Permanent scatterer (PS) selection is a crucial step in the processing of ground-based interferometric synthetic aperture radar (GB-InSAR).Existing methods rely on amplitude stability, phase stability or high coherence between pixels to select PS. Amplitude stability and phase stability are sensitive to phase fluctuations and may not well represent phase errors in some cases. The methods based on high coherence can easily lead to false detections due to their reliance on local windows. To address these issues, this paper analyzes the differences in the distribution characteristics of interferometric phases between PS and non-PS in GB-InSAR. A new PS selection method based on Gaussian mixture model (GMM) is proposed. The method firstly selects enough PS as prior reference PS. Then, GMM is used to fit the probability distribution of the interferometric phases of the reference PS. Finally, the PS and non-PS are distinguished based on the matching degree between the interferometric phase series of all image pixels and the GMM. The results of measured data show that, comparing to the traditional methods based on amplitude and phase stability, when the number of obtained PS is close, the proposed method yields stronger coherence and higher degree of aggregating of phase series among the obtained PS. The PS obtained by GMM are with less residues than other methods. This demonstrates the method's ability to accurately select PS.

Due to its large bandwidth and high resolution in the range dimension, microwave photonic radar enables finer information extraction for important targets such as ships through inverse synthetic aperture radar (ISAR) imaging, which is crucial for maritime surveillance. However, under the ultra-high resolution characteristics, the three-dimensional spatially variant Doppler parameters caused by target rotation can lead to image defocusing. In two-dimensional echo domain imaging, regional compensation processing is necessary, but existing methods cannot achieve adaptive regional segmentation, making it difficult to achieve two-dimensional ultra-high resolution imaging. To address these issues, this paper proposes a high-precision microwave photonic ISAR imaging method based on spatially variant Doppler parameter clustering. Firstly, the echo model of microwave photonic ISAR ship target is established, and the three-dimensional spatial variability characteristics of Doppler parameters are derived. The necessity of two-dimensional regional compensation processing is analyzed. Then, by separating strong scattering points, adaptive estimation and interpolation of Doppler parameters, a mapping relationship between each scattering point of the target and the two-dimensional Doppler parameter is established. Clustering processing in the two-dimensional Doppler parameter domain is performed to achieve adaptive optimal segmentation of spatially variant Doppler parameters, laying the foundation for high-precision regional compensation processing. Finally, regional non-spatially variant two-dimensional phase consistency compensation processing is carried out to achieve ultra-high resolution imaging of microwave photonic ISAR. The effectiveness of this method is validated through simulation and experimental data processing.

In synthetic aperture radar (SAR) system, the three-dimensional rotation of ship targets in the presence of a medium and high sea state would lead to time-varying Doppler spectrum and image defocusing, which will adversely affect the subsequent information interpretation of ship targets in SAR images. Aiming at the refocusing problem of three-dimensional rotating ship targets, this paper proposes a SAR refocusing method for three-dimensional rotating ship target based on minimum entropy criterion and generative adversarial network, and designs the network structure of generator and discriminator. The generator transforms the defocused complex SAR ship image into range-Doppler domain, and estimates the phase error coefficient by range unit using phase error coefficient estimation network, and realizes the compensation of multi-order phase errors. The discriminator is composed of a complex-valued convolutional neural network, and all its elements, including convolution layer, activation function, feature mapping and parameters, are extended to the complex domain. The minimum entropy criterion and adversarial loss are introduced into the loss function to achieve unsupervised training and avoid the problem that it is difficult to obtain the target labeling samples of non-cooperative ships. Experiments on simulated data and Gaofen-3 data show that the proposed method achieves significant improvements in both refocusing accuracy and efficiency.

Inverse synthetic aperture radar (ISAR) sparse imaging methods can provide the imaging results with high image contrast and less sidelobe interference. The premise of sparse imaging is that the scatterers distribution of the scene or target to be imaged is sparse, which means that the final imaging quality is determined by the sparse feature of the target or scene to be imaged. The natural sparsity of ISAR target scenes emphasize point-like features, and the sparse representations of to be imaged target scene in the transform domains can enhance general features (e.g., texture or contour features) of the target. The well learned sparse transformation dictionaries can adapt to the to be imaged target scenes and find their unique sparse representations. However, the image patches oriented sparse representations ignore the geometric feature of target to be imaged. The nearest neighbor graph model is able to establish the geometric feature description operator of the given data, which can be used for describing the geometric feature information of the given data. In this paper, we introduce the nearest neighbor graph model (NNGM) into ISAR sparse imaging to express the geometric feature of the to be imaged target. The NNGM of the to be imaged target is then used as the regularization term and mapped to ISAR sparse imaging model. We propose an ISAR sparse imaging method combined with the NNGM for the imaging of different types of real ISAR data. Compared with the existing ISAR sparse imaging methods, the proposed imaging method can provide the imaging result with clearer contour, and the imaging time is reduced by 10.4% on average.

Terahertz synthetic aperture radar (SAR) exhibits broad application prospects due to its capability for high-resolution imaging and detailed target extraction. However, its short wavelength makes terahertz SAR extremely susceptible to the platform vibration, leading to many issues during the imaging process such as false imaging points, azimuthal blurring, and defocused SAR images. Therefore, this paper establishes a fine platform vibration model of terahertz SAR, and proposes an adaptive terahertz SAR motion compensation algorithm. Based on the impact mechanism analysis of the platform vibration on imaging using the mathematical model, the complex platform vibration in terahertz SAR imaging scenes can be compensated flexibly and accurately. Firstly, a fine terahertz SAR vibration model is established based on the temporal amplitude modulation vibration model (TAMVM). By integrating the cosine time-varying amplitude and the random time-varying amplitude modulation vibration model, the TAMVM model reduces the limitation of the traditional harmonic model, and improves the adaptability to the complex and variable terahertz SAR platform vibration. Secondly, to address the performance loss of traditional harmonic model-based motion compensation algorithms when handling the complex platform vibration, this paper proposes an adaptive motion compensation method based on the Levenberg-Marquardt (LM) algorithm under the minimum Tsallis entropy criterion. The image quality-driven motion compensation algorithm proposed in this paper does not rely on the dominant target points, and it can precisely estimate the complex and varying vibration phase under the nonlinear least squares framework without the additional compensation steps. Moreover, the iterative process of the LM algorithm is derived under the minimum Tsallis entropy criterion in this paper. This algorithm adaptively adjusts the search displacement to achieve the feedback update and the iterative optimization, enabling precise estimation of the vibration phase and suppression of image blur, thereby obtaining high-quality focused terahertz SAR images. Furthermore, the comparison results of the simulated and real-measured data verify the rationality and feasibility of the proposed TAMVM model, and demonstrate the superiority of the proposed adaptive motion compensation method in achieving the precise terahertz SAR image focusing and suppressing false imaging points.