Synthetic aperture radar (SAR) is vulnerable to interference, including intentional and unintentional-ones. Noise frequency modulated (FM) interference is a kind of intentional interference, which has the characteristics of broadband and randomness, which makes the noise FM signal become a kind of most commonly used interference signal. Noise FM interference will have a serious impact on the SAR image, but the current algorithms for interference suppression are not sufficiently studied. This paper extends a time-domain cancellation algorithm for suppressing the noise FM interference of SAR. This algorithm can reconstruct the noise FM interference signal from the contaminated SAR echo, and then suppress the interference component in the echo by time-domain cancellation. Finally, this paper validates the superior performance of the algorithm by point target simulation and Radarsat-1 data. The proposed method is valid even when the signal-to-interference ratio is lower than -40dB.

Super resolution technology has played a significant role in enhancing the imaging resolution of forward-looking scanning radar. However, a large number of super-resolution methods still rely on single frame scanning echoes. This paper aims to leverage multi-frame real beam images for super-resolution imaging, utilizing the complementary information present in multiple images to construct a higher resolution image. This paper first establishes the multi-frame super-resolution imaging model. Subsequently, a feasible multi-frame super-resolution method was proposed, and motion parameter estimation was performed using the correlated phase method. Finally, the effectiveness of the proposed method was verified through simulation experiments.

The signal emitted by ground radiation sources often interferes with Synthetic Aperture Radar (SAR) satellites, with the most common interference being periodic pulses emitted by ground radars. This paper proposes a method for locating ground-based periodic pulse signal interference sources using SAR echo data. Firstly, We estimate the Time Difference of Arrival (TDOA) of each pulse emitted by the interference source to SAR from the received SAR signals, and we seek the mapping relationship between the coordinates of the interference source (latitude and longitude) and the variations in TDOA. Using this mapping relationship, we achieve the localization of the interference source through a two-dimensional search method. The proposed method in this paper is highly versatile, applicable to single-station SAR satellites, multi-station SAR, and single-station SAR with multiple passes. It is also applicable regardless of the modulation form of the interference signal. Finally, the proposed TDOA-based localization method is experimentally validated for its accuracy based on GaoFen-3 satellite-borne SAR. The results demonstrate that the positioning error using two measurements from the satellite is only 3.708 km.

Since the start of the Sentinel-1 mission, numerous cases of severe image degradation caused by RFI have been reported, which puts forward an urgent need for RFI identification and mitigation. In this paper, an automatic RFI identification method is proposed based on a siamese-type deep convolutional neural network (Siam-CNN-RIM). The Siam-CNN-RIM can be served as a pre-processing step before RFI mitigation to identify whether an S-1 image is RFI-contaminated or not. Different from traditional RFI identification networks which only use a single image as input, an additional image in the repeat-pass time-series is also fed into the input of Siam-CNN-RIM as a reference. Both of the input images correspond to the same illuminated area, and pass through the same convolutional layer followed by an energy function, such that the different features caused by RFI can be extracted and the background terrain features can be ignored. This is beneficial for distinguishing the real RFI signatures and the similar terrain signatures that may cause false positives, and thus improving the RFI identification performance. Experimental results show that the proposed method is robust in different scenarios and can achieve more than 97% RFI identification accuracy, even for the open-set task where the test scenarios are not included in the training set.

Deconvolution technology can be utilized to improve the angular resolution of real-aperture scanning radar (RASR) with high efficiency and low cost. However, it is an ill-posed problem and the solution is sensitive to noise. Regularization methods are considered to be efficient ways to ease the noise sensitivity by absorbing the prior information into the objective function. In this paper, we propose a new super-resolution imaging method for RASA based on the Markov random field (MRF). Compared with the published angular super-resolution methods for RASA, the proposed method takes advantage of the two-dimensional spatial prior information and can recover the shape of scene much better. Simulations are carried out to demonstrate the effectiveness of the proposed method.

Autofocus is one of the key problems in inverse synthetic aperture radar (ISAR) since the noncooperation of the target motion. For sparse aperture ISAR, classical autofocus algorithms are not suitable due to the discontinuity of the azimuth sampling. In this paper, a novel framework is proposed for ISAR autofocus with sparse aperture. The autofocus problem is transformed into an optimization problem with l0norm and nonlocal total variation (NLTV) regularization constraints. Therefore, both spatial sparsity and structural information of the target can be considered in the process. Dual iterative computation which combines regularization method and conjugate gradient (CG) algorithm is applied to reconstruct the image and correct the phase error. Results of real data experiments show the effectiveness of the proposed method.