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Recently, there are few reports on three-dimensional (3D) imaging method based on radar network. Aiming at the 3D imaging problem in radar network, a reconstruction method for 3D inverse synthetic aperture radar (ISAR) image based on radar network is proposed in this paper. The 2D ISAR image is considered as a geometric projection of a target on an imaging projection plane; thus, aerial points might be scatterers of targets if their geometric projection on an imaging projection plane is similar to the 2D ISAR image of the target. The proposed method makes use of the similarity between the ISAR Range-Doppler (RD) image and the target's geometric projection on the imaging projection plane to construct the 3D image reconstruction model. The reconstructed target 3D ISAR image is obtained by estimating the scattering centre position in 3D Cartesian space. In this paper, the projection formulas designed to calculate the geometric projection of the aerial points on the imaging projection plane are derived to first form the projection image. Depending on the similarity between the 2D ISAR image and the target's projection, the 3D ISAR image reconstruction model based on the radar network is constructed. Further, a corresponding algorithm for solving the 3D ISAR image reconstruction model is proposed. Finally, the radar distribution influence on reconstruction precision is analysed, and some experimental results are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

For radar network, each radar can obtain the two-dimensional (2D) inverse synthetic aperture radar (ISAR) image from the corresponding observation angle independently. Taking advantage of the multi-view observation via radar network and the projection relationship in ISAR imaging, the three-dimensional (3D) image of the target can be reconstructed by the inverse-projection principle. However, it is hard to reconstruct the scattering position and coefficient simultaneously, and the network optimization should be studied to improve the reconstruction performance. To solve these problems, a novel 3D scattering image reconstruction method is proposed in this paper. Firstly, the 3D reconstruction model is built as a compressed sensing (CS) framework. Then, the network optimization for single target is transformed into the measurement matrix optimization before the 3D scattering recovery. Finally, numerical simulations under the noise scenarios and the principle prototype experiment on real data are shown to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

A three-dimensional (3D) image reconstruction method for the spinning target based on the radar network is proposed in this letter and the design concept is sourced from the similarity of the two-dimensional (2D) imaging result with the target’s mapping on the imaging plane. The proposed method has the capacity to generate 3D image of the spinning target by reconstructing the scattering distribution in 3D Cartesian space. Firstly, the inverse Radon transform is applied to obtain the imaging result of the spinning target and the mapping formulas for mapping the air points onto the 2D image plane are derived. In addition, the radar network 3D image reconstruction model for the spinning target is constructed and the corresponding algorithm for solving the reconstruction model is proposed. Finally, some experiment results are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A high-resolution three-dimensional (3D) image reconstruction method for a spinning target is proposed in this paper and the anisotropy is overcome by fusing different observation information acquired from the radar network. The proposed method will reconstruct the 3D scattering distribution, and the mapping of the reconstructed 3D image onto the imaging plane is identical to the two-dimensional (2D) imaging result. At first, the range compression and inverse radon transform is employed to produce the 2D image of the spinning target. In addition, the process of mapping the spinning target onto the imaging plane is analyzed and the mapping formulas which are to map the point onto the 2D image plane are derived. After the micro-Doppler signature about which every reconstructed point in 2D imaging result is extracted by the Radon transform, the extended Hough transform is adopted to calculate an important parameter about the micro-Doppler signature, and the 3D image reconstruction model for the spinning target is constructed based on the radar network. Finally, the algorithm for solving the reconstruction model is proposed and the 3D image of the spinning target is obtained. Some simulation results are given to illustrate the effectiveness of the proposed method, and results show that the mean square error (MSE) relatively holds a steady trend when the signal-to-noise ratio (SNR) is higher than −10 dB and the MSE of the reconstructed 3D target image is less than 0.15 when SNR is at the level of −10 dB. [ABSTRACT FROM AUTHOR]