Target Detection in Sea Clutter Based on Multifractal Characteristics After Empirical Mode Decomposition

Abstract

Characteristic analysis of sea clutter is important in utilizing radar observations and detecting sea-surface targets. Real data signals are analyzed to determine the multifractal characteristics of sea clutter signals. Sea clutter is a nonlinear, nonstationary radar echo signal. A novel method that detects targets in sea clutter is proposed by completely utilizing the strengths of empirical mode decomposition (EMD) and combining it with multifractal characteristics. The EMD method is applied to decompose sea clutter signals into several intrinsic mode functions (IMFs). Multifractal detrended fluctuation analysis is utilized to calculate the generalized Hurst exponent for the main functions of IMF after which real sea clutter data are used for training and testing. Results show that targets in sea clutter can be effectively observed and detected through the proposed method, the performance of which is better than that of the target detection method for the generalized Hurst exponent under typical time, fractional Fourier transform and wavelet transform domains.