脉冲噪声中基于数据可信度加权的跳频信号检测

时频分析是跳频(frequency-hopping, FH)信号检测的有力工具,但是脉冲噪声下性能严重退化,无法有效地提取跳频信号的周期、频率和跳变时刻等参数;基于分数低阶统计量和最大似然估计(maximum-likelihood,ML)的算法是改善脉冲噪声下FH信号时频分布的两类常用方法,但前者性能改善有限,后者通常对噪声的概率分布较为敏感,且计算复杂度高。对此,提出一种基于数据可信度加权(weighting based on the data credibility,WDC)的FH信号检测方法。该方法基于云模型(cloud model, CM)理论,建立了数据可信度的概念,以分析脉冲噪声下接收信号的不确定性,然后在此基础上实现信号加权,改善脉冲噪声下FH信号的时频分布特征。仿真实验证明,在稳定分布噪声中,该方法与基于分数低阶及Myriad滤波器的时频分析方法相比,能够较好地抑制脉冲噪声,获得FH信号的参数信息,具有良好的鲁棒特性。

Detection of FH signals based on data credibility weighting in impulse noise environment

Time-frequency analysis is a powerful tool for frequency-hopping(FH) signal detection, however, the performance of time-frequency analysis will degrade drastically in impulse noise environment, failing to extract the hopping duration, frequency and timing effectively. Moreover, methods based on fractional lower order statistics and maximum-likelihood (ML) are generally used to improve the performance of FH signal time-frequency distribution, but the performance improvement of the former is limited, and the latter is usually sensitive to the noise distribution and has high computational complexity. To detect FH signals in the presence of impulse noise, a detection method of FH signal is proposed based on data credibility weighting. In the proposed method, the concept of data credibility is established based on the cloud model theory to analyze the uncertainty of the received signal. On this basis, the weighting process is implemented to the received signal and improves the performance of time-frequency distribution of FH signal in the impulse noise environment. Simulation results show that compared with the fractional lower order statistics as well as the Myriad filter based time-frequency analysis methods, the proposed method can detect the FH parameters with the noise being suppressed effectively, and it is robust in the stable noise environment.