Taking the advantages of optical camera and synthetic aperture radar (SAR) to perform complementary imaging is a research hot spot in remote sensing image processing, which can be used for target recognition and detection after image registration and fusion. Optical/SAR images acquired by noncommon aperture system have temporal and spatial inconsistencies, there are also significant differences in imaging and radiation mechanism, which make the image-domain alignment process complicated, and the alignment effect and accuracy are also limited. Therefore, this thesis proposes to acquire synchronal optical/SAR heterogeneous data by optics/frequency separation technology under the airborne co-aperture system and puts forward a consistent imaging alignment method based on data domain, which unifies optical photographs and SAR imaging results into the same pixel coordinate system and then establishes the theoretical relationship between the pixel deviation of the two and the position deviation of SAR imaging, finally achieves consistent imaging alignment with higher resolution optical photographs as a reference through the deviation compensation during SAR imaging process. Experimental simulations verified the feasibility of achieving consistent imaging alignment of optical camera/SAR within one pixel in distance and two pixels in azimuth based on SAR imaging compensation, which opens up a new direction for the enhancement of imaging processing efficiency of heterogeneous data and high-precision image processing.

Synthetic aperture radar (SAR) plays a crucial role in remote sensing because of its ability to operate in all weather conditions, both day and night. The traditional FFT-based SAR imaging algorithm suffers from severe speckle noise, which is almost inevitable owing to the coherent nature of the SAR system. Recently, the plug-and-play (PnP) SAR imaging method uses a plug-in denoiser as an image prior function to regularize the resulting image, thus suppressing speckle noise while maintaining the useful features of target objects. However, the existing plug-in denoisers used in statistical SAR imaging, either handcrafted or data-driven, are insufficient for complex remote sensing scenarios. More powerful image priors, such as the deep generative model for unconditional image generation, would be a better alternative regularizer for statistical SAR imaging. However, the most powerful diffusion generative model lacks an explicit latent space for conditional optimization to be adopted for SAR imaging from received signals. We propose a novel SAR imaging method based on conditional generation of a diffusion model. In detail, we embed the maximum a posteriori (MAP) formulation of SAR imaging from the received signal as a conditional guidance for diffusion generation, which overcomes the lack of latent space shortage. Compared with these statistical methods, our proposed methods exhibit exceedingly high performance both on simulated experiments and returned data imaging from RadarSat SAR data.

Synthetic aperture radar (SAR) is a widely used active imaging system for remote sensing applications. However, traditional signal processing-based SAR imaging algorithms suffer from coherent speckle problems. Recently, statistical SAR imaging methods such as the FESAR model and plug-and-play (PnP) SAR imaging methods have been applied to suppress the speckle phenomenon. However, they are sometimes unstable and require an elaborate hyperparameter adjustment strategy during the iteration process. We propose extending PnP statistical imaging with Langevin sampling, called the Langevin-PnP algorithm. To construct the Langevin-PnP algorithm, we provide an in-depth analysis of the PnP framework and incorporate Langevin dynamics into its iteration trajectory. We also present a convergence guarantee for Langevin-PnP because the injected stochasticity affects the convergence condition under the law of probability. The experimental results showed that our proposed Langevin-PnP maintained the best performance over other statistical imaging methods, both in the simulated experiments and the RadarSat-SAR data experiments.

Airborne multichannel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) has drawn increasing attention in wide-area surveillance, reconnaissance, and traffic monitoring due to its short revisit times. In this article, a novel scheme for CSSAR-GMTI systems is proposed to offer high-resolution focusing of moving targets and enable efficient detection and accurate parameter estimation, which are ensured by integrating moving target imaging with a space–time adaptive processing (STAP) clutter suppression step. The presented scheme develops a target imaging algorithm that forms SAR images focused on a particular 2-D motion parameter to maximize target signal energy and recover its high resolution. Moreover, benefiting from the suitable phase compensation designed in the 2-D frequency domain, moving targets can finally be properly focused in the SAR image without displacement, and their parameters can be uniquely determined. Experiments on an emulated radar dataset are conducted to validate the effectiveness of the proposed scheme.

This letter focuses on the detection of subtle human activity, from multipass airborne interferometric synthetic aperture radar (InSAR) images, of such activities causing absolute decorrelation between two acquisitions. These activities can be vehicle track, footprint, grazing, and human construction. The millimetric sensitivity of the interferometric phase makes it valuable for detecting such activities. It can bring a large value to civil and military applications. However, there are always high false alarms in the conventional detection method. To solve this problem, a human activity detection method based on the coherence matrix is approached in this letter. In the final part of this letter, a preliminary validation of the method is provided by processing actual multipass airborne InSAR data stacks acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences. It is noted that the proposed method reaches reliable results in comparison with the ground truth.

Airborne multichannel high-resolution radar (HRR)-ground moving target indication (GMTI) is of great significance to wide-area surveillance, traffic monitoring and target recognition. The Aerospace Information Research Institute, Chinese Academy of Sciences, produced an advanced airborne digital array radar with high resolution and conducted an experiment with slow and weak cooperative moving targets in 2021. In this paper, an overall processing framework for target detection, parameter estimation and target tracking using this system is introduced. For HRR detection, the echo energy of targets is spread into multiple range units, so-called range-spread targets; thus, using detectors designed for point-like targets will severely degrade the detection performance, especially for slow and weak targets. To address the adaptive detection of such range-spread targets embedded in Gaussian clutter with an unknown covariance matrix, a novel two-step generalized space-time adaptive processing (GSTAP) algorithm is proposed, which offers an enhanced clutter suppression capability compared with natural competitors. Moreover, a tracking method is implemented in the range-Doppler domain to avoid track loss caused by azimuth relocation error and reject discrete false alarms. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method and provide a paradigm for further research.

Synthetic aperture radar (SAR) installed on unmanned aerial vehicles (UAVs) has drawn increasing attention in the area of remote sensing due to its high spatial resolution and low energy consumption. However, due to the sensitivity toward atmospheric turbulences, strong motion errors can defocus the image in both azimuth and range directions. In this article, based on symmetric triangular linear frequency-modulation continuous microwave (STLFMCW) signals, a novel motion error estimation method is proposed. The core concept is to eliminate the effect of residual range cell migration (RCM) and investigate the motion parameters from azimuth phase history by range-frequency-domain interferometry between the up-ramp and down-ramp chirp sections. With preknowledge of the structural characteristics of the differential signal, we first obtain the high-order component, then estimate the quadratic term with an azimuth profile width minimization (APWM) algorithm on the basis of bisection search, and subsequently remove the linear part through phase gradient matching and joining. Consequently, the desired motion errors can be achieved purely based on raw data. Simulations demonstrate that our proposed algorithm can achieve high accuracy with or without standard prominent point targets. Experimental results on a W-band UAV SAR system in strip-map mode indicate that this approach also outperforms other existing postprocessing autofocus methods.

The application of miniature unmanned aerial platforms has been growing in popularity whether in military reconnaissance or in civilian monitoring systems over the past few years. Installation of synthetic aperture radar (SAR) on board of unmanned aerial vehicle (UAV) is a high-efficient but low-cost remote sensing technology. However, UAV is sensitive to atmospheric turbulences and it may not carry high-accuracy inertial navigation systems (INS) and global positioning system (GPS). These make Mini SAR imagery a challenge, and motion compensation (MoCo) a crucial task. This paper is aimed at studying on the MoCo technology for Mini SAR mounted on UAV. Practical data processing is presented to primarily demonstrate the validity of our proposed approach.