In view of the issue of high false alarms and missed detections in traditional target detection methods under rainy clutter environments, this paper primarily investigates the joint fractal characteristics of rain clutter spectra and their application in target detection. We propose a joint fractal feature detection method based on the directional blanket covering method has been proposed. Firstly, the fractal dimension and model fitting error features of the echo′s distanceDoppler domain are measured using the blanket covering method. Subsequently, these fractal dimension and model fitting error features are employed as verification statistics to construct a threshold-based detection method with combined features. By optimizing the computational steps of the blanket method, redundant calculations on non-target information are reduced, thereby enhancing the real-time performance of the method. Based on the processing results of the measured data in rainy and cluttered environments, the method demonstrates a significant reduction in false alarms and an improved detection performance for targets compared to traditional target detection algorithms when handling non-stationary data such as rain clutter.