In high-resolution spaceborne synthetic aperture radar (SAR), imaging of moving ship targets is strongly influenced by ships' complex three-axis motions, so that imaging results are fuzzy and unfocused. Yet scattered and moving information on ship targets is wholly contained in the complex image data. This paper proposes a novel SAR and inverse SAR (SAR-ISAR) hybrid imaging method to improve imaging effects, using this complex SAR image data on ship targets, and based on frequency-domain-extraction-based adaptive joint time frequency (FDE-AJTF) decomposition. First, complex SAR image data is transformed to the Doppler domain in the azimuth dimension, and the optimum azimuth data are selected. Next, the signal in each range cell is decomposed to its polynomial phase signal (PPS) components by FDE-AJTF. Finally, a two-dimensional image of the ship target at a given azimuth time is constructed directly. The feasibility and effectiveness of this proposed imaging method is verified through comparisons with conventional methods in simulation and experimental tests.
Abstract:The maneuvering target echo of high-resolution radar can be expressed as a multicomponent polynomial phase signal (mc-PPS). However, with improvements in radar resolution and increases in the synthetic period, classical time frequency analysis methods cannot satisfy the requirements of maneuvering target radar echo processing. In this paper, a novel frequency domain extraction-based adaptive joint time frequency (FDE-AJTF) decomposition method was proposed with three improvements. First, the maximum frequency spectrum of the phase compensation signal was taken as the fitness function, while the fitness comparison, component extraction, and residual updating were operated in the frequency domain; second, the time window was adopted on the basis function to fit the uncertain signal component time; and third, constant false alarm ratio (CFAR) detection was applied in the component extraction to reduce the ineffective components. Through these means, the stability and speed of phase parameters estimation increased with one domination ignored in the phase parameter estimation, and the accuracy and effectiveness of the signal component extraction performed better with less influence from the estimation errors, clutters, and noises. Finally, these advantages of the FDE-AJTF decomposition method were verified through a comparison with the classical method in simulation and experimental tests.
High-resolution synthetic aperture radar (SAR) operating with a large bandwidth is subject to impacts from various kinds of narrowband interference (NBI) in complex electromagnetic environments. Recently, many radio frequency interference (RFI) suppression approaches for SAR based on sparse recovery have been proposed and demonstrated to outperform traditional ones in preserving the signal of interest (SOI) while suppressing the interference by exploiting their intrinsic structures. In particular, the joint recovery strategy of SOI and NBI with a cascaded dictionary, which eliminates the steps of NBI reconstruction and time-domain cancellation, can further reduce unnecessary system complexity. However, these sparsity-based approaches hardly work effectively for signals from an extended target or NBI with a certain bandwidth, since neither of them is sparse in a prescient domain. Moreover, sub-dictionaries corresponding to different components in the cascaded matrix are not strictly independent, which severely limits the performance of separated reconstruction. In this paper, we present an enhanced NBI separation algorithm for SAR via sensing matrix optimization-based block sparse Bayesian learning (SMO-BSBL) to solve these problems above. First, we extend the block sparse Bayesian learning framework to a complex-valued domain for the convenience of radar signal processing with lower computation complexity and modify it to deal with the separation problem of NBI in the contaminated echo. For the sake of improving the separated reconstruction performance, we propose a new block coherence measure by defining the external and internal block structure, which is used for optimizing the observation matrix. The optimized observation matrix is then employed to reconstruct SOI and NBI simultaneously under the modified BSBL framework, given a known and fixed cascaded dictionary. Numerical simulation experiments and comparison results demonstrate that the proposed SMO-BSBL is effective and superior to other advanced algorithms in NBI suppression for SAR.
Abstract. Moving vessel imaging by spaceborne Synthetic Aperture Radar (SAR) is one of the most complex SAR systems, but its modeling and simulation is mostly procedural oriented functional programming in Matlab. Aiming to improve the convenience and efficiency, and enhance the expansibility and portability of this system, an object oriented programming method based on Matlab class is presented with modeling structure and simulation procedure. The rationality of the modular system is proved and the validities of SAR and ISAR imaging process are verified by the imagery results obtained in the simulation test.
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