Recent years have seen a surging interest in several novel tools pertaining to SAR, such as multichannel high-resolution and wide-swath (HRWS) SAR, multibaseline interferometric SAR (InSAR), multisubband SAR, polarimetric SAR (PolSAR), and polarimetric SAR interferometry (PolInSAR). We believe that these new approaches to SAR have valuable scientific applications. Here, we present a new experimental airborne SAR system named “N-SAR” (SAR of the Nanjing Research Institute of Electronic Technology) that can fulfill new requirements and is scalable to allow rapid development of modern SARs. As a dual-antenna airborne SAR system, the N-SAR system will be used to test new technologies and signal processing algorithms such as PolSAR and PolInSAR, multibaseline SAR interferometry, multichannel multichannel HRWS SAR/InSAR, and SAR ground moving target indicator. It will play a key role in evaluating the performance of current engineering-oriented SAR systems by using several new operational modes for scientific purposes. In this paper, we provide a conceptual description of the general system design features, instrument design, and capabilities of the N-SAR system. To meet the requirements of different experiments, a novel operational mode, named the alternating bistatic multipolarized mode based on the N-SAR system, is presented. A series of flight tests that started in April 2017 and will be carried out over the next few years are shown. Several preliminary experimental results pertaining to the calibration of PolSAR, multichannel SAR imaging, and interferometry are presented in this paper as an early validation of the capabilities of the N-SAR system.

Traditional interferometric synthetic aperture radar (InSAR) denoising methods normally try to estimate the phase fringes directly from the noisy interferogram. Since the statistics of phase noise are more stable than the phase corresponding to complex terrain, it could be easier to estimate the phase noise. In this paper, phase noises rather than phase fringes are estimated first, and then they are subtracted from the noisy interferometric phase for denoising. The denoising convolutional neural network (DnCNN) is introduced to estimate phase noise and then a modified network called IPDnCNN is constructed for the problem. Based on the IPDnCNN, a novel interferometric phase noise reduction algorithm is proposed, which can reduce phase noise while protecting fringe edges and avoid the use of filter windows. Experimental results using simulated and real data are provided to demonstrate the effectiveness of the proposed method.

Phase noise reduction is one of the key steps for synthetic aperture radar interferometry data processing. In this article, a novel phase filtering method is proposed. The main innovation and contribution of this research is to 1) incorporate local fringe frequency (LFF) compensation technique into the nonlocal phase filtering method to include more independent and identically distributed samples for filtering; 2) modify the nonlocal phase filter from three aspects: 1) executing nonlocal filtering in the complex domain of the residual phase to avoid gray jumps in phase, 2) adaptively calculating the smoothing parameter based on the LFF and the coherence coefficient, and 3) using the integral image in similarity calculation to improve the efficiency; 3) perform Goldstein filter in high coherence areas to reduce the computation expense. Experiments based on both simulated and real data have shown that the proposed method has achieved a better performance in terms of both noise reduction and edge preservation than some existing phase filtering methods.

Polarimetric synthetic aperture radar (SAR) tomography (Pol-TomoSAR) can be used for global forest digital terrain model (DTM) and canopy height model (CHM) mapping with high spatial and temporal resolution at low economic cost. However, the performance of DTM and CHM inversion in current Pol-TomoSAR methods is often compromised when the ground-to-volume ratio (GVR) is low, which usually happens in complicated terrain where large negative slope angles are commonly present, or in dense tropical forest where the ground visibility is low due to strong attenuation by the dense vegetation layer. In this work, a novel method for the separation of ground and volume scattering, aiming at robust and accurate DTM and CHM inversion in dense tropical forest and complicated terrain, is proposed. By fully exploiting multibaseline polarimetric SAR data, the proposed method can perform a more effective separation of ground and volume scattering. Subsequently, by applying the SAR tomography technology on the separated ground and volume scattering, the proposed method can retrieve accurate DTM and CHM information even at low GVR areas. Numerical experiments conducted on both simulated data and P-band airborne F-SAR data show that, compared to the most commonly used two-component algebraic synthesis method, the proposed one has a much better performance in terms of separation of ground and volume scattering. Furthermore, the inversed DTM and CHM present better agreement with light detection and ranging (LiDAR) measurements, especially in large negative slope angle terrain where the GVR is rather low.

A ground moving target usually shifts its initial azimuth location in the synthetic aperture radar (SAR) image due to the radial motion. To address this problem, a new azimuth relocation method is proposed for a multichannel SAR in this letter. Based on the signal analysis in the range-compressed domain, it is found that the zeroth-order term of the interferometric phase without coregistration is determined by the moving target's initial azimuth location. A new azimuth relocation method utilizing the noncoregistrated interferometric phase is then proposed. First, the moving target is detected after clutter suppression in the range-compressed domain. Then, the interferometric phase along the slow time without coregistration can be extracted for parameter estimation via the least-squares method. Compared with the existing radial velocity-based relocation methods, the proposed method has higher accuracy, and there is no azimuth location ambiguity problem even when the target has a large radial velocity. Finally, both the simulated and real data are provided to demonstrate the effectiveness of the proposed method.