突发信号盲存在性检测的两种算法

Two Blind Presence Detection Algorithms for Burst Signals

本文从频率域和广义频率域研究了复数高斯白噪声信道中突发信号的存在性检测问题,分别提出了基于频率域短时傅氏变换幅度谱熵和基于广义频率域奇异值分解的检测算法,并与时间域的短时能量算法进行了比较。仿真结果表明谱熵法具有较好的鲁棒性,特别是在低信噪比时能够获得较高的检测概率,并且受突发信号调制方式等参数的影响较小。奇异值算法与短时能量法相比更适用于采用QAM调制的突发信号。

This paper is focusing on the blind presence detection algorithm for burst signals in the complex Additive White Gaussian Noise （AWGN） channel from the viewpoint of frequency domain and generalized frequency domain. Two blind algorithms are proposed,one is based on the amplitude spectral entropy obtained from the Short-Time Fourier Transform （STFT） of the received data,the other is based on the singular values decomposition. Comparisons are made with the short-time energy detection algorithm in time domain. Simulation results show that the spectral entropy-based algorithm is of good robustness among the three algorithms, especially when the Signal-to-Noise Ratio （SNR） is low,and the probability of detection is influenced little by the parameters of the burst siguals,such as the modulation scheme. And the singular values-based detection algorithm is more effective than the energy-based algorithm for burst signals using Quadrature Amplitude Modulation （QAM）.