Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1155/ASP/2006/86053 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
wavelet transform,time frequency,neural network,short time fourier transform,time frequency representation | Signal processing,Synthetic aperture radar,Computer science,Short-time Fourier transform,Fourier transform,Artificial intelligence,Wavelet transform,Wavelet,Computer vision,Pattern recognition,Inverse synthetic aperture radar,Speech recognition,Time–frequency analysis | Journal |
Volume | Issue | ISSN |
2006, | 1 | 1687-6180 |
Citations | PageRank | References |
8 | 0.73 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. K. ShreyamshaKumar | 1 | 99 | 7.66 |
| B. Prabhakar | 2 | 8 | 0.73 |
| K. Suryanarayana | 3 | 8 | 0.73 |
| V. Thilagavathi | 4 | 8 | 0.73 |
| R. Rajagopal | 5 | 8 | 0.73 |