this problem, an improved parallel progressive optimal chi-square detection algorithm is proposed. The optimal cycle frequency and delay are determined by identifying the cyclic spectrum characteristics and ergodic cyclic autocorrelation. The test statistics are constructed by cyclic autocorrelation and asymptotic covariance matrix, and the real and imaginary parts of two-dimensional vector and spectral component are merged and iterated in parallel. The detection probability and detection time are obtained by simulation. At the same time, the expression of detection probability is derived according to the biased estimation and asymptotic distribution of the maximum likelihood ratio statistic. The results show that the theory of optimal delay is in good agreement with the simulation, and the multi-antenna technique makes full use of spatial diversity. The detection probability is still high when the interference is strong, and the detection time can be reduced effectively by parallel processing.
【作者单位】：云南大学无线创新实验室;云南财经大学物流学院;昆明市谱传感与无线电监测重点实验室;
【基金】：国家自然科学基金(61072079;61161007) 中国博士后基金项目(2013M531989)资助课题
【分类号】：TN911.23

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