摘要
针对跳频通信中跟踪干扰识别特征提取问题,提出了一种基于稀疏分解的跟踪干扰特征提取方法。简要介绍了稀疏分解的基本原理。根据跟踪干扰信号与跳频信号比较在时域存在时间延迟,在频域幅值大于跳频信号幅值的特点,通过对跳频信号和跟踪干扰信号进行重构,获得能表明两者之间显著差异的特征参数。仿真实验表明,该方法提取的特征参数,在信噪比大于5dB时,对跟踪干扰的识别率达到95%以上,验证了该方法的可行性和有效性。
A novel method is presented for feature extraction of follower jamming signals by using sparse decomposition technique. The basic principles of sparse decomposition are introduced. According to the difference of time delay in the time domain and amplitude in the frequency domain between the frequency hopping （FH） signals and follower jamming （FJ） signals, the marked difference feature extraction is obtained when the FH signals and FJ signals are reconstructed. The sim- ulation results show that the recognition rate of FJ can achieve 95% when the SNR is greater than 5dB, and the feasibility and effectiveness of the method is verified.
出处
《信号处理》
CSCD
北大核心
2012年第12期1714-1720,共7页
Journal of Signal Processing
关键词
稀疏分解
跳频信号
跟踪干扰信号
特征提取
时间延迟
信号重构
sparse decomposition
frequency-hopping signals
follower jamming signals
feature extraction
time delayestimation
signal reconstruction
