In terms of the target with micromotion parts, the readability of inverse synthetic aperture radar (ISAR) image of the main body is influenced by micro-Doppler (m-D) effects. Some facts, such as radar working modes and electromagnetic environment, may lead to sparse aperture, which further induces high side lobes and makes the removal of m-D effects more difficult. To jointly eliminate m-D effects and the interference introduced by sparse aperture, a novel m-D effects removed ISAR imaging algorithm is proposed. In this technique, sparse ISAR imaging with the removal of m-D effects can be established as an optimization model of joint low-rank and sparse representation, which is a quadruple constraint optimization problem, including the low rank of the high-resolution range profile (HRRP) of the main body, the sparsity of the HRRP of micromotion parts, the sparsity of the imaging result of the main body, and the noise constraint of the echo signal. Furthermore, the matrix factorization method is theoretically presented to simplify the optimization process of the nuclear norm, and the alternating direction method of multipliers is utilized to solve all constructed models efficiently. Experimental results based on both simulated and measured data demonstrate the superiority of the proposed algorithm.
KEYWORDS: Detection and tracking algorithms, Chemical species, Reconstruction algorithms, Radar imaging, Synthetic aperture radar, Signal to noise ratio, Computer simulations, Radar, Signal processing, Scattering
With regard to inverse synthetic aperture radar imaging with limited bandwidth and sparse aperture, it is a challenge to traditional range-Doppler (RD) algorithm. We proposed an innovative two-dimensional (2D) joint sparse imaging algorithm, namely, 2D fast orthogonal matching pursuit (2D-FOMP) algorithm. In the proposed algorithm, one-dimensional OMP (1D-OMP) is extended to 2D-OMP in the complex domain from three aspects of atom recognition, projection update, and residual update. Then, the equivalence between 1D-OMP and 2D-OMP is analyzed theoretically. Meanwhile, two strategies that multi atom recognition and matrix recursive update are added in 2D-OMP to further improve the reconstruction speed of 2D-FOMP. Experimental results based on both simulated and measured data demonstrate that the proposed algorithm has good imaging performance under noisy and sparse conditions.
KEYWORDS: Reconstruction algorithms, Radar imaging, Signal to noise ratio, Detection and tracking algorithms, Synthetic aperture radar, Scattering, Radar, Target detection, Strontium, Real time imaging
To realize inverse synthetic aperture radar imaging with limited bandwidth and short coherent processing interval, in view of pattern-coupled sparse structure of imaging targets, a two-dimensional joint sparse imaging algorithm, namely two-dimensional reweighted alternating direction method of multipliers (2D-RADMM), is proposed. The main idea of the proposed algorithm contains two aspects: one is expanding one-dimensional RADMM (1D-RADMM) to 2D-RADMM by matrix processing, so as to reduce the computation and memory usage. The other is designing different pattern-coupled modes to further improve the imaging performance. Extensive experiments based on two sets of measured data demonstrate that compared with traditional unweighted algorithms, the proposed algorithm has excellent imaging quality under noisy and sparse conditions, and its imaging time is <2 s, which can be suitable for the real-time inverse synthetic aperture radar imaging.
In the adjacent multi-target scenario, the Gaussian mixture probability hypothesis density (GM-PHD) algorithm encounters problems of inaccurate target number estimation and low tracking accuracy. To tackle these problems, this paper proposes an improved components management strategy for GM-PHD algorithm. We develop a master-slave mode to process Gaussian components, the master components whose weights exceed the extraction threshold are retained to avoid merging them each other, which guarantees the accuracy of target number estimation. Meanwhile, the slave components which satisfying the merging conditions are merged with the corresponding master components to improve the target tracking accuracy. Simulation results show that the proposed algorithm can achieve better performance than conventional GM-PHD algorithm in different clutter environments.
KEYWORDS: Signal to noise ratio, Data modeling, Scattering, Transmitters, Receivers, Reconstruction algorithms, Electromagnetism, Sodium, Detection and tracking algorithms, Radar
A sparse representation-based bistatic inverse synthetic aperture radar (ISAR) imaging method can achieve a high-resolution image of a target with sparse aperture data. However, the bistatic ISAR system is more sensitive to noise than the monostatic one because of its nonmirror reflection geometry. To overcome this drawback, we propose the sparse aperture bistatic ISAR imaging method based on joint sparse model. Considering the joint sparse information of bistatic ISAR echo, a joint sparse imaging model is constructed. Then, the dechirped sparse aperture bistatic ISAR echo after translational compensation is transformed into range fast time and azimuth slow time domains by the joint sparse imaging model, and a corresponding azimuth sparse basis is constructed. Then a joint sparse complex approximate message passing algorithm is proposed to joint sparse imaging model. The joint sparse imaging problem is converted to a block sparse imaging problem by vectorization. Using the relationship between the vectorization of the matrix and the Kronecker product, a matrix iteration structure is proposed to solve the joint sparse model efficiently and accurately. The experimental results based on both scattering point model and electromagnetic calculation model data verify the effectiveness of the proposed imaging method.
A matrix-formed iteratively reweighted complex approximate message passing (MIRCAMP) method is proposed for inverse synthetic aperture radar (ISAR) imaging with two-dimensional (2-D) cluster sparse structure. The main idea of the proposed method is extending the complex approximate message passing into matrix-form and utilizing the iteratively reweighted way to exploit the 2-D cluster sparse structure in the ISAR image. The improved reconstruction performance of the MIRCAMP contains two parts: one is enhancing 2-D cluster signal reconstruction performance through a coupling relation between the sparsity patterns of neighboring coefficients. The other is improving recovery efficiency through 2-D matrix-formed iteration to avoid the large memory and computational requirements. Experimental results show the superior reconstruction performance of the proposed MIRCAMP.
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