Study of a class of wavelets transforms for clutter characterization for enhanced target detection and identification

Low flying target often experience severe clutter of interference from various sources, which is difficult to characterize. Wavelets and wavelet transforms present a new and promising mathematical approach for clutter characterization for enhanced detection and identification of low flying targets. A class of wavelet transforms is proposed in this study to determine the feasibility of the wavelet based approach. They include the Daubechies/Mallat wavelet method, the wavelet decomposition method, and the Gabor transform method. All three methods provide effective time-frequency analysis of the signal. The RF waveform can be converted to its lowpass equivalent for analysis. The relative merits of the three methods are examine, especially their performance in detection and identification. The implementation and computation issues are also considered, in view of the real-time processing requirements. Simulated data sets are used to evaluate the detection and identification performances. Comparison with the traditional approach is also considered.