Synthetic aperture radar (SAR) has significant role in remote sensing. Phase errors due to uncompensated platform motion, measurement model mismatch, and measurement noise can cause degradations in SAR image reconstruction. For efficient processing of the measurements, image plane is discretized and autofocusing algorithms on this discrete grid are employed. However, in addition to the platform motion errors, the reflectors, which are not exactly on the reconstruction grid, also degrade the image quality. This is called the off-grid target problem. In this paper, a sparsity-based technique is developed for autofocused spotlight SAR image reconstruction that can correct phase errors due to uncompensated platform motion and provide robust images in the presence of off-grid targets. The proposed orthogonal matching pursuit-based reconstruction technique uses gradient descent parameter updates with built in autofocus. The technique can reconstruct high-quality images by using sub Nyquist rate of sampling on the reflected signals at the receiver. The results obtained using both simulated and real SAR system data show that the proposed technique provides higher quality reconstructions over alternative techniques in terms of commonly used performance metrics.

No abstract

Compressive Sensing (CS) based techniques generally discretize the signal space and assume that the signal has a sparse support restricted on the discretized grid points. This restriction of representing the signal on a discretized grid results in the off-grid problem which causes performance degradation in the reconstruction of signals. Sensor calibration is another issue which can cause performance degradation if not properly addressed. Calibration aims to reduce the disruptive effects of the phase and the gain biases. In this paper, a CS based blind calibration technique is proposed for the reconstruction of multiple off-grid signals. The proposed technique is capable of estimating the off-grid signals and correcting the gain and the phase biases due to insufficient calibration simultaneously. It is applied to off-grid frequency estimation and direction finding applications using blind calibration. Extensive simulation analyses are performed for both applications. Results show that the proposed technique has superior reconstruction performance.

Synthetic Aperture Radar (SAR) has significance in many remote sensing applications. One of the main problems with SAR is the platform motion that causes defocusing in the reconstructed SAR image. To mitigate this problem, for particularly on imaging of fields that admit a sparse representation, various sparsity based techniques that either apply optimization procedures or greedy iterative solutions have been proposed in the literature. Although these techniques have been mainly compared with classical phase gradient autofocus (PGA) algorithm, they have not been analyzed and compared with each other. In this paper several of the recent sparsity based SAR phase correction techniques are compared using metrics such as mean square error (MSE), entropy, target to background ratio (TBR) in terms of undersampling ratio, signal to noise ratio (SNR). In addition to comparisons, a cross validation based stopping criterion is introduced with an OMP procedure to free the algorithm from user defined parameters. The techniques are tested on simulated data for detailed comparisons. Real data results of tested techniques are also provided. Our initial results show that all compared sparsity based techniques provide better performance compared to PGA with varying relative performances.