Target Identification Using Harmonic Wavelet Based ISAR Imaging

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 nonparametric 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 nonparametric 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.

ISAR geometry, signal model, and image processing algorithms

“…In [21], a method for aircraft target shape extraction from ISAR images based on the combination of a modified SUSAN algorithm and variational of level set is proposed, to guarantee the invariance in translation and rotation of the extracted shape the moment invariants and Fourier descriptors are used, the impact of the information fusion on recognition is also investigated. An approach reducing the computations in the ISAR image identification based on harmonic wavelet time–frequency representation is proposed in [22]. On the basis of a comparison of range–Doppler imagery to 3D ship reference models, an ISAR image classification is discussed in [23].…”