混合扩频信号快速参数估计与识别算法

Fast Parameter Estimation and Recognition Approach for Hybrid Spread Spectrum Signals

针对混合扩频信号多是基于直接序列扩频和跳频扩频相结合的现状,提出了基于直接序列和线性调频相结合的混合扩频信号快速参数估计与识别算法。通过建立基于频域矩峰度系数的神经网络,将两类信号识别开来,信噪比为-2 dB时识别概率均达到95%以上。通过倒序共轭卷积和分段截取后快速解线调,实现了复合信号各参数的快速估计,信噪比为-1 dB时码速率估计精度均较高。

To improve the status of hybrid spread spectrum signal mainly based on direct sequence spread spec- trum（DSSS） signal and frequency hopping（FH） spread spectrum signal, a fast approach is presented for DSSS and chirp spread spectrum. By establishing coefficient of frequency domain moment peak based neural network, two types of signals are identified. The recognition probability is above 95% when SNR（Signal- to- Noise Ra- tio） is close to - 3 dB. By reverse - order conjugation convolution - fast dechirp, the code rate and initial frequency and slope of convolutional compound signal can be estimated. Code rate estimation accuracy is higher when SNR is close to - 1 dB.