With the increase of urgent demand for the emergent distributed target (e.g., collaborative drone swarms, ships, and car group) detection in complicated environments, traditional adaptive detection methods face many challenges: 1) The distributed targets often appear as a set of multiple targets' scattering centers (SCs) in range-Doppler domain with unknown Doppler frequencies, dispersing the target energy and making it more difficult to detect effectively. 2) The electromagnetic interference and non-Gaussian clutter raise the false alarm of radar and/or reduce the detection probability of the targets. In this paper, we consider range-Doppler distributed target detection under structured interference and non-Gaussian clutter. To overcome the above challenges, we first assume that the targets' SCs are sparsely-distributed in the Doppler domain. Then, a sparse recovery method is utilized to extract the target component from the echo data. To mitigate the influence of interference, two strategies are adopted to design detectors: detecting while canceling interference strategy and interference canceling before detecting strategy. Moreover, the clutter is modeled as spherically invariant random vector with unknown texture component and an unknown covariance matrix (CM). A set of training data is adopted to estimate the CM. The computational complexity of the detectors is analyzed theoretically. Numerical experiments based on the simulated and measured data show that the proposed detectors outperform the existing ones.

To date, various methods have been proposed for calibrating polarimetric synthetic aperture radar (SAR) using distributed targets. Some studies have utilized the covariance matching estimation technique (Comet) for SAR data calibration. However, practical applications have revealed issues stemming from ill-conditioned problems due to the analytical solution in the iterative process. To tackle this challenge, an improved method called Comet IS is introduced. Firstly, we introduce an outlier detection mechanism which is based on the Quegan algorithm’s results. Next, we incorporate an initial search approach which is based on the interior point method for recalibration. With the outlier detection mechanism in place, the algorithm can recalibrate iteratively until the results are correct. Simulation experiments reveal that the improved algorithm outperforms the original one. Furthermore, we compare the improved method with Quegan and Ainsworth algorithms, demonstrating its superior performance in calibration. Furthermore, we validate our method’s advancement using real data and corner reflectors. Compared with the other two algorithms, the improved performance in crosstalk isolation and channel imbalance is significant. This research provides a more reliable and effective approach for polarimetric SAR calibration, which is significant for enhancing SAR imaging quality.