In this paper, in order to reduce the probability of the radar waveform intercepted by the passive detection system, the time-bandwidth product of the radar waveform is increased, and the detection probability of the radar waveform to the target is improved. This paper tackles the holographic RF stealth radar and proposes a joint coding waveform based on the linear frequency modulation (LFM) waveform. Joint coding uses complementary codes to perform phase-coding, and combines the codewords optimized by genetic algorithm in order to perform discrete frequency-coding waveform. The joint coding waveform model is theoretically analyzed, and the ambiguity function, pulse compression and target detection probability of the joint coding waveform are obtained by numerical simulation. In addition, the complexity of the algorithm and the low probability of intercept (LPI) characteristic of the joint coding waveform are analyzed. The results show that the joint coding waveform has an approximate “pushpin” ambiguity function, ultra-low sidelobe characteristics, better RF stealth and target detection performance. Finally, it has good application prospects in the current battlefield environment.

Cognitive electronic warfare is gaining increasing attention and will play an important role in warfare. Cognitive jamming decision-making, as a key technology, significantly influences battlefield dynamics. Modern radar can work in multiple directions at the same time, and most of the existing research is on jamming style decision-making of a single jammer, which is difficult to adapt to complex battlefield situations. Multi-agent coordination can effectively deal with multi-functional radar, so this paper investigates cooperative jamming decision-making algorithms. First, this paper establishes a cooperative jamming decision-making model, in which three jammers are assumed to jam the radar in three directions and are based on a centralized learning distributed execution architecture. Then, this paper extends the Advantage Actor-Critic(A2C) algorithm by proposing the Multi-Agent A2C(MA-A2C) algorithm and applying it to the model. In order to effectively solve the problems of high variance and environmental sensitivity in the MA-A2C algorithm, we propose two improvement methods and combine them to propose the Multi-Agent Heuristic Dueling A2C(MA-HDA2C) algorithm. We conducted simulation experiments on MA-A2C, two improved methods and MA-HDA2C algorithms under the same modeling conditions. The results show that the MA-A2C algorithm can effectively carry out cooperative jamming decision-making, but it has problems with low efficiency and poor stability. The MA-HDA2C algorithm has the advantages of fast convergence speed and stable convergence results. Compared with the MA-A2C algorithm, the MA-HDA2C algorithm has more significant superiority, which proves the effectiveness of the improvement method proposed in this paper.

In order to recover the three‐dimensional (3D) structure of the target from sequential inverse synthetic aperture radar (ISAR) images, the factorisation method is generally used. It requires a large number of high‐quality matched feature points from different ISAR images. If the number of extracted feature points is insufficient, the restored 3D structure is not obvious. Furthermore, the mismatching of feature points will greatly affect the quality of target reconstruction. However, the factorisation method only uses the information from the ISAR images, while that from imaging geometry is not sufficiently considered. ISAR imaging is a kind of projection, and the projection plane information could be taken into account for the 3D reconstruction. Hence, a new 3D reconstruction method for stable targets from sequential ISAR images is proposed in this paper. Firstly, the ISAR images are preprocessed with the CLEAN algorithm. The maximum between‐cluster variance method is applied to extract feature points from the processed images. Moreover, the image projection plane and projection equation corresponding to different ISAR images are analysed by using imaging geometry information. According to the projection equation, the feature points are back‐projected (BP) to the 3D space. Finally, the 3D point clouds obtained by the BP from multiple radar images are fused by the iterative closest point algorithm to restore the 3D structure of the target. The simulation and experiment results show the effectiveness and robustness of this method.

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.

High-Resolution Range Profile (HRRP) represents the electromagnetic backscattering distribution of targets and plays a pivotal role in remote-sensing-based Automatic Target Recognition (RATR). However, in non-cooperative sensing scenarios, acquiring sufficient measured data is severely constrained by operational costs and physical limitations, leading to data scarcity that hampers model robustness. To overcome this, we propose SpecM-DDPM, a spectral-aware Denoising Diffusion Probabilistic Models (DDPM) tailored for generating high-fidelity HRRPs that preserve physical scattering properties. Unlike generic generative models, SpecM-DDPM incorporates radar signal physics into the diffusion process. Specifically, a parallel multi-scale block is designed to adaptively capture both local scattering centers and global target resonance structures. To ensure spectral fidelity, a spectral gating mechanism serves as a physics-constrained filter to calibrate the energy distribution in the frequency domain. Furthermore, a Frequency-Aware Curriculum Learning (FACL) strategy is introduced to guide the progressive reconstruction from low-frequency structural components to high-frequency scattering details. Experiments on measured aircraft data demonstrate that SpecM-DDPM generates samples with high physical consistency, significantly enhancing the generalization performance of radar recognition systems in data-limited environments.

3-D reconstruction and motion estimation of space targets are crucial for space situational awareness (SSA). Inverse synthetic aperture radar (ISAR) is one of the primary observation techniques for SSA. Traditional single-station ISAR methods, however, are unable to effectively reconstruct non-stationary targets because their unknown intrinsic motion prevents the determination of the correct projection geometry. To address this, this paper proposes an algorithm for instantaneous 3-D reconstruction and motion estimation using a distributed ISAR system. Our method leverages multi-view images acquired in a single time snapshot to resolve the inherent ambiguity between the target's structure and its motion. The core of our contribution is a joint optimization framework that simultaneously estimates the target's 3-D point cloud structure and its instantaneous intrinsic rotation parameters, including the rotation axis and angular velocity. This is achieved by using the consistency of multi-view silhouettes as the optimization objective, which is efficiently solved by particle swarm optimization (PSO). Extensive simulations validate that the proposed method rapidly reconstructs dense 3-D point clouds of non-stationary targets while accurately estimating their motion and pose.