Abstract

A new approach has been proposed to reduce the computations involved in the ISAR imaging, which uses Harmonic Wavelet (HW) based Time-Frequency Representation (TFR). Since the HW based TFR falls into a category of non-parametric Time-Frequency (T-F) analysis tool, it is computationally efficient compared to parametric T-F analysis tools such as Adaptive Joint Time-Frequency Transform (AJTFT), Adaptive Wavelet Transform (AWT) and Evolutionary AWT (EAWT). Further, the performance of the proposed method of ISAR imaging is compared with the ISAR imaging by other non-parametric T-F analysis tools such as Short Time Fourier Transform (STFT) and Choi-Williams Distribution (CWD). In the ISAR imaging, the use of HW based TFR provides similar/better results with significant (92%) computational advantage compared to that obtained by CWD. The ISAR images thus obtained are identified using a Neural Network based classification scheme with feature set invariant to translation, rotation and scaling.