Ground-based synthetic aperture radar (GB-SAR) has become an important technique for remote sensing deformation monitoring. However, most of the existing GB-SAR systems realize synthetic aperture by exploiting two closely spaced horn antennas to move along a linear rail. In order to obtain higher data acquisition efficiency and a wider view angle, we introduce arc antenna array technology into the GB-SAR system, which realizes a novel kind of system: ground-based arc array SAR (GB-AA-SAR). In this paper, we analyze arc observation geometry and derive analytic expressions of sampling criteria. Then, we propose a novel wavenumber domain imaging algorithm for GB-AA-SAR, which can achieve high image reconstruction precision through numerical solutions in the wavenumber domain. The proposed algorithm can be applied in wide azimuth view angle scenarios, and the problem of azimuth mismatch caused by distance approximation in arc geometric efficient omega-k imaging can be solved successfully. Finally, we analyze the two-dimensional (2D) spatial resolution of GB-AA-SAR, and verify the effectiveness of the proposed algorithm through numerical simulation experiments.

The azimuth multichannel imaging scheme with the large receive antenna divided into multiple sub-apertures usually leads to azimuth non-uniform sampling, and echoes from all azimuth channels should be reconstructed based on the signal model before conventional SAR imaging. Unfortunately, the multichannel signal model of a moving target is different from that of a fixed target. This paper analyzes the multichannel signal model of the moving target and the effect of the target velocity on azimuth multichannel reconstruction. Based on the multichannel signal mode of the moving target, a new multichannel signal reconstruction algorithm is proposed. Furthermore, the slant range velocity is estimated by computing signal energy distribution. Simulation results on point targets validate the proposed approach.

Velocity measurements of moving targets based on radar systems have extensive applications in many engineering fields. This paper proposes an optimized velocity estimation strategy for dual-frequency radar by introducing a new DFT interpolation scheme and the fusion of the observations acquired by two receivers. Firstly, a new two sample DFT interpolation method based on a phase detector is contributed. The new interpolation method can achieve an accurate estimation for each single receiver. Additionally, a weighted least square estimation scheme is further proposed through the fusion of the observations of dual receivers. The fusion boundaries of dual receivers are investigated, and an optimized fusion strategy is finally presented. Numerical and experimental results show that the proposed strategies equip good noise resistance and outperform existing methods under various conditions.

Cooperative track spoofing (CTS) poses an unsolved conundrum to netted radar systems (NRS) by creating increasingly persistent and precise phantom tracks. Existing anti-deception methods in NRS struggle to counter this sophisticated joint range-angle deception. To address this pressing challenge, we propose a supervisory-review mechanism with its parameter optimization to identify CTS while minimizing its impact on tracking and searching tasks of NRS. The mechanism begins with a supervisory process that involves selecting the supervisory frame and radar node, along with scheduling resources during dwell time to check targets. Following this, binary hypothesis testing in the review process determines whether to repeat the supervisory process. These steps thus enable NRS with supervisory-review mechanism to detect CTS. To enhance effectiveness, the parameter optimization incorporates a hybrid policy for selecting supervisory radar nodes and employs reinforcement learning (RL)-based resource scheduling via a constructed Markov decision process (MDP). Simulations in complex multi-target scenarios validate the effectiveness of the proposed mechanism in recognizing CTS and demonstrate the advantages of RL-based resource scheduling.

Self-play methods have achieved remarkable success in two-player zero-sum games, attaining superhuman performance in many complex game domains. Parallelizing learners is a feasible approach to handle complex games. However, parallelizing learners often leads to the suboptimal exploitation of computational resources, resulting in inefficiencies. This paper introduces the Mixed Hierarchical Oracle (MHO), which is designed to enhance training efficiency and performance in complex two-player zero-sum games. MHO efficiently leverages interaction data among parallelized solvers during the Parallelized Oracle (PO) process, while employing Model Soups (MS) to consolidate fragmented computational resources and Hierarchical Exploration (HE) to balance exploration and exploitation. These carefully designed enhancements for parallelized systems significantly improve the training performance of self-play. Additionally, MiniStar is introduced as an open source environment focused on small-scale combat scenarios, developed to facilitate research in self-play algorithms. The MHO is evaluated on both the AlphaStar888 matrix game and MiniStar environment, and ablation studies further demonstrates its effectiveness in improving the agent’s decision-making capabilities. This work highlight the potential of the MHO to optimize compute resource utilization and improve performance in self-play methods.