A method of weak target detection based on the sea clutter

Abstract

According to the phase space reconstruction theory of nonlinear system, we propose a prediction method of support vector machine based on genetic algorithm. Using the improved autocorrelation method and Grassberger-Procaccia algorithm to determine the time delay and embedding dimension of chaotic signal, the phase space reconstruction is implemented. The penalty coefficient and the kernel function parameter of support vector machine are optimized by genetic algorithm. Combined with support vector machine, single-step prediction model of the chaotic sequence is set up, so we can detect the weak signal in chaos from the prediction error (including the transient signal and periodic signal). Lorenz attractor and the data from the McMaster IPIX radar sea clutter database are used in the simulation. The proposed method can effectively detect the weak target from chaotic signal. When the signal-to-noise ratio is -89.7704 dB in the chaotic noise background, by using the new method the root mean square error can be reduced by two orders of magnitude, reaching 0.00049521, while the conventional support vector machine can reach only 0.049 under the condition of -54.60 dB.