Target Identification Using Harmonic Wavelet Based ISAR Imaging
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Target Identification Using Harmonic Wavelet Based ISAR Imaging B. K. Shreyamsha Kumar, B. Prabhakar, K. Suryanarayana, V. Thilagavathi, and R. Rajagopal Central Research Laboratory, Bharat Electronics Limited, Bangalore-560013, India Received 30 April 2005; Revised 21 November 2005; Accepted 23 November 2005 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. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
Inverse synthetic aperture radar (ISAR) is an imaging radar that uses the target’s pitch, roll, and yaw motions to generate an image in the range-Doppler plane. Primarily, the Fourier transform (FT) was used for the ISAR imaging with the assumption that Doppler frequency is constant over the imaging time duration [1, 2]. However, the assumption of constant Doppler frequency is not true as the Doppler frequency varies in time because of the nonuniform motion of the target due to maneuvers. Hence the FT-based method suffers from the disadvantage of image blurring in the final output. In the last decade, many techniques such as transform domain methods, subaperture methods, and superresolution methods have been applied to obtain the time-varying spectrum in the hope of enhancing image resolution. However, none of them completely resolved the blurring problem. With the intention of obtaining focused ISAR image, Chen et al. introduced time-frequency (T-F) transform in the place of FT. Well-known T-F transforms include shorttime Fourier transform (STFT), Wigner-Ville distribution (WVD) [1, 2], continuous wavelet transform (CWT) [3], adaptive joint time-frequency transform (AJTFT) [4], adaptive wavelet transform (AWT) [5], and evolutionary AWT (EAWT) [6]. Among these T-F transforms, STFT, WVD, and CWT fall into a category of nonparametric T-F analysis tools whereas AJTFT, AWT, and EAWT fall into a
category of parametric T-F analysis tools. The STFT is the best-known and most basic T-F analysis tool, but it suffers from tradeoff between time resolution and frequency resolution. The WVD [7, 8] provides better resolution both in time as well as frequency, but
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