Target identification using harmonic wavelet based ISAR imaging

Authors:
B. K. Shreyamsha Kumar;B. Prabhakar;K. Suryanarayana;V. Thilagavathi;R. Rajagopal
Affiliations:
Central Research Laboratory, Bharat Electronics Limited, Bangalore, India;Central Research Laboratory, Bharat Electronics Limited, Bangalore, India;Central Research Laboratory, Bharat Electronics Limited, Bangalore, India;Central Research Laboratory, Bharat Electronics Limited, Bangalore, India;Central Research Laboratory, Bharat Electronics Limited, Bangalore, India
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2006

Citing 7
Cited 2

Ten lectures on wavelets

Ten lectures on wavelets
Fuzzy set theoretic measures for automatic feature evaluation II

Information Sciences: an International Journal
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Time-frequency analysis: theory and applications

Time-frequency analysis: theory and applications
Digital Image Processing

Digital Image Processing
A hybrid information maximisation (HIM) algorithm for optimal feature selection from multi-channel data

ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Very low bit rate video coding using arbitrarily shaped region-based motion compensation

IEEE Transactions on Circuits and Systems for Video Technology

Superresolution versus motion compensation-based techniques for radar imaging defense applications

EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Subinteger range-bin alignment method for ISAR imaging of noncooperative targets

EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications

Quantified Score

Hi-index	0.00

Visualization

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.