Cognitive electronic warfare (CEW) has received increasing attention, and it is widely recognized that it will play a significant role. Cognitive jamming decision-making, as one of the critical technologies of CEW, dramatically impacts the global battlefield situation. In this paper, we introduce the A2C algorithm into cognitive jamming decision-making and propose a heuristic improved A2C algorithm. As a fusion algorithm of DQN and Policy Gradient, on the one hand, the Actor-Critic algorithm has the advantages of iterative updating of policies and high efficiency in complex spaces, compared to the DQN algorithm. On the other hand, compared with the Policy Gradient algorithm, it features fast convergence. But, it suffers from high variance, making convergence difficult. First, we establish a cognitive jamming decision-making model to address the above issues. Then, we develop an improved A2C algorithm by introducing a baseline and dueling networks. The baseline reduces the variance of the critic network, while dueling networks further decrease variance, enhancing the convergence of the A2C algorithm. Additionally, the improved A2C algorithm does not rely on prior information, and enhances the adaptive capability of the jammer when interacting with the target radar. We conducted numerical simulations based on the designed cognitive jamming decisionmaking model. The results demonstrated that compared with the four algorithms (DQN, Policy Gradient, Actor-Critic, A2C), the convergence speed of the improved A2C algorithm is improved by 50%, 57.8%, 34.5%, and 13.64%, respectively, and verified the excellent performance of the improved A2C. Finally, we introduce the heuristic reward function and propose the heuristic improved A2C algorithm. Compared with the improved A2C algorithm, the convergence speed of this algorithm is improved by 31.58%. The simulation results prove that the algorithm can greatly improve our advantage in electronic countermeasures

Dual-function radar-communication (DFRC) systems are key to addressing spectrum congestion and hardware constraints in future 5G/6G networks. Among various DFRC waveform candidates, OFDM stands out for its flexibility, but its high range sidelobes pose challenges for weak target detection in cluttered environments. In this work, we propose a novel waveform optimization framework that jointly minimizes the peak sidelobe level (PSL), maintains a low symbol error rate (SER), and enforces constant envelope constraints. To solve the resulting non-convex, NP-hard problem efficiently, we introduce a Block-wise Majorization-Minimization (BWMM) algorithm that iteratively refines the phase of each OFDM symbol to suppress both auto- and cross-correlation sidelobes. Theoretical analysis and simulation results validate that the proposed BWMM-PSL method significantly enhances radar sensing performance while preserving communication reliability.

To detect moving weak targets in the dual function radar communication (DFRC) system of an orthogonal frequency division multiplexing (OFDM) waveform, a modified hybrid integration method is addressed in this paper. A high-speed aircraft can cause range walk (RW) and Doppler walk (DW), rendering traditional detection methods ineffective. To overcome RW and DW, this paper proposes an integration approach combining DFRC and OFDM. The proposed approach consists of two primary components: intra-frame coherent integration and hybrid multi-inter-frame integration. After the echo signal is re-fragmented into multiple subfragments, the first step involves integrating energy across fixed situations within intra-frames for each subcarrier. Subsequently, coherent integration is performed across the subfragments, followed by the application of a Radon transform (RT) to generate frames based on the properties derived from the coherent integration output. This paper provides detailed expressions and analyses for various performance metrics of our proposed method, including the communication bit error ratio (BER), responses of coherent and non-coherent outputs, and probability of detection. Simulation results demonstrate the effectiveness of our strategy.

Spectrum competition and hardware complexity inherent in communication and radar systems can be alleviated by a dual-function radar communication (DFRC) systems. However, enhancing detection capabilities for maneuvering or weak targets remains a significant challenge, as traditional radar signal accumulation algorithms are not directly applicable to DFRC systems. This paper proposes a novel method that integrates signal accumulation and parameter estimation for target detection in DFRC systems. The approach employs an orthogonal frequency division multiplexing (OFDM) waveform within a multiple-input multiple-output (MIMO) framework, enabling the detection of high-speed or weak targets through a computationally efficient signal accumulation process. The proposed method comprises three key steps: first, the designed signals combined with the multiple signal classification (MUSIC) algorithm for target angle estimation in the spatial domain. Second, a two-step signal accumulation process is introduced, which separately extracts range and velocity information for weak or high-speed targets. Third, acceleration is derived based on velocity information and accumulation time. We present explicit expressions and detailed analyses of various performance metrics, including accumulation output response for high-speed targets, multiple target scenarios, and cases with low signal-to-noise ratio (SNR). Additionally, Cram4er-Rao bounds (CRBs) for azimuth, range cell, and velocity cell estimation in DFRC MIMO-OFDM systems are derived. Simulation results validate the proposed method, demonstrating its superior target detection performance compared to existing techniques.

Based on Landsat ETM+ remote sensing data and historical geography analysis, this paper studied structural characteristics and driving factors for polder (artificial dike-surrounded field) landscapes in the East Chaohu Area of Anhui Province, China. According to the characteristics of polder structure and spectral analysis of typical objects, differential methods were used to gain structural characteristics of polder landscape in this area. The results indicated that the spatial structural characteristics of polder landscape could be divided into six categories: polygon-shaped structure, jaggies-shaped structure, dendritic-shaped structure, feather-shaped structure, cricoid fan-shaped structure, and rectangular-shaped structure. All six landscape structures were formed by the long-term human exploitation of farmland, reflecting different natural and social functions of polder's construction. Finally, combined with regional environment and historical literature, this paper synthetically analyzed the five main driving factors of this artificial field development.