The current conventional fusion imaging system mainly relies on the fusion of light scattering information to obtain imaging results, which leads to poor fusion imaging results due to the lack of correction and denoising of images. In this regard, the design of an ISAR fusion imaging system based on an orthogonal matching tracking algorithm is proposed. A long-wave infrared detector is used as the main acquisition design for infrared images, and the hardware structure of the system is designed. The non-uniformity of the IR image is corrected by combining the single-point correction algorithm, and the image is filtered and de-noised, and finally the image pixels are fused by the pixel weighting method. In the experiments, the proposed fusion imaging system is verified for its imaging performance. The experimental results show that the proposed fusion imaging system performs image fusion with low imaging ranging errors and has a more satisfactory fusion imaging performance.
Inverse synthetic aperture radar (ISAR) autofocus imaging performance is challenged by the residual phase errors that arise from the traditional motion compensation methods with sparse aperture. Moreover, sparse reconstruction algorithms usually ignore the block sparsity in the ISAR image, which cannot recover structured information of the block structure target completely. An imaging method based on joint minimum Tsallis entropy and pattern-coupled sparse Bayesian learning algorithm is proposed to achieve ISAR autofocus imaging of block structure targets with sparse aperture. A pattern-coupled hierarchical complex Gaussian prior is utilized to characterize the correlation among ISAR image pixels in the complex domain. Such a prior model has the potential to encourage block-sparse patterns and achieve ISAR sparse aperture imaging of block structure targets without knowing the block partition prior information. The residual phase errors are estimated based on the minimum Tsallis entropy criterion during the sparse reconstruction of ISAR image to achieve ISAR autofocus imaging, which has the advantage of improving the computational efficiency compared with minimum Shannon entropy criterion. The superiority of the proposed algorithm is verified by the experimental results based on both simulated and measured data.
In bistatic inverse synthetic aperture radar (Bi-ISAR) imaging of targets with rotating components, the micro-Doppler effect generated by rotating components can produce interference bands in azimuth resolution, seriously affecting the imaging results and causing difficulties in target recognition. A unique Bi-ISAR target micro-Doppler separation method with rotating components is proposed by introducing the complex variational mode decomposition (CVMD) method. First, the echo signal is decomposed into different intrinsic mode functions (IMFs) by CVMD for the Bi-ISAR imaging target with rotating parts. Then, IMFs are divided into main and micromotion parts by setting the energy threshold. The IMFs belonging to the main part are extracted, and the imaging is performed using the range-Doppler algorithm. The simulation results show that CVMD outperforms the complex empirical mode decomposition algorithm and the complex local mean decomposition algorithm in the Doppler separation of Bi-ISAR targets with rotating components. In addition, CVMD exhibits good robustness and provides innovative ideas for future research on micro-Doppler separation technology.
Based on the low imaging resolution of bistatic inverse synthetic aperture radar (Bi-ISAR) and the failure of pulse correlation under the condition of sparse aperture cause that of the traditional self-focusing algorithm, a Bi-ISAR sparse aperture self-focusing algorithm with the combined constraint of image quality optimization and sparsity is proposed. First, the proposed algorithm establishes the Bi-ISAR sparse aperture self-focusing signal model, reconstructs images through fast sparse Bayesian learning (FSBL), uses the minimum Tsallis entropy and constraints the reconstruction process, iteratively updates the phase error, and performs self-focusing to realize the initial phase correction of Bi-ISAR images. Simulation results show that the proposed algorithm has a fast convergence speed, strong robustness to noise, and high accuracy in reconstructing images.
The range resolution of inverse synthetic aperture radar (ISAR) imaging can be improved by directly increasing the bandwidth of the transmitted signal. However, it complicates the design of radar system hardware and increases the manufacturing cost. Aiming at solving the abovementioned problems, a multi-band ISAR fusion imaging method based on the multiple measurement vectors (MMV) model is proposed to improve the range resolution. First, a multi-band ISAR fusion imaging model based on the compressed sensing theory is established. Second, to improve the computational efficiency, a MMV accelerated improved linearized Bregman algorithm is proposed to solve the model. Nesterov’s acceleration gradient method and the condition number optimization of the sensing matrix are combined to further improve the iterative convergence speed. Finally, experimental results based on the simulation data and measured data verify the effectiveness and superiority of the proposed algorithm, which can achieve multi-band ISAR fusion imaging with higher imaging efficiency and better image quality.
The two-dimensional (2D) resolution is poor due to the narrow transmitting bandwidth and the limited observation angle in monostatic ISAR imaging. A multiradar fusion imaging method based on fast linearized Bregman iteration (FLBI) algorithm is proposed to improve the 2D resolution of the ISAR imaging. First, the sparsity of the ISAR imaging echo data is exploited to establish the multiradar fusion ISAR imaging model based on sparse representation, which can be converted into a one-dimensional sparse vector reconstruction problem. Then, a sparse reconstruction method based on FLBI is proposed to solve the sparse representation problem with large scales and achieve the ISAR fusion imaging. Combined with the weighted back-adding residual and condition number optimization of the sensing matrix, the FLBI algorithm can further accelerate the iterative convergence speed. The proposed algorithm only involves matrix–vector multiplications and componentwise shrinkages, which greatly improves the imaging efficiency. Finally, the simulation results show that the proposed method can effectively improve the iterative convergence speed and achieve the better 2D ISAR fusion imaging.
KEYWORDS: Image fusion, Signal to noise ratio, Reconstruction algorithms, Synthetic aperture radar, Radar imaging, Detection and tracking algorithms, Space based lasers, Bayesian inference, Image resolution
Images from high-resolution inverse synthetic aperture radar (ISAR) can provide more information about the targets. Multiband fusion imaging techniques can achieve higher range resolution without increasing hardware costs. A multiband fusion imaging algorithm based on variational Bayesian inference (VBI) is proposed to improve the range resolution of ISAR images. First, a multiband fusion ISAR imaging model is established based on sparse representation. Second, the scattering coefficients and noise are assumed to be the Laplacian scale mixture distribution and the complex Gaussian distribution, respectively. Finally, the fusion image is directly reconstructed in the complex domain by the VBI based on Laplace approximation method. The effectiveness and robustness of the proposed algorithm are verified by the experimental fusion results of one-dimensional signals and two-dimensional ISAR images.
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