The next generation of synthetic aperture radar (SAR) systems will need transmit and receive analog signals over a wide frequency range with large bandwidth to fulfil the increasing demands. Since the performance of the digital microwave component is deteriorated at increasing frequency, radio frequency (RF) front-end with the capability of frequency conversion is needed for frequency beyond a few gigahertz. However, for the widely employed RF mixers, the inherent imperfect of I-V characteristics of nonlinear devices, generates many undesired mixing spurs which restricts on the instantaneous bandwidth and the spurious-free dynamic range (SFDR). At Aerospace Information Research Institute Chinese Academy of Sciences, a photonic-assisted front-end with functions of signal generation and deramp processing has been conceived and implemented in an experimental deramp-on-receive CW SAR system. The system is called PFDIR (Photonic-Assisted Front-End Deramp-on-Receive Imaging Radar) and is envisaged to obtain SAR imaging at high resolution. In current stage, the Ku-band system with a bandwidth of 5.72 GHz, corresponding to a relative bandwidth of 37.8%, operates well with a fixed two-horn antenna. The configuration of the photonic front-end and the system design are described. Ground-based tests and airborne imaging results with geometrical resolution down to 5 cm are presented.

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.

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 combination of synthetic aperture radar (SAR) with unmanned aerial vehicles (UAVs) is attractive for acquiring high-resolution images without the restrictions of time and weather. However, because UAVs are lightweight and small in size, the UAV SAR system is very sensitive to turbulence, resulting in serious trajectory deviations and remaining residual motion errors (RMEs), such as residual range cell migration (RCM) and aperture phase errors (APEs). In this paper, a novel motion compensation (MoCo) strategy is developed based on the symmetric triangle linear frequency modulated continuous wave (STLFMCW) signal. The STLFMCW signal model is first presented, which reveals the relevance between the positive-negative frequency modulations and motion errors. The residual RCMs and relative phase errors are estimated directly through the phase differential interferometry of up-ramp and down-ramp chirp signals, instead of the traditional approach of relying on the range-shift gradient of adjacent range profiles. The proposed approach is independent of conventional prominent point processing (PPP) and calculates the range deviation to avoid the accumulation of errors, thereby achieving high precision and efficiency. Experiments based on simulated and measured data sets validate the proposed approach.

The post-Doppler adaptive matched filter (PD-AMF) with constant false alarm rate (CFAR) property was developed for adaptive detection of moving targets, which is a standardized version of the post-Doppler space–time adaptive processing (PD-STAP) in practical applications. However, its detection performance is severely constrained by the training data, especially in a dense signal environment. Improper training data and contamination of moving target signals remarkably degrade the performance of disturbance suppression and result in target cancellation by self-whitening. To address these issues, a novel post-Doppler parametric adaptive matched filter (PD-PAMF) detector is proposed in the range-Doppler domain. Specifically, the detector is introduced via the post-Doppler matched filter (PD-MF) and the lower-diagonal-upper (LDU) decomposition of the disturbance covariance matrix, and the disturbance signals of the spatial sequence are modelled as an auto-regressive (AR) process for filtering. The purpose of detecting ground moving targets as well as for estimating their geographical positions and line-of-sight velocities is achieved when the disturbance is suppressed. The PD-PAMF is able to reach higher performances by using only a smaller training data size. More importantly, it is tolerant to moving target signals contained in the training data. The PD-PAMF also has a lower computational complexity. Numerical results are presented to demonstrate the effectiveness of the proposed detector.