Phase autofocus is a significant step in translational motion compensation for inverse synthetic aperture radar (ISAR) imaging. Among the existing autofocus methods, contrast maximization-based algorithms are superior in accuracy and robustness. However, the existing maximum contrast autofocus methods are parametric, which significantly affects their convergence speed and, in some cases, limits their applicability. In this paper, a novel non-parametric maximum contrast ISAR autofocus algorithm based on Newton's method is proposed to inherit the high level of accuracy and robustness of contrast-based algorithms and, at the same time, achieve faster convergence. The simplified Newton's method, the modified Newton's method, and the secant method are used to obtain a higher computational efficiency. The proposed method is then compared to a well-established non-parametric method, namely the minimum entropy autofocus method. It will be shown that the proposed method can improve the computational efficiency by a factor from 5 to 10 times in typical scenarios. Both simulated and real data will be used to test the proposed algorithm, particularly in terms of computational effectiveness.

The latest progress of the multiple‐input multiple‐output (MIMO) radar system developed for small drones detection at Beijing Institute of Technology is presented herein. A low‐cost S‐band MIMO scanning radar system is designed for the detection of small drones. A practical design optimisation and implementation of a sparse array covering the whole airspace, and a target‐based MIMO radar array calibration method to improve the signal‐to‐noise ratio of the target are proposed. An experiment in the microwave chamber was conducted to verify the designed sparse‐array beam‐pattern, and measurement campaigns for small drones were performed to test the system performance where the radar achieved detection and tracking of a small DJI‐Phantom 4 drone at a range of up to 5 km. Target features, including micro‐Doppler and radar cross‐section signatures were extracted to assist target classification. The experimental results indicate that the system has promising capability in small drones detection and tracking, despite the challenges of low dwell time on targets due to the scanning nature of the system.

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 paper 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 paper also employs a low-cost receiver array to carry out the main-lobe jamming suppression verification. Results of the experiment prove 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.

The popularisation of unmanned aerial vehicles (UAVs) and the security threats that follow have made the detection of low‐slow‐small (LSS) targets a hotspot in the radar field. For ground‐based surveillance radars (GSR), the folded‐clutter is an important factor affecting LSS target detection performance. Herein, the stepped‐frequency (SF) signal is used to improve the performance of the folded‐clutter suppression and the LSS target detection for the GSR. Furthermore, an optimisation method for the SF signal parameter design is proposed to maximise the folded‐clutter improvement factor. Specifically, signal parameters including the frequency step number and the step sequence are optimised based on the clutter cognition results. To verify the effectiveness of the well‐designed SF signals in the LSS target detection, simulation experiments and field tests using an L‐band SF‐GSR and a DJI M600‐Pro UAV are conducted. Compared to the results achieved by a simple chirp signal, the detection probabilities of the LSS target with different velocities are significantly increased when the SF signals are used in a typical strong folded‐clutter background.