Adaptive target detection in UWB images using Laguerre network
The new ultra wide band (UWB) synthetic aperture radar (SAR) promises foliage penetration capabilities due to the different phenomenology of the reflection of the target in UWB SAR. Therefore, the automated target detection algorithms must be redesigned to take advantage of the new phenomenology. Besides, the new algorithms should also work robustly in the nonstationary environment in UWB SAR. In this paper, a new adaptive generalised likelihood ratio test (GLRT) is proposed, which can detect the metallic object robustly using the resonance response in the UWB SAR scenario. The adaptive GLRT is formulated based on the linear transform of the resonance response. For practical online applications, the applied linear transform must be chosen to achieve a better representation of the signal without too much complexity. In this case, Laguerre recurrent networks is proposed to implement the linear transform, so that the online GLRT becomes feasible.