Deconvolution technique can be utilized in the forward-looking radar (FLR). However, the forward-looking imaging performance degenerates greatly due to the effect of high-speed movement of the platform. In this article, an efficient Bayesian forward-looking superresolution imaging algorithm based on Doppler deconvolution in expanded beam space is proposed. First, the Doppler phase information caused by the high-speed platform is fully exploited and the Doppler matrix is integrated with the antenna pattern. The Doppler convolution model of the echo signal for forward-looking is derived in this article. Then, the Doppler phase information is adopted to perform the Doppler deconvolution. Moreover, an expanded beam space is constructed to enhance the sparsity of the imaging scene. The complex Gaussian distribution and the Laplace distribution have been used to model the distribution characteristics of noise and targets in the imaging scene, respectively. Finally, based on the Bayesian framework, the forward-looking imaging problem is converted into the convex optimization problem. The performance assessment based on simulated and experimental data, also in comparison to the conventional real beam, truncated singular value decomposition (TSVD), iterative adaptive approach (IAA) methods, has demonstrated the effectiveness of our proposed algorithm under high-speed platform scenarios.

An adaptive super‐resolution imaging algorithm to enhance the performance of airborne single channel forward‐looking radar is proposed. Multiple beams data to expand the processing space to enforce the sparsity of scatterers with respect to the antenna pattern was first utilised. Then, the imaging is converted into a problem of signal reconstruction with Lp‐norm regularisation, and the regularisation parameter is data driven. Simulations are given to verify the effectiveness of the algorithm.

Traditional forward-looking imaging methods of real aperture radar yield unsatisfactory performance in the presence of outliers. In this article, a method based on variational Bayesian (VB) is proposed to obtain forward-looking imaging in the presence of outliers. First, considering the non-Gaussian property of the imaging noise due to the outliers, we propose to use the Student- $t$ distribution to model noise. In this model, the echo signal does not need preprocessing for the outliers. Second, the Laplace hierarchical distribution is introduced to describe the sparsity of the target. Then, the forward-looking imaging problem converts to the optimal problem. Finally, we give the VB derivation to solve the imaging parameter. To illustrate the imaging performance in the presence of outliers, the outliers are randomly added to some angles and the whole scene of the echo signal in the simulations, respectively. From the simulation results, we can see that the proposed method achieves excellent performance for forward-looking imaging in the presence of outliers.

The efficient scan moving target indication (MTI) mode is implemented in the wide‐area ground MTI systems for the purpose of wide‐area surveillance. However, because of the scanning movement of the radar antenna, it is difficult to acquire enough coherent pulses in a single azimuth direction in the Doppler beam sharpening (DBS), so the sharpening ratio is greatly limited. To mitigate this problem, a combined super‐resolution algorithm with aperture extrapolation for DBS imaging is proposed to enhance the sharpening ratio in this study. In this algorithm, aperture extrapolation technique is utilised to increase the data length in azimuth direction and the amplitude and phase estimation of a sinusoid, which can acquire accurate spectral estimation with much lower side lobes and narrower spectral peaks, is applied to replace the fast Fourier transform to perform the Doppler analysis. In this way, the sharpening ratio in the new algorithm could be enhanced by one time. Experimental results on simulation data and real data verify the effectiveness of the new super‐resolution algorithm, and demonstrate that the proposed algorithm can provide sharp and clear scene information with lower side lobes and narrower peaks.

In airborne forward-looking imaging, the azimuth resolution and the imaging efficiency are important. In this paper, we propose a low-dimension sparse Bayesian learning with Doppler compensation (LDSBL-DC) method to improve the azimuth resolution with a low computational complexity in airborne forward-looking imaging. First, since the variant pitching angle causes the space-variant of the Doppler centroid, the Doppler convolution matrix needs to be constructed in each range cell. We construct a Doppler compensation matrix to eliminate the space-variant of the Doppler centroid. After the Doppler centroid compensation, the Doppler convolution matrix only needs to be constructed once. Second, we propose a low-dimensional projection model based on the singular value decomposition. In the low-dimensional projection model, the high-dimension echo data is compressed to low-dimension data. Finally, combining Doppler centroid compensation and low-dimensional projection model, a new forward-looking imaging model is created, and we introduce sparse Bayesian learning (SBL) to estimate the imaging parameters. In the estimation of the targets’ scattering coefficient, we reduce the computational complexity by the matrix transformation. Several simulations are designed to evaluate the performance of the efficient forward-looking imaging method. The simulation results show the LDSBL-DC method can improve the azimuth resolution with a low computational complexity.