As the electromagnetic environment becomes increasingly complex, a synthetic aperture radar (SAR) system with wideband active transmission and reception is vulnerable to interference from devices at the same frequency. SAR interference detection using the transform domain has become a research hotspot in recent years. However, existing transform domain interference detection methods exhibit unsatisfactory performance in complex interference environments. Moreover, most of them rely on label information, while existing publicly available interference datasets are limited. To solve these problems, this paper proposes an SAR unsupervised interference detection model that combines Canny edge detection with vision transformer (CEVIT). Using a time–frequency spectrogram as input, CEVIT realizes interference detection in complex interference environments with multi-interference and multiple types of interference by means of a feature extraction module and a detection head module. To validate the performance of the proposed model, experiments are conducted on airborne SAR interference simulation data and Sentinel-1 real interference data. The experimental results show that, compared with the other object detection models, CEVIT has the best interference detection performance in a complex interference environment, and the key evaluation indexes (e.g., Recall and F1-score) are improved by nearly 20%. The detection results on the real interfered echo data have a Recall that reaches 0.8722 and an F1-score that reaches 0.9115, which are much better than those of the compared methods, and the results also indicate that the proposed model achieves good detection performance with a fast detection speed in complex interference environments, which has certain practical application value in the interference detection problem of the SAR system.

With the rapid development of low-earth-orbit (LEO) internet satellite constellations, LEO satellites are becoming promising illuminators of opportunity for passive radar. However, the moving satellite platform results in a shifted Doppler frequency and increased Doppler spread of the clutter, leading to decreased clutter suppression performance. In this paper, the clutter model for a LEO satellite-based passive radar is analyzed. Based on the properties of the clutter, a Doppler-frequency-shifted normalized least mean square (LMS) filter is proposed to suppress the clutter. Furthermore, an efficient block adaptive method is introduced for fast implementation. Moreover, a Butterworth filter is designed to filter out the residual clutter. Simulations demonstrate the effectiveness of the proposed method.