The scattering centre model is well known as an approximation to provide a sparse abstraction of complex targets in radar applications. On the basis of the block-sparsity and high-frequency approximations, an improved scattering centre representation scheme is proposed for different angular observations of the land-based vehicles. For good insights into target physics and feature stability, the land-based vehicle is observed by airborne radar system in separate observation sections according to independent aspect angle regions. On the basis of that, the image patch of the land-based vehicle can be reconstructed with the measurements in each observation section. To reduce redundant information, compressive sensing followed by the CLEAN algorithm is exploited to extract the scattering centres from each image patch separately. Thus, the predominant scattering centres of the land-based vehicle can be grouped into several sub-structures with respect to the image patches. Numerical simulations are used to verify the validity and efficiency of the block-sparse representation (SR) scheme. The block-sparse representation scheme can be implemented with low computational complexity and flexibly extended to further research with multiple targets representation and applications in the radar field.

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In this paper, an imaging algorithm for the airborne radar system maneuvering along an arbitrary trajectory is proposed. The algorithm aims at wide-angle imaging with incomplete measurements from the nonlinear trajectory. The proposed composite joint sub-aperture imaging algorithm provides high reconstruction quality and supports efficient data collection policy. The image can be reconstructed by combining image patches corresponding to non-overlapping sub-apertures. The image patch is obtained by compressive sensing with joint sparse representation of the scene. Numerical results have proved that the proposed algorithm is highly effective and capable of image reconstruction without much loss in quality, especially on objects signature and contour.@en

Motivated by the sparsity of filter coefficients in full-dimension space-Time adaptive processing (STAP) algorithms, this paper proposes a fast ℓ1-regularized STAP algorithm based on the alternating direction method of multipliers to accelerate the convergence and reduce the calculations. The proposed algorithm uses a splitting variable to obtain an equivalent optimization formulation, which is addressed with an augmented Lagrangian method. Using the alternating recursive algorithm, the method can rapidly result in a low minimum mean-square error without a large number of calculations. Through theoretical analysis and experimental verification, we demonstrate that the proposed algorithm provides a better output signal-To-clutter-noise ratio performance than other algorithms.@en

In this paper, 3D imaging of forward-looking Ground Penetrating Radar (GPR) data acquired by rotating antennas have been done. The data acquisition procedure mimics data collection of the Tunnel Boring Machine (TBM). Real GPR data for a Karst scenario were analyzed, preprocessed and finally imaged with back-projection method. Results show that objects buried in the subsurface of the ground can be successfully imaged using rotating antennas, which is a solid foundation for further development of the GPR system on TBM.@en