Robust Signals Detection Algorithm Based on Cyclostationarity in Impulsive Noise

A robust method for detecting the communication signals impinging on an antenna with interference and non-Gaussian impulsive noise is introduced in this paper. Degradation of the conventional cyclic detector which based on max-output-SNR criterion in impulsive noise is shown both theoretically and experimentally. By fusing second-order cyclostationarity and fractional lower-order statistics, a type of cyclic fractional lower-order statistics is developed which is defined for exploiting cyclostationarity property. Then, a new robust type of detection algorithm is developed using the theory of optimal filtering based on max-output-SNR criterion and alpha-stable distribution, including the fractional lower-order cyclic matched filter, which is formulated for detecting the communication signals in the presence of interference and non-Gaussian alpha-stable distribution impulsive noise. It is shown that the new method is robust to Gaussian and non-Gaussian impulsive noises, and is immune to the interfering signals which occupy the same spectral band as that of the received signal. Simulation results show the robustness and effectiveness of the proposed algorithm.

Frequency Spectrum Estimate Based on FLOS in Various Radar Clutter Noises Environments

It is very important that radar clutters are distinguished from others exactly when radar working in a complex environment. A new method named square convergence was proposed to classify radar clutter noises. The performance of conventional spectrum estimate algorithms based on second order statistic (SOS) degenerate in stable distribution environment, auto-covariation and auto-covariance methods based on fractional lower order statistics (FLOS) were proposed in this paper. The simulation results showed that the proposed FLOS methods were robust.