Aiming at improving the performance of sparse aperture inverse synthetic aperture radar (ISAR) imaging under the condition of phase error, an efficient structural sparse imaging algorithm with joint phase autofocusing is proposed in this letter. First, an azimuth sparse ISAR imaging model containing phase error is constructed. To fully utilize the structural sparse characteristics of the imaging target, the above imaging model is further transformed into an$\boldsymbol {l}_{1}$norm optimization problem using structural weighting. Second, leveraging fast iterative shrinkagethresholding algorithm (FISTA), the phase error estimation and the target structure weight updating are integrated into the image reconstruction framework. By solving this compound optimization problem iteratively, the final high-resolution ISAR imaging results are obtained. Finally, the experimental results of the measured data show that the proposed algorithm can achieve well-focused image efficiently under phase error conditions and has a remarkable imaging performance under low signal-to-noise ratio (SNR) and sparse aperture conditions due to the utilization of the sparse structure of the target.

Compared to conventional microwave regime radars, microwave photonic (MWP) radar is capable of transmitting extremely large bandwidth signals, wherein the frequencies of such signals distribute across multiple bands. In practical applications, the large bandwidth of MWP radar may be split into multiple discrete sub-bands due to various considerations such as anti-jamming, resource-saving, communication band avoidance, etc. Nonetheless, it leads to the fact that MWP radar suffers from the challenging problems of side-lobes elevation and main-lobes broadening. These problems will affect the image quality seriously. In order to address this issue, a spectrum recovery algorithm based on an improved Truncated Schatten-pNorm and Sparse Regularizer-Alternating Direction Method of Multipliers (TSPN-ADMM) network is proposed in this paper. This algorithm can efficiently recover the lost spectrum in MWP radar applications and further improve the imaging quality of the MWP radar. In the lost spectrum recovery problem, the parameters of the recovery algorithm directly determine the recovery performance. The different forms of lost spectrum possessed by MWP radar make the selection of parameters for the spectrum recovery algorithm extremely difficult. As a consequence, in this paper, the spectrum recovery problem for MWP radar can be reformulated into a matrix completion problem by exploiting its joint sparsity and low-rankness. Based on the traditional TSPN-ADMM algorithm, an improved TSPN-ADMM-Net approach is proposed by utilizing the algorithm unrolling technique, wherein the hyperparameters in TSPN-ADMM algorithm are optimized in an end-to-end training manner. Consequently, the algorithm proposed in this paper can achieve excellent recovery results when dealing with the multiple spectrum missing situations existing in MWP radar. The effectiveness of the algorithm is verified by a combination of numerical simulations and actual MWP radar data.

Along-track multichannel synthetic aperture radar is usually used to achieve ground moving target detection and imaging. Nevertheless, there is a design dilemma between azimuth high resolution and wide swath (HRWS). To solve this problem in HRWS mode, we introduce a virtual multichannel (VMC) scheme. For each virtual channel, the low real pulse repetition frequency (PRF) improves the ability of resolving range ambiguity for wide-swath, and the high virtual PRF improves the capability of resolving Doppler ambiguity for azimuth high resolution. For multiple virtual channels, strong ground clutter is eliminated by the joint VMC processing. Furthermore, a detailed signal model of a moving target in the virtual channel is given, and the special false-peak effect in the azimuthal image is analyzed. Moreover, we propose a novel ground moving target processing method based on the VMC scheme and the clutter suppression interferometry (CSI) technique, which is called VMC-CSI. The integration of detection, location, velocity estimation, and imaging for ground moving targets can be achieved. Accounting for the unresolved main peak and false peak for a moving target, in the VMC-CSI method, we adopt a two-step scheme to estimate the radial velocity and along-track velocity, namely, rough estimation and precise estimation. Meanwhile, considering the same interferometric phases of the main peak and the false peak, we use false peaks first for the robustness of initial azimuth location estimation and remove false peaks afterward. Numerical simulations are provided for testing the effect of the false peak and the effectiveness of VMC-CSI.

The 3-D inverse synthetic aperture radar imaging fast maneuvering targets is an active research area in the past decades. Planar antenna arrays are used to avoid the motion compensation algorithms but at the cost of increasing the hardware complexity. In this letter, to reduce the hardware complexity of the imaging system, a wideband multiple-input multiple-output system with two perpendicular linear arrays is suggested. In contrast to the existing 3-D imaging methods with planar antenna arrays, with the proposed method, the lower hardware complexity is achieved by many additive virtual array elements. Simulations based on synthetic data are provided for testing the proposed method.

Imaging a fast maneuvering target has been an active research area in past decades. Usually, an array antenna with multiple elements is implemented to avoid the motion compensations involved in the inverse synthetic aperture radar (ISAR) imaging. Nevertheless, there is a price dilemma due to the high level of hardware complexity compared to complex algorithm implemented in the ISAR imaging system with only one antenna. In this paper, a wideband multiple-input multiple-output (MIMO) radar system with two distributed arrays is proposed to reduce the hardware complexity of the system. Furthermore, the system model, the equivalent array production method and the imaging procedure are presented. As compared with the classical real aperture radar (RAR) imaging system, there is a very important contribution in our method that the lower hardware complexity can be involved in the imaging system since many additive virtual array elements can be obtained. Numerical simulations are provided for testing our system and imaging method.

This paper presents a system model and method for the 2-D imaging application via a narrowband multiple-input multiple-output (MIMO) radar system with two perpendicular linear arrays. Furthermore, the imaging formulation for our method is developed through a Fourier integral processing, and the parameters of antenna array including the cross-range resolution, required size, and sampling interval are also examined. Different from the spatial sequential procedure sampling the scattered echoes during multiple snapshot illuminations in inverse synthetic aperture radar (ISAR) imaging, the proposed method utilizes a spatial parallel procedure to sample the scattered echoes during a single snapshot illumination. Consequently, the complex motion compensation in ISAR imaging can be avoided. Moreover, in our array configuration, multiple narrowband spectrum-shared waveforms coded with orthogonal polyphase sequences are employed. The mainlobes of the compressed echoes from the different filter band could be located in the same range bin, and thus, the range alignment in classical ISAR imaging is not necessary. Numerical simulations based on synthetic data are provided for testing our proposed method.