Reconfigurable intelligent surfaces (RIS) and integrated sensing and communication (ISAC) are key enablers for 6G networks, with their integration enhancing spectrum efficiency and multifunctional operations, as explored in recent RIS-aided ISAC frameworks. However, prior works overlook two issues: distinct channel models for radar typically LoS-dominant versus multipath-heavy communication links, and the need for advanced radar metrics beyond traditional bounds. In this letter, we propose a novel RIS-assisted ISAC system tailored for urban environments and adopt a hybrid fading channel model, where LoS-dominant radar direct links are characterized by Rician fading, while RIS-assisted reflected links are modeled using Nakagami- $m$ fading. We introduce a novel dual-path radar model where the base station receives target echoes from both direct and RIS-reflected illuminations. To rigorously assess radar performance, we adopt the radar estimation information rate (REIR), an advanced metric derived from mutual information principles, which quantifies the information-theoretic potential for extracting target-related information from radar echoes under fading and noise. For communication, closed-form expressions for outage probability and ergodic rate are derived under the hybrid channel conditions. Simulations validate the analytical results and demonstrate superior REIR and rate gains over non-RIS baselines in urban environments.

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar offers significant advantages in mainlobe deceptive interference suppression, as its transmit steering vector contains both angle and range information, providing additional degrees of freedom beyond the angular dimension. However, conventional FDA-MIMO radar suffers from insufficient angle-range resolution, which limits its ability to suppress interferences located close to the target. Moreover, it lacks robustness under limited snapshots and parameter mismatch conditions. To address these issues, this paper proposes a robust beamforming method based on the FDA-MIMO radar model. A collocated sparse array with a sinusoidal element spacing offset and a logarithmic frequency offset is adopted to enhance beam resolution and resolve the periodic angle-range ambiguity problem. Based on this model, the interference-plus-noise covariance matrix is reconstructed using two-dimensional Capon spatial spectrum, and the steering vector is corrected via a joint objective function that combines MUSIC orthogonality and the flatness of the covariance residual spectrum. Simulation results demonstrate that, under conditions of near-target interferences, random range-angle errors, and frequency offset errors, the proposed method achieves a signal-to-interference-plus-noise ratio (SINR) close to the ideal value, exhibiting excellent mainlobe interference suppression performance and robustness.

Frequency diverse array (FDA) has a distance-angle-dependent beam direction map, which has a greater application advantage than phased array. However, due to the distance-angle coupling and periodicity of its beam direction map, it makes the parameter information of the target obtained through FDA have ambiguous phenomena. Currently, researchers mostly use different types of frequency offsets, array element arrangement, and optimized parameters to eliminate the coupling and periodicity, but the effect is poor and does not consider the balance between the main flap width, side flaps, and gate flaps. We introduce an improved distributed array with two ends transmitting and middle receiving into the FDA-MIMO radar and propose a new distance-angle decoupled beam formation method to eliminate the coupling and periodicity of the FDA beam. The method utilizes the NSGA-II algorithm to jointly optimize the frequency offset increment and array element layout to achieve complete decoupling of the distance-angle dimension and spot beam focusing and significantly suppresses the sidelobes and eliminates the grating flaps, providing flexible and adjustable beam characteristics. Simulation results demonstrate the effectiveness of this scheme.

The growing frequency of unauthorized UAV activities has increased the demand for real-time perception and rapid response on resource-constrained edge devices. This study proposes an edge-deployable UAV detection and net-capture system based on Net-Capture Detection YOLO (NCDet-YOLO). Developed from YOLOv8n, NCDet-YOLO incorporates C2f_Faster, SPD_Conv, EMA, and a lightweight three-scale detection head, with CrossKD used to compensate for accuracy loss caused by structural compression. The dataset contains 6615 images and was divided into 5292 training and 1323 validation images. The self-collected data include DJI Phantom 4 and DJI Inspire 2 UAVs observed at approximately 4–30 m under different daytime backgrounds. NCDet-YOLO achieves an mAP50–95 of 0.6504 with 1.55 M parameters and 4.1 GFLOPs. On a Jetson Orin NX Super under the 15 W power mode, it achieves 31.53 FPS, representing a 31.67% increase over YOLOv8n. The detector is further integrated with target alignment, distance determination, trigger control, and net-capture execution. In 10 real-platform trials, 8 captures were successful, corresponding to an 80.0% success rate, with one false-trigger event and an end-to-end latency from target detection to net-capture firing of approximately 400 ms.

In this paper, a novel Frequency Diverse Array–Multiple Input Multiple Output (FDA-MIMO) radar parameter estimation algorithm based on Graph Signal Processing (GSP) is proposed for joint range–angle–velocity estimation. By modeling the FDA-MIMO radar echoes as graph signals and constructing an adjacency matrix that captures the spatial correlations among array elements, the proposed method performs Graph Fourier Transform (GFT) analysis to extract the spectral characteristics of the target signal. The target parameters are then obtained by searching for the spectral peak responses corresponding to the unit eigenvalue of the graph adjacency matrix. The proposed GSP-based FDA-MIMO radar framework provides an efficient and high-precision solution for multi-target parameter estimation, with strong potential for real-time and complex-environment applications.

Due to the inherent imaging characteristics of synthetic aperture radar (SAR), SAR images are inevitably corrupted by speckle noise, which degrades image quality and reduces the reliability of subsequent applications. Although deep generative networks have shown strong potential for SAR denoising, generative adversarial network (GAN)-based methods still suffer from convergence difficulties, while diffusion models rely on computationally expensive iterative inference. To address these issues, this paper proposes an efficient one-step generative architecture, termed 1-NFE Drifting U-Net. The proposed method introduces a long shortterm memory (LSTM)-based decomposition mechanism and a joint loss function composed of multiscale feature drift, spatial reconstruction, native edge preservation, and total variation regularization terms to preserve both global topology and local details. Experimental results on the Sentinel-1 & Sentinel-2 Image Pairs dataset demonstrate the effectiveness of the proposed method, achieving a peak signal-to-noise ratio (PSNR) of $\mathbf{1 5. 2 5 ~ d B}$, a structural similarity index measure (SSIM) of 0.2087, and an equivalent number of looks (ENL) of 24.59.

This paper presents the design and implementation of a real-time visual tracking system for unmanned aerial vehicles (UAVs), based on the DJIPayload Software Development Kit (PSDK), addressing the challenge of balancing high precision with low latency on resource-constrained edge platforms. By utilizing DJI PSDK to abandon the Robot Operating System (ROS) layer and its associated serialization overhead, the proposed Middleware-Free Architecture reduces end-to-end latency by over 60% to approximately 30 ms. To address computational constraints, a Lightweight Asymmetric De-coupled Visual Servoing (ADVS) strategy is proposed. It adopts orthogonal kinematic de-coupling to bypass Jacobian matrix inversion and integrates a non-linear dead-zone mechanism with dynamics-aware gain scheduling to compensate for sensing anisotropy and gravitational nonlinearity. Simultaneously, a Geometry-Aware Fusion strategy is employed to reject visual outliers, while a Finite State Machine (FSM) strictly enforces temporal consistency. Field experiments in various scenarios verify the system’s stability and tracking capability. Specifically, the platform maintains a robust lock on targets at speeds up to 23 m/s across dynamic maneuvers. The successful implementation of this system confirms that high-performance edge tracking does not rely solely on the scaling of visual model complexity but can also be effectively achieved through the architectural minimization of latency combined with the optimization of theoretically grounded robust control strategies.

In complex wireless environments, DroneID signals are highly susceptible to timing offset, carrier frequency offset, multipath fading, and noise, which degrade synchronization, channel estimation, and equalization performance. To address this problem, this paper proposes a low-SNR demodulation method based on time-domain channel smoothing. The proposed method transforms the least-squares (LS) channel estimate from the frequency domain to the time domain, retains only the effective channel taps within a limited delay range, and suppresses noise-dominated tail components, thereby reconstructing a more refined frequency-domain channel response for equalization. Experimental results show that, when the number of effective channel taps is set to 64, the proposed time-domain channel smoothing method reduces the stable demodulation threshold from $\mathbf{3 ~ d B}$ to $\mathbf{- 3 ~ d B}$. Moreover, in the TDL-D multipath channel, the method achieves a demodulation success rate of no less than 90% at $\mathbf{- 2 ~ d B}$, demonstrating its effectiveness and robustness for low-SNR DroneID signal demodulation.

This paper presents an enhanced method for non-contact detection of human vital signs using FMCW radar. A key challenge in this field is accurately separating weak respiratory and heartbeat signals from environmental noise and body movement artifacts. To address this, the authors propose a novel algorithm combining Variational Mode Decomposition with an Enhanced Whale Optimization Algorithm. The proposed E-WOA is designed to adaptively optimize VMD’s crucial parameters, overcoming the defects of the standard WOA, such as slow convergence and susceptibility to local optima. This is achieved by introducing a new population initialization, a pooling mechanism, and improved search strategies (migration, priority selection, and enriched encirclement), which enhance the algorithm’s global exploration ability. The performance of E-WOA was validated against the original WOA on benchmark functions, demonstrating superior convergence and accuracy. Subsequently, real-world experiments using a millimeter-wave radar confirmed that the E-WOA-VMD method significantly outperforms the traditional WOA-VMD approach. It achieves lower error rates and greater stability in estimating respiration and heart rates, proving its effectiveness and potential for reliable, non-contact health monitoring.

ObjectiveWith the concurrent evolution of wireless communication and radar technologies, spectrum congestion has become increasingly severe. Integrated Sensing and Communication (ISAC) has emerged as an effective approach that unifies sensing and communication functionalities to achieve efficient spectrum and hardware sharing. Orthogonal Frequency Division Multiplexing (OFDM) signals are regarded as a key candidate waveform due to their high flexibility. However, estimating target azimuth angles and suppressing interference from non-target directions remain computationally demanding, and confidential information transmitted in these directions is vulnerable to eavesdropping. To address these challenges, the combination of Directional Modulation (DM) and OFDM, termed OFDM-DM, provides a promising solution. This approach enables secure communication toward the desired direction, suppresses interference in other directions, and reduces radar signal processing complexity. The potential of OFDM-DM for interference suppression and secure waveform design is investigated in this study.MethodsAs a physical-layer security technique, DM is used to preserve signal integrity in the intended direction while deliberately distorting signals in other directions. Based on this principle, an OFDM-DM ISAC waveform is developed to enable secure communication toward the target direction while simultaneously estimating distance, velocity, and azimuth angle. The proposed waveform has two main advantages: the Bit Error Rate (BER) at the radar receiver is employed for simple and adjustable azimuth estimation, and interference from non-target directions is suppressed without additional computational cost. The waveform maintains the OFDM constellation in the target direction while distorting constellation points elsewhere, which reduces correlation with the original signal and enhances target detection through time-domain correlation. Moreover, because element-wise complex division in the Two-Dimensional Fast Fourier Transform (2-D FFT) depends on signal integrity, phase distortion in signals from non-target directions disrupts phase relationships and further diminishes the positional information of interference sources.Results and DiscussionsIn the OFDM-DM ISAC system, the transmitted signal retains its communication structure within the target beam, whereas constellation distortion occurs in other directions. Therefore, the BER at the radar receiver exhibits a pronounced main lobe in the target direction, enabling accurate azimuth estimation (Fig. 5). In the time-domain correlation algorithm, the target distance is precisely determined, while correlation in non-target directions deteriorates markedly due to DM, thereby achieving effective interference suppression (Fig. 6). Additionally, during 2-D FFT processing, signal distortion disrupts the linear phase relationship among modulation symbols in non-target directions, causing conventional two-dimensional spectral estimation to fail and further suppressing positional information of interference sources (Fig. 7). Additional simulations yield one-dimensional range and velocity profiles (Fig. 8). The results demonstrate that the OFDM-DM ISAC waveform provides structural flexibility, physical-layer security, and low computational complexity, making it particularly suitable for environments requiring high security or operating under strong interference conditions.ConclusionsThis study proposes an OFDM-DM ISAC waveform and systematically analyzes its advantages in both sensing and communication. The proposed waveform inherently suppresses interference from non-target directions, eliminating target ambiguity commonly encountered in traditional ISAC systems and thereby enhancing sensing accuracy. Owing to the spatial selectivity of DM, only legitimate directions can correctly demodulate information, whereas unintended directions fail to recover valid data, achieving intrinsic physical-layer security. Compared with existing methods, the proposed waveform simultaneously attains secure communication and interference suppression without additional computational burden, offering a lightweight and high-performance solution suitable for resource-constrained platforms. Therefore, the OFDM-DM ISAC waveform enables high-precision sensing while maintaining communication security and hardware feasibility, providing new insights for multi-carrier ISAC waveform design.

In close-range UAV net-capture scenarios, relying on a single sensing modality may result in unstable target perception and limited ranging capability. To address this issue, this paper develops a cooperative perception and ranging method that jointly exploits millimetre-wave radar measurements and visual information. Firstly, a mapping relationship between millimetre-wave radar targets and image pixel coordinates is established through multi-sensor spatiotemporal calibration; then, the YOLOv8 is utilised for visual object detection, and effective radar targets are extracted by combining millimetre-wave radar point cloud pre-processing, the DBSCAN clustering, and inter-frame correlation; subsequently, a radar-vision target fusion discrimination method is constructed based on bounding box constraints, projection position consistency, and temporal continuity constraints to achieve cross-modal target matching; finally, the distance from successfully matched millimetre-wave radar is used as the observed value to output the continuous target distance. The experimental evaluation verifies that the proposed approach can stably extract valid targets, establish effective radar-vision associations, and generate continuous range measurements, achieving a maximum absolute error of 0.17 m, a mean absolute error of 0.089 m, and a mean absolute percentage error of approximately 2.07%.

Interferometric radar can provide complete two-dimensional velocity information of any moving object regardless of its trajectory. Although current technology can jointly detect the angle and angular velocity of the target, the ability to distinguish the tangential velocity of multiple targets is still insufficient. To address this challenge, this paper proposes a moving target detection method that jointly measures distance and tangential velocity. Firstly, the post-interferometric algorithm is utilized to enhance the signal-to-noise ratio and suppress cross-terms. Then, a joint detection algorithm for distance and tangential velocity is proposed. Finally, a two-dimensional joint estimation of distance and tangential velocity for moving targets is achieved. Simulation results show that this method has a low root mean square error, demonstrating its feasibility.

In this paper, a novel Doppler shift estimation method for frequency diverse array (FDA) radar based on graph signal processing (GSP) theory is proposed and investigated. First, a well-designed graph signal model for a monostatic linear FDA is formulated. Subsequently, spectral decomposition is conducted on the constructed signal model utilizing graph Fourier transform (GFT) techniques, enabling the extraction of the target’s Doppler shift parameter through spectral peak search. A comprehensive series of simulation experiments demonstrates that the proposed method can achieve the accurate estimation of target parameters even under low signal-to-noise ratio (SNR) conditions. Furthermore, the proposed method exhibits superior performance compared to the MUSIC algorithm, offering enhanced resolution and estimation accuracy. Additionally, the method is highly amenable to parallel processing, significantly reducing the computational burden associated with traditional procedures.

Frequency diverse array (FDA) beams show an “S” shape in space and cannot form a spot beam; thus, they cannot be directly combined with spotlight synthetic aperture radar (SSAR). In this paper, we propose a 2D imaging system emitting multiple repeated subpulses using an FDA and spotlight synthetic aperture radar (MRS-FDA-SSAR). This system carries the FDA on an airborne platform and uses the frequency difference between the array elements to synthesize broadband signals and obtain the distance-direction resolution, and then it uses a synthetic aperture technique to obtain the azimuth-direction resolution. Subsequently, 2D imaging results are obtained using the BP algorithm. A deconvolution algorithm is introduced to address the problem of high target sidelobes in the BP imaging results, which can result in the masking of weak targets. This allows 2D imaging results to be obtained with lower sidelobes. Finally, the MRS-FDA-SSAR model was simulated in experiments to verify its effectiveness.

To improve the range-angle estimation accuracy of frequency diverse array multiple Input multiple output (FDA-MIMO) radar at low SNR, this letter proposes a joint estimation algorithm of FDA-MIMO radar target range-angle based on graph signal processing (GSP). Firstly, the adjacency matrix is constructed according to the FDA-MIMO radar signal model, and then the eigenvector of the target is extracted by the eigen-decomposition of the adjacency matrix. Secondly, the echo signal is processed by graph Fourier transform (GFT). Finally, the spectral peak search response function is established to obtain the range-angle information of the target. The experimental results show that the proposed algorithm has higher estimation accuracy than the MUSIC algorithm when the SNR is lower than 0 dB, and it has certain advantages and application potential for the parameter estimation of weak targets.

A dynamic weighted multi-sensor fusion method is proposed to address localization degradation in four-wheeled differential mobile robots under GNSS denial and wheel slippage conditions. Sensor data from the IMU, wheel odometer, and RTKGNSS are first preprocessed. A dual-threshold decision mechanism, based on RTK confidence evaluation and wheel speed-IMU displacement residual detection, is then constructed to determine system states. Subsequently, observation noise weights for each sensor are dynamically adjusted using a noise-driven model and incorporated into an error-state Kalman filter (ESKF) for position estimation. Experimental results demonstrate that, under GNSS denial or wheel slippage, the proposed method effectively mitigates the impact of partial sensor failures and significantly enhances the robustness of the localization system against dynamic disturbances.

In the field of radar signal processing, moving target detection has consistently garnered widespread attention. Under complex signal backgrounds, Frequency Modulated Continuous Wave (FMCW) radar systems are susceptible to interference from clutter and noise sources, which significantly degrades their detection performance for moving targets. To address this challenge, this paper proposes a moving target detection method based on graph connectivity density. Initially, the Moving Target Indication (MTI) technique is employed to perform pulse integration of moving targets in signals acquired by FMCW radar. A signal model of pulse spectral intensity maps is constructed within target and clutter range cells, followed by the extraction of the maximum eigenvalue of the Laplacian matrix as a measure of graph connectivity. This parameter is subsequently utilized for detection across all range cells. Experimental results demonstrate that the proposed method achieves superior detection probability under conditions of low false alarm probability.

Aiming at the challenges of significant scale and perspective variations of targets, marine environmental variability, and dataset scarcity in ship detection under drone vision, this paper proposes the EPA-YOLOv10 ship detection model to effectively suppress wave interference and enhance small target detection accuracy. At the algorithmic level: (1) the C2f-EMSC multi-scale feature enhancement module is designed to achieve local and global feature fusion of ships and multi-scale integration of distant and nearby ship targets; (2)the PTSSA spatiotemporal attention mechanism is proposed to suppress sea surface interference through reverse spatial attention. Additionally, to address the scarcity of ship data, a multi-source ship dataset containing four major ship categories is constructed. Experiments show that the detection accuracy of the proposed model on the self-built dataset Myship is significantly improved with mAP0.5.

To address the challenges of accuracy and robustness in UAV autonomous tracking under multi-target dynamic scenarios, this study proposes a millimeter-wave radar-based UAV tracking system incorporating improved DBSCAN clustering and Kalman-PID control. The implementation involves three key phases: Firstly, an autonomous UAV tracking system with millimeter-wave radar is established on the ROS platform. Secondly, time-domain sliding average filtering is applied to compensate for radar signal distortion, effectively resolving motion-induced point cloud ambiguity. Finally, front-end multi-target separation is achieved through distance-weighted DBSCAN algorithm, while back-end trajectory tracking is accomplished via cascaded Kalman filtering and PID controller. Experimental results demonstrate static target positioning accuracy of less than or equal to 0.18 meters and dynamic target tracking error of less than or equal to 0.4 meters, representing a 31.5 percent improvement in accuracy compared to traditional fixed-parameter DBSCAN-Kalman filter hybrid methods.