The combination of microwave photonic (MWP) technology and synthetic aperture radar (SAR) facilitates the generation, transmission, and reception of ultra-wideband (UWB) signals, thereby enabling the production of centimeter-resolution SAR images. Recently, an experimental MWP SAR system with a bandwidth of 14 GHz has been constructed at Aerospace Information Research Institute, Chinese Academy of Sciences, which has been tested by outfield SAR experiments. In the context of this system, we analyzed the manifestation of systematical errors in the coarse-focused image and found that deviations occur in both the envelope and the phase of the range cell migration (RCM) curves, which can be considered as inherent slant range error and center frequency error. In addition, the inclusion of a radio frequency power amplifier (PA) in this system can introduce frequency-dependent error. In this article, we analyze the sources and establish signal models for the above three types of systematical errors individually and propose a systematical error estimation method by exploiting the trihedral reflectors (TRCs) within the coarse-focused image. Utilizing the error estimation results and the measured frequency response of the PA, a post-processing compensation method is also proposed. The estimation and compensation method is applied to the processing of the vehicle-borne data acquired by this experimental MWP SAR system, yielding superior performance and a fine-focused SAR image with a resolution of approximately 0.013 m in the range dimension.

With the advancement of Microwave Photonic (MWP) synthetic aperture radar (SAR) technology, resolution has increased to 0.02 m, and platforms have expanded from airborne to vehicle-borne. Incorporating ultra-wideband, long synthetic aperture, and varied observation ranges presents two primary challenges for MWP SAR imaging: 1) The enhancement of two-dimensional (2-D) resolution renders the imaging process more susceptible to 2-D space-variant motion errors (SVMEs). 2) The expansion of application platforms, particularly close-range observation by vehicle-borne platforms, invalidates traditional imaging algorithms based on the far-field assumption. To address the challenges, a novel time-domain processing framework is proposed for both airborne and vehicle-borne MWP SAR systems. Firstly, we analyzes wavenumber spectrum resampling during the back-projection (BP) process, establishing a mapping relationship between phase errors in image and time domain. This allows for the estimation of trajectory deviation, enabling a rough estimation of the 2-D SVME. Subsequently, a motion compensation (MoCo) method, based on an overlapping sub-image configuration combined with fast ground Cartesian BPA (GCBPA), is introduced to enable imaging. This method solves the problem that MoCo method cannot be integrated with fast GCBPA. In the third stage, the relationship between azimuth phase error (APE) and 2-D phase error is established. Leveraging this relationship, a 2-D wavenumber domain autofocus method is developed to concurrently compensate for APE and nonsystematic range cell migration (NsRCM). Experimental validations on both airborne (0.03m) and vehicle-borne (0.02m) MWP SAR platforms data confirm the effectiveness and versatility of the proposed time-domain processing framework.

The combination of microwave photonic (MWP) technology and synthetic aperture radar (SAR) can realize ultra-wideband transmitted signals and thus provides ultra-high-resolution. Recently, an experimental MWP SAR system has been implemented at Aerospace Information Research Institute, Chinese Academy of Sciences. It can generate transmitting signals up to 14 GHz bandwidth and adopts dechirp-on-receive method at the receiving end. However, the power amplifier (PA) in front of the transmitting antenna induces an additional frequency-dependent error that needs to be compensated during signal processing. In this paper, we compensate for this specific systematical error utilizing the residual video phase (RVP) removal technique. Some simulated and experimental data processing results are presented to validate the effectiveness of the method.

Microwave Photonic Synthetic Aperture Radar (MWP-SAR) holds significant promise for applications in Earth remote sensing owing to its exceptional imaging resolution. This radar technology emits ultra-wideband signals surpassing those of conventional radar systems. Hence, MWP-SAR exhibits the potential to generate pseudo-color images by exploiting scattering differences, thereby augmenting the information acquisition capabilities of MWP-SAR. This paper introduces a methodology for synthesizing pseudo-color images while preserving the high resolution of MWP-SAR. The proposed algorithm employs an optimization technique to identify subband echo channels exhibiting the most significant differences in scattering characteristics. Simultaneously, to safeguard the resolution of MWP-SAR, a fusion model is devised to integrate the full-resolution Synthetic Aperture Radar (SAR) image with the multi-subband image. Finally, a full-resolution pseudo-color image was successfully synthesized from the measured airborne MWP-SAR data.

The motion error of airborne microwave photonic synthetic aperture radar (SAR) has 2-D spatial variation characteristics, and the range spatial variant motion error (RVE) and azimuth spatial variant motion error (AVE) significantly interplay during the motion error estimation. For the RVE estimation, the standard weighted least square (WLS) algorithms are susceptible to the AVE and the moving targets. In addition, the azimuth subimage-based WLS algorithms face the problem of dominant points decreasing dramatically. This letter proposes a WLS estimation kernel based on nonlinear chirp scaling (NCS) to address the issues above. The AVE is first significantly corrected by the NCS processing, and RVE is subsequently estimated using the standard WLS kernel. In addition to eliminating the adverse effects of AVE and moving targets, the proposed method can retain sufficient dominant points to ensure the accuracy of phase gradient autofocus (PGA). The measured data processing results verify the effectiveness of the proposed method.

High-resolution synthetic aperture radar (HR-SAR) is extensively used in ground remote sensing applications, including disaster monitoring and resource crop assessment. This is attributed to its exceptional high-resolution imaging capabilities. Microwave photonics technology plays a crucial role in enhancing the performance of SAR systems. It enables the direct emission of ultra-wideband signals across multiple frequency bands. However, this advancement also makes microwave photonic synthetic aperture radar (MWP-SAR) susceptible to complex and diverse electromagnetic interference. Particularly, it is vulnerable to radio frequency interference (RFI), and this interference seriously affects the high-resolution imaging results. In order to solve these problems, an MWP-SAR imaging algorithm based on depth spectral sensing and spectral reconstruction that is suitable for complex electromagnetic environments is proposed. Simultaneously, a sensing-removing-recovering (SRR) anti-interference imaging framework is established. To address the high-precision detection of various unknown interferences in MWP-SAR echoes, a deep learning network is constructed. This network is based on spectrum sensing theory and achieves an interference detection probability greater than 98% in various interference scenarios. Subsequently, targeting the continuous spectrum loss in the MWP-SAR signal after interference removal, a signal reconstruction algorithm is proposed. This algorithm employs Toeplitz transformation for signal loss scenarios, facilitating the recovery of signals with extensive spectrum loss post-interference removal. The factor group sparse regularization (FGSR) algorithm is used to quickly solve the signal recovery problem. Through simulation and measured data processing, the superiority of the proposed algorithm over existing interference suppression imaging algorithms is demonstrated.

Airborne synthetic aperture radar (SAR) with an imaging resolution of up to 0.03 m is developed. However, the imaging process suffers from motion errors with 2-D spatial-variant characteristics that invalidate approximations suitable for motion compensation (MOCO) in a submeter resolution SAR system. To estimate and compensate for 2-D spatial-variant motion error (2-D SVME), we propose a novel two-stage processing framework for the ultrahigh-resolution microwave photonic (UHR MWP) airborne SAR imaging. In the first stage, the two-step MOCO compensates for the spatial-invariant and range-variant motion errors. Range downsampling and azimuth windowing are adopted to increase the robustness of the method. Afterward, the coupling of the 2-D SVME is greatly decreased, and a coarse-focused image is obtained. In stage two, an extended autofocusing method in the 2-D wavenumber domain based on the extended range migration algorithm (ERMA) compensates for the azimuth-variant motion errors and nonsystematic range cell migration (NsRCM) for 2-D wide-swath stripmap SAR data. After the ERMA and obtaining the coarse-focused image, the analytical structure of the residual 2-D phase error in the wavenumber domain is revealed. A nonlinear scaling equation is developed, thus relating the 1-D azimuth phase error to the 2-D phase error correction. The Ku-band stripmap UHR MWP (0.03 m) airborne SAR data are analyzed to verify the necessity and effectiveness of the proposed framework. A well-focused stripmap SAR image is obtained.

Microwave photonic synthetic aperture radar (MWP-SAR) offers a larger signal bandwidth than conventional SAR, and its theoretical resolution can be improved to centimeter level. To achieve same order of magnitude resolution in azimuth direction, long synthetic aperture is always required, which results in extremely high requirements for the accuracy of imaging procedure. Compared with conventional SAR imaging algorithms, two major problems should be considered: 1) The scattering characteristics of target might vary from the frequency of signal and the angle of incidence, which seriously affects the coherence of received echo. 2) The imaging of MWP-SAR system is more sensitive to motion errors and therefore requires higher accuracy of motion compensation processing. To solve the above issues, a high-precision imaging method for MWP-SAR is proposed. First, based on the attribute scattering center(ASC) model, this paper analyzes the target-scattering characteristics with different frequencies and incident angles. Then, according to the influence of scattering phase on MWP-SAR imaging, an optimal sub-aperture division algorithm is proposed to guarantee the coherence of each sub-aperture data and better imaging results. Furthermore, to compensate for the effects of high-order motion errors, this paper proposes a motion error estimation algorithm based on sub-image registration, where the full aperture high-order motion errors can be divided into multiple linear components, and the flight trajectory of platform can be accurately reconstructed. Finally, a full-aperture time-domain high-precision imaging method is presented, and the effectiveness of the proposed formation is verified by both simulation and actual airborne MWP-SAR data processing.

With the exploration of synthetic aperture radar (SAR), the requirements for its functionality, precision, and response time are inevitably increasing, in which the technical core is the generation, reception, and processing of wideband signals with low time cost. Owing to the excellent performance of modern photonics, such as the ultra-wide bandwidth (UWB), flat response, low transmission loss, fast analog signal processing, and microwave photonics (MWP) promises to be an appropriate solution to improve the capability of SAR in resolution, coverage, and efficiency. However, on the one hand, due to the high sensitivity of optical fiber, moisture, temperature, or physical vibration can produce an unknown extra propagation delay. On the other hand, the wavelength shift effect should be taken into consideration for the UWB system. Thereby, under very high resolution (VHR) circumstance, two-dimensional (2-D) defocus including migration through resolution cells (MTRC) and azimuth phase error (APE) becomes a challenge for MWP SAR imaging. Unfortunately, existing 2-D autofocus approaches concerning motion errors inherently fail for the commonly underlying assumption that the prominent nonsystematic residual range cell migration (RCM) is global in the azimuth time domain. In this article, we analyze the effects of the above-mentioned negative factors in the image processing of range migration algorithm (RMA) and reveal the structural characteristics of the 2-D phase errors. A novel two-step postprocessing compensation strategy is developed, and experiments on real data acquired by an airborne MWP SAR system demonstrate its effectiveness.