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Anti-jamming technology holds an important position in the field of radar research. The main-lobe jamming has emerged as a significant threat to radar system performance. To address the imperative need for radar main-lobe jamming suppression, this article proposes a cascaded least mean square (LMS) jamming suppression method. Compared with the traditional fast-time LMS filtering method, the proposed method can offer notable enhancements in both signal-to-interference-plus-noise ratio (SINR) and signal-to-noise ratio (SNR). This article also employs a low-cost receiver array to carry out the main-lobe jamming suppression verification. The results of the experiment prove the effectiveness of the proposed method. Moreover, the utilization of the low-cost and lightweight receiving array can be applied to other jamming suppression processes, thereby providing an alternative technical strategy for main-lobe jamming suppression.

Highly accurate target angle estimation is essential for target tracking. In closely spaced scenarios, when multiple targets are close to each other, their measurement points will be located in the same range-azimuth cell. In particular, under wideband radar conditions, the target is expanded into multiple scattering points. When multiple extended targets are located close to each other in space, there may be multiple target scattering points falling within the same range-azimuth cell. In this case, there is a large bias in the estimated angle of each extended target, which affects the accuracy of the estimated angle of the closely spaced extended target. To solve this problem, this article proposes a closely spaced extended target angle estimation method under multiple observations. The proposed method utilizes the principle of invariance of extended target scattering characteristics between adjacent pulses to predict the scattering of extended target echoes and employs maximum likelihood estimation (MLE) to estimate the extended target angle. The effectiveness and feasibility of the proposed method are evaluated through experimental simulation.

Recently, human respiration detection using microwave radars has attracted significant interest as a noninvasive and noncontact form of measurement. Most prior research has focused on respiration detection of the subjects who are stationary or in random body movement. So far, few reported studies have been carried out on detecting the subjects in large-scale body movement. In this article, we present a tracking-aided method, which integrates the Kalman filter (KF)-based tracking method and a range-spread human subject model, to detect the subjects moving across multiple range bins. The experimental results validate the correctness of the range-spread model and the effectiveness of the proposed method.