Wavelet transform and principal component analysis based clutter reduction for through wall imaging

Wavelet transform and principal component analysis based clutter reduction scheme is proposed for through wall imaging which is capable of discriminating between target, noise and clutter signals. Principal component analysis is used to reduce clutter and wavelet transform is used to further suppress noise signals (which results in enhancement of target signals). Proposed scheme significantly work well especially for extracting multiple targets in heavy clutters. Selection of different parameters (like mother wavelet, threshold type, threshold parameter, decomposition level and filter order etc) are explored for TWI. Existing and proposed schemes are compared on the basis of mean square error, peak signal to noise ratio and visual inspection.