Abstract
Developing compressed sensing (CS) theory reveals that optimal reconstruction of an unknown signal can be achieved from very limited observations by utilizing signal sparsity. For inverse synthetic aperture radar (ISAR), the image of an interesting target is generally constructed by limited strong scattering centers, representing strong spatial sparsity. Such prior sparsity intrinsically paves a way to improved ISAR imaging performance. In this paper, we develop a super-resolution algorithm for forming ISAR images from limited observations. When the amplitude of the target scattered field follows an identical Laplace probability distribution, the approach converts super-resolution imaging into sparsity-driven optimization in the Bayesian statistics sense. We show that improved performance is achievable by taking advantage of the meaningful spatial structure of the scattered field. Further, we use the nonidentical Laplace distribution with small scale on strong signal components and large scale on noise to discriminate strong scattering centers from noise. A maximum likelihood estimator combined with a bandwidth extrapolation technique is also developed to estimate the scale parameters. Real measured data processing indicates the proposal can reconstruct the high-resolution image though only limited pulses even with low SNR, which shows advantages over current super-resolution imaging methods.
Highlights
High-resolution radar imaging techniques are widely applied in many military and civilian fields, such as target classification and recognition and aircraft traffic control
We extend the Bayesian SR imaging by using nonidentical Laplace distributions since the scattered field of a target usually follows the energy-assembling and geometric organization of the target
In case 3 and 4, we find that Improved Bayesian SR (IBSR) provides the lowest Mean Square Error (MSE) and the highest probability of detection only in the high signal-to-noise ratio (SNR) situations, with its performance degrading dramatically with the decrease of SNR
Summary
High-resolution radar imaging techniques are widely applied in many military and civilian fields, such as target classification and recognition and aircraft traffic control. All this work indicates that it is possible to substantially enhance the performance of the radar imaging by exploiting target sparsity It is usually not an easy task to characterize this sparsity quantitatively due to the uncertainty and complexity of the real target scattered field, together with the inevitable presence of noise and clutter. This makes some regularization-based methods including the featureenhanced image approach ambiguous in both parameter selection and controlling the sparsity of the recovery. A novel scheme to estimate the noise and target statistics parameters are developed for both the original Bayesian SR and the improved version by combining the constant-falsealarm-ratio (CFAR) [34] and Burg’s BWE techniques.
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