面向非平稳海杂波的信号处理方法研究进展

Research Progress of Signal Processing Methods for Nonstationary Sea Clutter

在海杂波背景的信号检测问题中,由于海杂波具有明显的非平稳特性,其统计特性随时间改变,传统的统计信号处理方法无法取得最优效果。现有的非平稳信号处理方法主要包括基于统计模型的方法和基于时间序列分析的方法。从统计模型的角度出发,目前最常用的是使用复合高斯(Compound Gaussian, CG)分布对海杂波进行建模。从时间序列分析的角度出发,目前有使用时变自回归(Time-varying autoregressive, TVAR)模型、广义自回归条件异方差(Generalized autoregressive conditional heteroskedasticity, GARCH)模型、随机波动(Stochastic volatility, SV)模型等对非平稳信号进行建模的方法。本文对上述非平稳信号处理方法进行对比研究,并通过仿真实验验证其运用在海杂波背景下信号检测问题中的可行性。上述方法均可以一定程度上准确描述海杂波的部分特性,但是难以通过这些方法设计易于实现的检测器,还需进一步研究面向海杂波背景下检测问题的非平稳信号建模表征方法。

With regard to signal detection problems in sea clutter background,traditional methods can not achieve optimal performance due to that sea clutter is an example of nonstationary signal and its statistical characteristics vary over time.The existing nonstationary signal processing methods mainly include two categories:methods based on statistical models and methods based on time series analysis.From a statistical point of view,the most commonly used method is modeling sea clutter by compound Gaussian(CG)distribution.From the perspective of time series analysis,there are many models to describe nonstationary signals including time-varying autoregressive(TVAR)model,generalized autoregressive conditional heteroskedasticity(GARCH)model and stochastic volatility(SV)model.We make comparisons of these methods mentioned above and evaluate if they could be applied to detection in sea clutter background.All of the methods can accurately describe part of the characteristics of a nonstationary sea clutter signal to some extent.However,there exist difficulties if we try to design easy-to-implement detectors.Further research about modeling the characteristics of nonstationary signals is needed for signal detection in sea clutter background.