Phase filtering is one of the core signal processing steps in interferometric synthetic aperture radar (InSAR). In recent years, InSAR phase filtering algorithms have evolved from traditional solutions to deep learning (DL) methods, significantly improving the processing efficiency. However, most DL-based phase filtering techniques originate from optical filtering methods, and these methods inevitably entail a tradeoff between noise suppression and detail preservation. To resolve this contradiction and fully take into account the characteristics of InSAR phase, a multiscale phase filtering network (MPFNet) based on multilook information fusion is proposed. First, the network adopts the multiscale structure to balance noise suppression and detail preservation, where the multiscale information is obtained through multilook interferograms of varying numbers of looks. Second, drawing on the mechanism of super-resolution, the network incorporates the residual feature distillation blocks (RFDBs) to restore the scale of interferograms. Finally, in response to the demand for complex phase filtering, a loss function based on cosine similarity is constructed, which avoids the discontinuity at$\pm \pi $affecting the filtering results. Computer simulation and experiments based on real InSAR data verified the effectiveness of the proposed method.

A ground-based radar is a potential technique for lunar surface imaging. However, due to the Earth’s rotation, the maximum azimuth resolution is limited for a single observation. To solve this problem, this letter analyzes the feasibility and performance of obtaining multiangle data from different observations and forms large virtual apertures through aperture synthesis. To ensure the quality of synthesized images, an observation baseline selection method is proposed based on the principle of spectrum continuity, specifying that for two noncontinuous observations, there should be a point on each of them whose target-to-radar vectors share the same direction. Besides, an aperture synthesis algorithm based on spectrum compression is proposed to eliminate spectrum aliasing. The effectiveness of the algorithm is verified via computer simulations and real data experiments. By forming a well-focused 500-m resolution image from two noncontinuous observations, the validation of the proposed algorithm has been proved.

Multistatic unmanned aerial vehicle synthetic aperture radar (MUAV-SAR) three-dimensional (3-D) imaging system suffers from the time and phase synchronization errors among multiple stations. The classical two-way direct wave pulse exchange synchronization method introduces the π-ambiguity phase error and causes limited time-phase synchronization accuracy with multiple system nodes, leading to 3-D images defocusing. An MUAV-SAR joint synchronization method based on multiple direct wave pulses exchange is proposed to solve the π-ambiguity problem robustly and improve the synchronization accuracy significantly. Firstly, the π-ambiguity phase error is estimated through the comparative calculation of delay-phase information extracted from direct wave pulses and the high estimated success probability (99.73%) of the π-ambiguity can be achieved through the noise smoothing. Secondly, the synchronization accuracy is improved, that is, the time and phase error are reduced to about √2/Nof the existing method by utilizing N stations information fusion to jointly process redundant information of direct wave pulses from multiple synchronization links. Finally, a four-station UAV SAR real data experiment verifies the effectiveness of the proposed approach.

Tomographic synthetic aperture radar (TomoSAR), as a 3-D imaging technique, is widely applied in urban mapping. In our previous work, a multi-master (MM) TomoSAR approach was proposed for the long-baseline observation configuration. This article focuses on the MM TomoSAR for a common observation configuration. The main contribution includes two aspects: theoretical complement and performance extension. For the theoretical complement, the theory of virtual difference coarray (VDCA) in array signal processing (ASP) is introduced to the MM TomoSAR. First, we prove that the MM TomoSAR processing is equivalent to constructing a VDCA in the elevation direction, which essentially explains its principle. Then, to solve the redundancy problem in MM TomoSAR, the equivalent VDCA in the MM model is constructed by selecting the elements from the covariance matrix of the observed signal. Finally, the improved MM TomoSAR processing method is proposed. For performance extension, it is demonstrated that the performance improvement of the MM model is extended from the original two aspects (sidelobe suppression and high estimation accuracy) to include the third aspect (aperture expansion, which improves elevation resolution). The computer simulation and experiment based on TerraSAR-X data verify the proposed method effectively.

Due to the advantage of flexible and rapid deployment, unmanned aerial vehicle (UAV)-based synthetic aperture radar (SAR) tomography (TomoSAR) is a promising technology in 3-D urban mapping. The long baseline is indispensable for P-band SAR systems to achieve high elevation resolution. It will introduce two problems. On the one hand, the unavoidable spatial decorrelation brings serious phase noise and sidelobes in 3-D imaging. On the other hand, the noticeable image distortion fails the image registration and the TomoSAR data stack (TDS) construction. Aiming at the above problems, this article proposes a multimaster (MM) TomoSAR approach via three main contributions. First, the traditional TomoSAR signal model is extended to the MM case to improve the number of baselines and the average image coherence of the TDS and suppress the sidelobes. Second, a short-baseline-recursion image registration method is proposed to achieve high-precision image registration. Third, a TDS optimization processing consisting of interferometric SAR (InSAR) phase screening and baseline sign reassignment is introduced. Moreover, a clustering-based outliers’ elimination method is also adopted to ensure the 3-D imaging quality. Computer simulation and long-baseline P-band UAV-SAR experiment validate the proposed approach.

Multifrequency (MF) interferometric synthetic aperture radar (InSAR) phase unwrapping (PU) technology is proposed for PU in steep terrains where the phase changes of adjacent pixels exceed the commonly required threshold of$\pi $. Traditional MF PU methods will fail in the case of extremely steep terrain such as artificial buildings due to insufficient quality of phase noise suppression (PNS). In this article, we propose a refined MF-InSAR PU method that can be robustly applied for extremely steep terrain PU via two main contributions. First, an additional steep edge extraction step is introduced for geological local PU window generation to prevent inaccurate PNS across the extracted steep edges. Second, the traditional linear phase model is extended to a nonlinear one for more accurate MF local fringe frequency estimation in PNS. The computer simulations and the real dual-frequency airborne experiment validate the presented approach.

The interferometric phases in highly sloped terrain have the characteristics of large fringe density, narrow width, low correlation, and under-sampling. The local fringe frequ- ency (LFF) is a criterion to evaluate the trend and magnitude of the local terrain gradient and can be employed to improve the quality of interferograms. The results of the traditional LFF estimation method can be affected by phase noise, and sometimes the phase unwrapping (PU) operation is also required for some local regions. When it comes to highly sloped terrain, the phenomenon of phase under-sampling may cause incorrectness in the absolute interferometric phase during the operation of PU and may then influence the accuracy of the whole estimation. In order to solve this problem, this letter proposes an extended maximum-likelihood method for LFF estimation based on the multifrequency interferometric synthetic aperture radar (InSAR) data. Through the differences in the LFF between the different frequency InSAR data, the estimation quality map is introduced to modify the large error in certain regions by local 2-D fitting and thus achieves a accurate estimation of LFF in highly sloped terrain. Finally, the estimated results of LFF are used to guide the process of phase filtering. Simulated data and real airborne dual-frequency InSAR data are both employed to validate this proposed method.