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.