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Multicarrier SAR Image Reconstruction using Integrated MUSIC-LSE Algorithm

Lookup NU author(s): Mohammed Buhari, Professor Gui Yun Tian, Dr Rajesh Tiwari

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2018.

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Abstract

OAPA In synthetic aperture radar (SAR) applications, high-resolution images and effective estimation processes are vital for the reconstruction of any targets. This can be achieved using multicarrier waveforms such as orthogonal frequency division multiplexing (OFDM) with the help of appropriate signal processing algorithms. However, the quality of the reconstructed image degrades in low signal-tonoise ratio (SNR) environments during SAR data acquisition. In this paper, an integrated multiple signal classification (MUSIC) assisted least square estimation (LSE) algorithm (MUSIC-LSE) is proposed to enhance the quality of the reconstructed SAR image in a low-SNR environment. Simulation results measured and evaluated the quality of the reconstructed image using three performance indicators of root mean square error (RMSE), main lobe width and cumulative side lobe levels. These indicators are also used to investigate the effect of OFDM subcarrier selection on the reconstructed image for a different number of subcarriers. Experimental validation of the approach is carried out using two steel pipes to image and detect the curvature of the steel pipes. The results show that the proposed MUSICLSE approach produces better-reconstructed images compared to the existing linear frequency modulated (LFM) chirp and OFDM-LSE approaches in low-SNR (–10 dB) environments and enables the radar to distinguish and detect the curvature of the pipes even below the radar range and cross-range resolution.


Publication metadata

Author(s): Buhari MD, Tian GY, Tiwari R, Muqaibel AH

Publication type: Article

Publication status: Published

Journal: IEEE Access

Year: 2018

Volume: 4

Online publication date: 26/03/2018

Acceptance date: 26/03/2018

Date deposited: 14/06/2018

ISSN (electronic): 2169-3536

Publisher: IEEE

URL: https://doi.org/10.1109/ACCESS.2018.2817359

DOI: 10.1109/ACCESS.2018.2817359


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