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China has one of the longest land borders in the world and features a diverse range of terrain types and a dense electromagnetic environment. Therefore, in practical applications, airborne radar faces complex environments. The efficacy of detecting airborne radar is severely deteriorated in regions with complex terrains and electromagnetic environments, limiting the ability to meet military operational requirements. Cognitive space-time adaptive processing is an effective technical approach for addressing this problem. In this study, a cognitive Space-Time Adaptive Processing (STAP) architecture is proposed, and based on this architecture, the database, algorithm library, cognitive STAP technology, and feedback control are introduced. Analysis of the simulated data reveals that compared to traditional STAP technology, cognitive space-time adaptive processing technology can significantly enhance the efficacy of detecting moving targets using airborne radar in complex environments.
In this paper, we investigate the adaptive detection of range-distributed targets in compound-Gaussian clutter, where the texture component follows a Weighted Generalized Inverse Gaussian (WGIG) distribution. We propose adaptive detectors for WGIG-distributed clutter based on two-step Rao, Wald, Durbin, and Gradient tests. The unknown covariance matrix is estimated using Approximate Maximum Likelihood (AML) and the Normalized Sample Covariance Matrix (NSCM). To address the analytical intractability of Maximum A Posteriori (MAP) estimation for the texture component, we adopt an alternative approach: The MAP estimator of the reciprocal expectation of the texture component, which is used in designing adaptive detectors based on the Rao, Wald, and Durbin tests. For the Gradient test-based detector, the test statistic is derived directly from the posterior probability density function. Our theoretical analysis confirms the consistency of the detectors derived from the Rao, Durbin, and Gradient tests. Extensive evaluations on both simulated and real data yield three key findings: (1) the proposed AML-based detectors maintain the constant false alarm rate property; (2) under matched signal conditions, the detectors based on the Rao and Wald tests achieve the best performance on both the IPIX radar dataset and the Journal of Radar’s maritime surveillance dataset—specifically, they outperform the two-step generalized likelihood ratio test-based detector, requiring 0.1~0.5 dB and 0.7~0.8 dB lower Signal-to-Clutter Ratio (SCR) to achieve the same detection probability, respectively; and (3) under mismatched signal conditions, the Rao test-based detector with AML estimation exhibits superior robustness, while the Wald test-based detector demonstrates the strongest suppression capability against mismatched signals.
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Detecting targets despite sea clutter is crucial in military and civilian applications. In complex marine environments, sea clutter exhibits target-like spikes and inherently broad-spectrum characteristics, posing a significant challenge for marine radars in detecting Low-Slow-Small (LSS) targets and leading to high false alarm rates. In this study, an S-band holographic staring radar with high-Doppler and high-range-resolution capabilities (i.e., “dual-high” capability) was utilized in sea detection experiments. We obtained sea clutter data, LSS target data (over the sea surface and in the air), ground truth data on target positions and trajectories, as well as wind and wave data. Using these data, we constructed an S-band holographic staring radar dataset for low-observable targets at sea. The time-domain, frequency-domain, and time-Doppler characteristics of the dataset were analyzed, and the results served as a reference for data utilization. Future work will involve continuing experiments to expand the maritime experimental environment (e.g., sea state and region) and target types toward enhancing data diversity. This open dataset will support the enhancement of new radar systems for detecting low-observable targets at sea and improving maritime target detection and recognition performance.
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Small rotorcraft Unmanned Aerial Vehicles (UAVs), owing to their compact size, lightweight nature, and excellent maneuverability, are often used as platforms for Synthetic Aperture Radar (SAR) systems. These UAVs exhibit great potential in complex environment detection at low altitudes. However, the operation of small rotorcraft UAVs involves sharp, random motion errors during flight at low altitudes. Additionally, the limited payload capacity of these vehicles further limits their capacity to carry high-precision positioning equipment. The abovementioned motion errors observed during the operation of UAVs become a key factor that affects the imaging accuracy in UAV-mounted through-the-wall SAR imaging. To address this drawback, a conventional error compensation algorithm based on the Stage by Stage Approaching (SSA) algorithm has been proposed. This approach is based on the bunching SAR imaging mechanism, assuming that the phase error of all the pixels in the scene is the same; this approach is not applicable under the condition of a bandwidth beam. Therefore, this paper presents a wide-beam motion error compensation method for through-the-wall SAR imaging based on the SSA algorithm. The method employs the Back Projection (BP) algorithm to model the motion errors of the radar echo of the rotorcraft UAVs. Using the image entropy evaluation criterion of SAR, the SSA optimization algorithm was applied in this study to estimate the phase errors of the antenna phase center for each pixel in the imaging scene. Subsequently, the BP algorithm was used to perform high-precision phase compensation for each pixel, thereby addressing the spatial variations of motion errors in the wide-beam through-the-wall SAR system. The results of the simulation and experimental data processing reveal that the proposed algorithm can accurately compensate for spatially varying motion errors in wide-beam scenarios. It enables good focusing of multiple targets in the scene and effectively resolves the problem of spatially varying motion errors in wide-beam through-the-wall SAR imaging.
To enhance the jamming recognition capabilities of radars in complex electromagnetic environments, this study proposes YOLO-S3, a lightweight network for recognizing composite jamming signals. YOLO-S3 is characterized by three core attributes: smartness, slimness, and high speed. Initially, a technical approach based on visual detection algorithms is introduced to identify 2D time-frequency representations of jamming signals. An image dataset of composite jamming signals is constructed using signal modeling, simulation technology, and the short-time Fourier transform. Next, the backbone and neck networks of YOLOv8n are restructured by integrating StarNet and SlimNeck, and a self-attention detect head is designed to enhance feature extraction. These modifications result in a lightweight network without compromising recognition accuracy. Finally, the network’s performance is validated through ablation and comparative experiments. Results show that YOLO-S3 features a highly lightweight network design. When the signal-to-jamming ratio varies from −10 to 0 dB and the signal-to-noise ratio (SNR) is ≥0 dB, the network achieves an impressive average recognition accuracy of 99.5%. Even when the SNR decreases to −10 dB, it maintains a robust average recognition accuracy of 95.5%, exhibiting strong performance under low SNR conditions. These findings provide a promising solution for the real-time recognition of composite jamming signals on resource-constrained platforms such as airborne radar signal processors and portable electronic devices.
In recent years, the rapid development of Multimodal Large Language Models (MLLMs) and their applications in remote sensing have garnered significant attention. Remote sensing MLLMs achieve deep integration of visual features and semantic information through the design of bridging mechanisms between large language models and vision models, combined with joint training strategies. This integration facilitates a paradigm shift in intelligent remote sensing interpretation—from shallow semantic matching to higher-level understanding based on world knowledge. In this study, we systematically review the research progress in the applications of MLLMs in remote sensing, specifically examining the development of Remote Sensing MLLMs (RS-MLLMs), which provides a foundation for future research directions. Initially, we discuss the concept of RS-MLLMs and review their development in chronological order. Subsequently, we provide a detailed analysis and statistical summary of the proposed architectures, training methods, applications, and corresponding benchmark datasets, along with an introduction to remote sensing agents. Finally, we summarize the research status of RS-MLLMs and discuss future research directions.
In light of challenges related to weak target detection and limited communication performance in extended clutter environments, this paper proposes a joint design of a transmit waveform and receive filter within a Multiple-Input Multiple-Output (MIMO) Radar Communication Integration (RCI) system, considering the uncertainty in the extended Target Impulse Response (TIR). Due to difficulties in accurately determining the extended TIR, an objective function was formulated to maximize the minimum Signal-to-Interference-plus-Noise Ratio (SINR) within a set of sphere TIR uncertainties. To ensure reliable information transmission for each user and to achieve desirable properties of the ambiguity function for the transmission waveform, per-user interference constraints were imposed, along with constraints on waveform similarity and peak-to-average ratio. A cyclic optimization algorithm was introduced to address the nonconvex quadratic constrained fractional programming problem. The optimal receive filter was first derived using a generalized Rayleigh quotient, and the nonconvex part of the original NP-Hard problem was then transformed into a convex problem using the Lagrange duality principle and subsequently solved by the semidefinite optimization method. Also, the convergence and computational complexity of the proposed algorithm are thoroughly discussed. Furthermore, the simulation results confirmed that the algorithm effectively enhances SINR in extended clutter environments and fulfills the communication needs of multiple users.
Traditional airborne radar Pulse Compression (PC) and Space-Time Adaptive Processing (STAP) suffer performance degradation in complex target and clutter environments due to their reliance on predefined linear models. To address this issue, we developed a deep learning-based joint STAP-PC technique. This approach employed dedicated networks—a super-resolution space-time spectrum network for nonlinear clutter estimation and a PC network for nonlinear PC. The proposed architecture effectively mitigated model mismatch within the processing chain, leading to improved clutter suppression and target detection. Notably, we mathematically established the feasibility of post-pulse compensation to prevent nonlinear PC from introducing phase errors across elements and pulses. The implemented architecture utilized multimodule convolutional neural networks for super-resolution space-time spectrum estimation and PC, with each module’s functionality demonstrating clear mathematical correspondence, thereby ensuring the reliability of the overall processing chain. Simulation results revealed that in scenarios with dense weak targets and limited samples, the proposed nonlinear joint processing technique improved signal-to-clutter-plus-noise ratio by approximately 20 dB over traditional methods.
A single Synthetic Aperture Radar (SAR) image can capture only two-dimensional information, and traditional multitemporal interferometric SAR (InSAR) techniques struggle with the layover problem, particularly in urban areas. SAR Tomography (TomoSAR) provides the advantage of obtaining three-dimensional (3-D) information while offering a feasible solution to the layover problem. This technique relies on repeated observations of the target scene to achieve 3-D resolution by synthesizing the aperture in the elevation direction. In China, early data sources for spaceborne TomoSAR primarily came from foreign satellites such as TerraSAR-X and COSMO-SkyMed, which constrained the development of the country’s TomoSAR technology. In recent years, the launch of Chinese commercial SAR satellites (e.g., Fucheng-1 and Hongtu-1) has expanded the range of data acquisition sources. However, studies on the tomographic 3-D inversion of urban buildings and structures using data from Chinese commercial SAR satellites remain limited. To validate the usability of Chinese commercial SAR satellite data in urban tomography 3-D parameter inversion and the effectiveness of applying these data to existing tomography imaging methods, this paper develops a 3-D inversion framework for urban TomoSAR and conducts a 3-D inversion study of urban buildings and structures using data from the Fucheng-1 satellite of Spacety Co., Ltd. (Changsha) and the Hongtu-1 SAR satellite of Piesat Information Technology Co., Ltd. The experimental results validate the potential of these two satellite systems for tomographic applications, providing pioneering technical support for future in-depth research and applications.
Existing moving-target detection methods for missile-borne sum-difference beam radars require large amounts of training range cell data, yet still exhibit low detection performance. To address these challenges, this paper proposes a new detection method based on intelligent multiclassification and network parameter transfer learning. The proposed method uses a small set of training range cell data to construct a dataset for training a deep convolutional neural network (CNN), which classifies data from the range cell under test (RCUT) into clutter (target-free) or target classes with different Doppler frequencies. To avoid the high computational cost and time associated with online training on measured data, an echo signal model is first established for moving target detection in the missile-borne sum-difference beam radar. This model is validated using measured data and subsequently used to generate simulated data for offline network training. In addition, to overcome common limitations of typical CNNs, such as large parameter sets, high computing complexity, and low training efficiency, this paper enhances the DenseNet architecture by incorporating a feature fusion module (FFM) and a spatial attention module (SAM), resulting in an improved FFM-SAM-DenseNet multiclassifier. Furthermore, conventional detection methods based on intelligent multiclassification require retraining the network when processing data from different RCUTs, leading to long convergence time and reduced efficiency. To solve this problem, transfer learning is introduced to share network parameters across multiclassifiers for different RCUTs, accelerating the overall convergence speed. Simulation and measured data show that, even with limited training range cell data, the proposed method achieves better moving target detection performance than existing typical methods.
Long-term cyclic loading by vehicles is a non-negligible contributor to post-work settlement of highways. Current Interferometric Synthetic Aperture Radar (InSAR) deformation models used for monitoring the deformation of soft-soil highways generally neglect the contribution of cyclic loading. The InSAR time-series deformation models used for monitoring highway deformation are typically combinations of one or several purely empirical functions, which lack clarity in physical significance and overlook the impact of cyclic loading on settlement. Herein, a method for estimating the deformation of soft-ground highways that accounts for cyclic loading is proposed (Improved InSAR Model considering both the Rheological Properties and the Traffc Loading, IRTM). The method improves InSAR deformation modeling and the parameter estimation algorithms. In the deformation modeling, the Maxwell rheological model, which describes the deformation and creep characteristics of soft soil, serves as the base model for InSAR modeling. An additional dynamic stress model was incorporated to describe the plastic deformation caused by cyclic loading, which was combined with a thermal expansion model to characterize the thermal expansion component of the road base and bridge affected by temperature. This combination provided a more reasonable interpretation of the deformation estimation data. For parameter estimation, a method based on a Genetic Algorithm (GA) and a parameter estimation algorithm was proposed. In particular, a parameter estimation method combining GA and the Levenberg-Marquardt (LM) algorithm was developed, where the initial value obtained by GA was further optimized by LM to enhance the solving efficiency and accuracy. The proposed method was validated through simulation and experiments employing real data. The simulation revealed that the relative errors of the model parameter estimates were all below 6% when ±0.5 rad noise was applied. Real data from the selected study area, i.e., the Beijing-Pinggu Expressway, were utilized, and the time-series deformations from 22 January 2012 to 1 July 2014 were obtained. The results show that the cumulative deformation reached −140 mm, where the rheological component of the soft-ground section was the dominant contributor to deformation, accounting for approximately 76%, whereas the cyclic through-load component was dominant at road intersections, accounting for 81%. Compared with single Maxwell and traditional linear models, the modeling accuracy of the developed method was improved by 44.4% and 49.6%, respectively. Finite Element Analysis (FEA) was used to verify the deformation accuracy obtained from real experiments. The deformation curves generated using the developed method were consistent with those produced by FEA under different axle loads, with a maximum standard deviation of only 1.8 mm. Cross-validation against existing studies showed that the external accuracy of the deformation rate obtained in this study was ±1.4 mm/yr, further confirming the reliability of the developed method for estimating and interpreting the post-work deformation of highways under cyclic loading. This method can provide a reference for controlling the stability of highways.
Low-sidelobe waveforms are fundamental for ensuring the basic detection performance of radars. Designing waveforms with low sidelobes in the range dimension, the velocity dimension, or both, remains a major challenge in radar research. To address the issue of sidelobe suppression in the velocity dimension for coherent pulse trains, this paper proposes a joint design method of variable-pulse-width pulse trains and receive mismatched filtering. The proposed method uses a symmetric positive window function to directly construct both the pulse width sequence and the receive weighting sequence. As a result, the characteristics of the window function’s amplitude spectrum, including low sidelobes and the broadening of the 3-dB mainlobe, are transferred into the mismatched filtering output of the coherent pulse train. Theoretical analysis shows that the proposed method incurs a smaller mismatched Signal-to-Noise Ratio (SNR) loss than when the window function is applied solely for receive mismatched filtering. The effects of window functions and the minimum pulse-width constraint on SNR loss and weak target detection performance under strong target interference are analyzed through simulations, illustrating the advantages of the proposed joint transceiver design method.
In recent years, with the increasing diversification of mission requirements, radar imaging has expanded from conventional side-looking and squint-looking modes to the forward-looking mode. In this regard, the monopulse imaging method offers several advantages, including its forward-looking imaging capability, real-time processing ability, and effective anti-jamming performance. These features can help efficiently overcome the problems faced by conventional imaging methods, i.e., low azimuth resolution and Doppler ambiguity in the forward-looking region. Hence, this method has emerged as a key solution to these challenges. This study first explores the distinction between monopulse tracking and monopulse imaging, followed by a systematic review of the existing technical approaches and evaluation metrics for monopulse imaging. Subsequently, the performance of different methods is analyzed, and specific applications of monopulse imaging technology in various scenarios are introduced, including three-dimensional imaging, moving target localization and imaging, and multi-view image fusion. The paper ends with a discussion of the development trends of monopulse imaging technology and an analysis of future research directions, such as imaging quality improvement and the expansion of the application scope.
Bistatic Inverse Synthetic Aperture Radar (Bi-ISAR) has garnered significant attention in the military and civilian domains due to its superior stealth and antijamming capabilities. However, the changing bistatic angle during Bi-ISAR imaging causes space-variant defocusing and geometric distortion in the resulting images, thereby severely compromising the accuracy of subsequent information extraction and target recognition. To address these issues, this study proposes a fast space-variant phase error compensation and geometric correction method for Bi-ISAR imaging based on a modified Newton’s method. This method uses the image entropy of the Bi-ISAR imaging result as the cost function and introduces space-variant coefficients and rotation parameters as optimization variables to formulate an optimization equation. By modifying the traditional Newton’s method to ensure the positive definiteness of the Hessian matrix, the cost function is guaranteed to be optimized along the descent direction in each iteration. Solving this optimization equation to minimize image entropy simultaneously estimates the rotation parameters, which are then used to construct a geometric correction function and calculate the scaling factor, that is, the actual size of each grid in the image, enabling geometric correction and scaling of the final imaging result. The proposed method simultaneously corrects space-variant phase errors and geometric distortion and operates in a data-driven manner, requiring only low initial image quality. Furthermore, due to the quadratic convergence property of Newton’s method, the proposed method offers higher computational efficiency compared with other methods. Finally, the effectiveness of the proposed method is validated through the processing and comparative analysis of the point-target simulation, electromagnetic calculation, and ground real target experimental data.
In radar systems that track multiple maneuvering targets, conventional approaches often suffer from performance degradation due to suboptimal resource allocation and insufficient utilization of prior information. To address this challenge and significantly enhance tracking performance under equivalent resource constraints, a resource allocation and precise tracking algorithm for multiple maneuvering targets is proposed. First, by integrating a multiple model interaction architecture with tracker feedback prediction, a probabilistic distribution model for target position prediction through multiple model interaction is constructed. This model establishes an integrated detection and tracking method based on multiple model interactions to achieve precise tracking of maneuvering targets. Subsequently, by analytically modeling the coupling mechanism between radar resources and tracking performance, and deriving the Bayesian Cramér-Rao Lower Bound (BCRLB) for maneuvering targets, a performance-driven multimodel weighted resource allocation framework is developed. Simulations validate that the proposed method can significantly enhance the overall tracking precision of multiple maneuvering targets under equivalent resource consumption.
Bistatic Synthetic Aperture Radar (BiSAR) needs to suppress ground background clutter when detecting and imaging ground moving targets. However, due to the spatial configuration of BiSAR, the clutter poses a serious space-time nonstationary problem, which deteriorates the clutter suppression performance. Although Space-Time Adaptive Processing based on Sparse Recovery (SR-STAP) can reduce the nonstationary problem by reducing the number of samples, the off-grid dictionary problem will occur during processing, resulting in a decrease in the space-time spectrum estimation effect. Although most of the typical SR-STAP methods have clear mathematical relations and interpretability, they also have some problems, such as improper parameter setting and complicated operation in complex and changeable scenes. To solve the aforementioned problems, a complex neural network based on the Alternating Direction Multiplier Method (ADMM), is proposed for BiSAR space-time adaptive clutter suppression. First, a sparse recovery model of the continuous clutter space-time domain of BiSAR is constructed based on the Atomic Norm Minimization (ANM) to overcome the off-grid problem associated with the traditional discrete dictionary model. Second, ADMM is used to rapidly and iteratively solve the BiSAR clutter spectral sparse recovery model. Third according to the iterative and data flow diagrams, the artificial hyperparameter iterative process is transformed into ANM-ADMM-Net. Then, the normalized root-mean-square-error network loss function is set up and the network model is trained with the obtained data set. Finally, the trained ANM-ADMM-Net architecture is used to quickly process BiSAR echo data, and the space-time spectrum of BiSAR clutter is accurately estimated and efficiently restrained. The effectiveness of this approach is validated through simulations and airborne BiSAR clutter suppression experiments.
Spaceborne Synthetic Aperture Radar (SAR) data may be prone to interrupted-sampling repeater jamming and many common unintentional interferences, such as linear frequency modulated pulses. In this paper, we first divide a single-look complex SAR image into multiple sub-band images of equal bandwidth in the range frequency domain. Then, we model the pixel intensity of these sub-band images and analyze the fluctuation mechanism of interfering and noninterfering pixels across the sub-bands. The findings reveal that the energy distribution of interfering pixels is uneven across different sub-bands, leading to substantial intensity fluctuations within the sub-band domain, whereas the intensity of noninterfering pixels remains relatively stable. Based on this observation, we define sub-band contrast and sub-band entropy as statistical measures to quantify fluctuation characteristics across the sub-bands. These measures are then compared with certain thresholds to obtain detection results. Statistical analysis revealed that under noninterfering conditions, these two statistics approximately follow the beta distribution. By leveraging this finding, we fit the distributions of these measures using the beta distribution and develop a method to determine detection thresholds under the constant-false-alarm-rate criterion. Experimental results showed that the proposed method can effectively detect interrupted-sampling repeater jamming and common unintentional interferences. In addition, we investigated the impact of the jamming-to-signal ratio on detection performance and verified the reliability and stability of the method via Monte Carlo simulations. Furthermore, we introduced an interference suppression technique based on a rank-1 model to reduce the adverse effects of interference on downstream tasks. This technique is capable of adaptively suppressing interference in detected regions.
A real aperture radar has physical space limitations that result in a wide antenna beam, leading to low angular resolution. The angular super-resolution method based on sparse reconstruction introduces sparse prior constraints of the target under a regularization framework and reconstructs the target reflectivity function through iterative optimization, thereby significantly enhancing the angular resolution of the radar. However, existing sparse reconstruction methods primarily consider the sparse distribution characteristics of strong point targets, neglecting the contour information of extended targets, which results in distortion in the recovery of target edges. Additionally, these methods are sensitive to one or more hyperparameters introduced into the cost function. Thus, meticulous manual adjustments are essential in practical applications, and they pose challenges in terms of the adaptive selection of hyperparameters in dynamic scenarios. To address these issues, this paper proposes a hyperparameter-free Total Variation (TV) regularization angular super-resolution method. First, a square-root Least Absolute Shrinkage and Selection Operator (LASSO) cost function was established to characterize the fitting residuals between the scan echo sequence and target reflectivity function and to characterize the sparse constraints on the target edge gradients. Using this function, the target contour reconstruction problem was transformed into a non-smooth convex optimization problem under TV regularization constraints. The analytical expression of the hyperparameter-free TV regularization term was derived based on the covariance fitting criterion. Finally, a Generalized Iteratively Reweighted Least Squares (GIRLS) strategy was proposed, and an iterative optimization method for solving the non-smooth convex optimization problem of square-root LASSO was derived. The simulation and experimental results demonstrate that the proposed method improves angular resolution of the radar while preserving the contour information of the target without requiring manual adjustment of the hyperparameters.
Conventional spaceborne monostatic radar systems incur huge engineering costs to achieve small moving-target detection and low anti-interference ability. By manipulating the transmitter-receiver separation in a spaceborne bistatic radar system, the target radar cross section can be effectively improved by adopting a configuration with a large azimuth bistatic angle, and the anti-interference ability can be improved because the receiver does not transmit signals. However, the characteristics of the background clutter echo in a spaceborne bistatic radar system differ drastically from those in a spaceborne monostatic radar system because of the transmitter-receiver separation in the former. To overcome the limitations of existing empirical clutter scattering coefficient models, which typically do not capture the variation of scattering coefficient with azimuth bistatic angle, this study proposes a semiempirical bistatic clutter scattering coefficient model based on the two-scale model. In the proposed model, an empirical clutter backscattering coefficient model can be converted to a bistatic clutter scattering coefficient model based on electromagnetic scattering theories, and the bistatic scattering coefficient is further modified based on the two-scale model. The proposed model was validated using real measured data of bistatic clutter scattering coefficients obtained from existing literature. Using the proposed model, clutter suppression performance under different azimuth bistatic angles was analyzed by employing space-time adaptive processing in spaceborne bistatic radar systems. Reportedly, under HH polarization, the clutter suppression performance was relatively good when the azimuth bistatic angle was 30°~130°, whereas the clutter suppression performance was considerably affected by large-power main-lobe clutter when the azimuth bistatic angle was >150°.
In the field of radar target recognition, the introduction of Icosahedron Triangular Trihedral Corner Reflector (ITTCR) has increased the difficulty of target identification tasks, especially under moderate to high sea states. Under such conditions, the undulating sea surface can couple with an ITTCR to produce scattering characteristics similar to those of the target, resulting in a decline in the performance of traditional target identification methods. As a solution, a joint matrix of polarization features and range was constructed by considering the dominant scattering mechanisms and scattering complexity. This matrix characterizes the component-level differences between ships and ITTCR arrays in the presence of sea clutter. Subsequently, a temporal neural network extracts features from the joint matrices of the vessels and ITTCR arrays, achieving effective target identification. The performance of the proposed method was validated using a dataset. The proposed method effectively reduces information loss during manual knowledge refinement. Under moderate to high sea states, the proposed method has an accuracy higher than that of the existing methods by 10.14%. Furthermore, the proposed method considerably reduces false alarms caused by ITTCR arrays.
The measurement from automotive millimeter-wave radar consists of position coordinates in the polar coordinate system and doppler velocity, which has a complex, nonlinear relationship with the extended object state modeled in the Cartesian coordinate system. To address this nonlinear state estimation problem, a Variational Marginalized Particle Filter-based Extended Object Tracking (VMPF-EOT) algorithm is proposed. First, the object’s two-dimensional planar contour is modeled as an ellipse with an explicitly defined orientation angle. A parameterized inverse gamma distribution is constructed as the conjugate prior distribution for the contour size. Second, the measurement source position is introduced as an auxiliary variable to establish a measurement model for extended objects detected by automotive millimeter-wave radar. To enhance the contour estimation performance for maneuvering objects, the joint distribution of the extended object state is marginalized with respect to the contour orientation angle. The posterior distribution of the contour orientation angle is estimated independently using a particle filter. The approximate analytical solution for the posterior distributions of the remaining state variables—including the target’s center motion state and contour size—is derived using the variational Bayesian inference. The simulation results demonstrate that the proposed algorithm achieves higher state estimation accuracy than existing algorithms. In tracking maneuvering targets, the proposed algorithm offers a more significant advantage in terms of estimating the contour orientation angle and contour size.
The development of intelligent jamming decision-making technology has substantially enhanced the survival and confrontation capabilities of sensitive targets on the battlefield. However, existing jamming decision-making algorithms only consider active jamming while neglecting the optimization of passive jamming strategies. This limitation seriously restricts the application of adversarial models in jamming decision-making scenarios. Aiming to address this defect, this paper constructs a joint optimization method for active-passive jamming strategies based on Rainbow Deep Q-Network (DQN) and dichotomy. The method uses Rainbow DQN to determine the sequence of active and passive jamming styles and applies a dichotomy to dynamically search for the optimal release position of passive jamming. Additionally, considering the partially observable nature of the jamming confrontation environment, this paper further designs an optimization method for active-passive jamming strategies based on Rainbow DQN and Baseline DQN. A reward function is also introduced, based on changes in the radar beam pointing point, to accurately feedback the effectiveness of the jamming strategy. Through simulation experiments in jammer-radar confrontations, the proposed method is compared with the following three mainstream jamming decision models: Baseline DQN, Dueling DQN, and Double DQN. Results show that, compared to other interference decision-making models, the proposed method improves the Q value by an average of 2.43 times, the reward mean value by an average of 3.09 times, and reduces the number of decision-making steps for passive interference location by more than 50%. The experimental results show that the proposed joint active-passive jamming strategy optimization method based on Rainbow DQN and dichotomy substantially enhances the effectiveness of decision-making, improving the applicability of jamming strategy models and drastically boosting the value of the jammer in electronic countermeasures.
In an increasingly complex electromagnetic environment, the composite detection of active-passive radar, with its excellent complementary advantages, has become an important working mode for enhancing the combat capability and anti-interference capability of radars. The traditional single suppression or deception jamming method can only produce effective jamming in active or passive radar mode, and a good jamming effect is difficult to produce on the composite detection of active-passive radar. In order to improve the jamming ability of active-passive radar composite detection, this paper proposes a full-pulse multi-jammer cooperative jamming method for active-passive radar composite detection. By analyzing the principle of Constant False Alarm Rate (CFAR) detection in radar active mode, the power series and position spacing distribution of multiple false targets is adjusted through the correlation between radar detection probability and signal-to-noise ratio, and the full-pulse time-domain rendering covert jamming model is constructed to effectively suppress the radar active mode. At the same time, by analyzing the principle of angle direction finding in radar passive mode, a cooperative jamming strategy based on multiple jammers is proposed, which dynamically adjusts the transmitting power of jammers and generates multiple random deception angles among the jammers to realize the multi-angle deception effect in radar passive mode. Finally, through the organic combination of the aforementioned two strategies, a full-pulse multi-jammer cooperative jamming method is constructed to achieve effective jamming in active-passive radar composite detection. The experimental results show that compared with the traditional single suppression or deception jamming methods, the proposed full-pulse multi-jammer cooperative jamming method can effectively increase the detection threshold of radar CFAR and, reduce the detection probability in the radar active mode. At the same time, different false angles are generated in each frame near the jammer to expand the range of angle deception, to comprehensively improve the jamming performance of active-passive radar composite detection.
The paper proposes a double hierarchical nonhomogeneous multirank target detection method for the distributed Multiple-Input Multiple-Output (MIMO) radar to detect targets in scenarios with subspace interference and nonhomogeneous clutter. First, a multirank target model and subspace interference model are established based on the fact that the target signal and interference are located in two linearly independent subspaces, each with a rank greater than 1. The corresponding subspace matrices of the two subspaces and the coordinate vectors of the corresponding distance units are unknown. Then, a distributed MIMO radar system with a double hierarchical nonhomogeneous structure is designed, and the interference of each transmit-receive pair is nonhomogeneous, i.e., each transmit-receive pair possesses different statistics. In addition, the clutter in one transmit-receive pair is nonhomogeneous. Subsequently, the double hierarchical nonhomogeneous multirank target Rao detector and Wald detector are designed for the distributed MIMO radar in the context of subspace interference. This is achieved by adopting the Rao and Wald test criteria, constructing the parameter estimation strategy to be solved, and estimating the power median normalized covariance. Theoretical derivation showed that the proposed method had a constant false alarm property for the clutter covariance matrix structure. Simulation experiments showed that the proposed method guarantees a constant false alarm property for the clutter covariance matrix structure; in addition, compared with the existing distributed MIMO radar detection methods, the proposed detection method improves the target detection and interference suppression performances.
Transmit coherence synthesis of the target of interest is crucial for achieving full coherence in distributed coherent aperture radars. Interrupted-Sampling Repeater Jamming (ISRJ) in the coherent parameter estimation phase poses great difficulties in transmitting coherence completely. To solve this issue, an interference suppression method based on ISRJ matched filtering features is proposed. This method can overcome the limitations of time-frequency domain filtering under low Jamming-to-Noise Ratio (JNR) conditions while providing a more accurate means of estimating interference parameters for interference reconstruction and cancellation under high JNR conditions. Simulation results showed that the proposed method achieved a significant suppression effect on ISRJ. At low JNRs, the probability of target detection increased by over 40% compared with other methods such as time-frequency domain filtering. At high JNRs, the equivalent signal-to-jamming ratio improved by more than 2.5 dB relative to other approaches.
Clutter suppression is an important technology for moving target indication. However, for Bistatic Synthetic Aperture Radar (BiSAR) moving target indication, traditional space-time adaptive processing and displaced phase center antenna methods cannot achieve the expected clutter suppression because of the strong coupling nonlinearity and nonstationarity of clutter. To address the aforementioned challenge, this study proposes a dual-channel clutter cancellation processing method via space-time decoupling for airborne BiSAR. The core lies in establishing the space-time decoupling matrix, which converts the strongly coupled nonlinear two-dimensional space-time spectrum of airborne BiSAR into that with consistent spatial frequency. The proposed method mainly consists of the following steps: (1) To improve the signal-to-clutter-plus-noise ratio of moving targets, the first-order Keystone transformation and high-order range migration correction function are applied to concentrate the energy of moving targets in the same range cell. (2) To weaken the azimuth spectrum expansion effect caused by the motion of bistatic platforms, the Doppler frequency rate term is compensated for each range cell. (3) To achieve clutter cancellation, the space-time decoupling matrix is introduced. The normalized Doppler frequency remains unchanged, and the clutter atoms on the airborne BiSAR space-time plane are linearly transformed into atomic positions with the same normalized spatial frequency. Then, the echo signals of dual channels are subtracted for effective clutter suppression. The effectiveness of the proposed method for airborne BiSAR clutter suppression is demonstrated through simulation and real data processing.
This study addresses the issue of fine-grained feature extraction and classification for Low-Slow-Small (LSS) targets, such as birds and drones, by proposing a multi-band multi-angle feature fusion classification method. First, data from five types of rotorcraft drones and bird models were collected at multiple angles using K-band and L-band frequency-modulated continuous-wave radars, forming a dataset for LSS target detection. Second, to capture the periodic vibration characteristics of the L-band target signals, empirical mode decomposition was applied to extract high-frequency features and reduce noise interference. For the K-band echo signals, short-time Fourier transform was applied to obtain high-resolution micro-Doppler features from various angles. Based on these features, a Multi-band Multi-angle Feature Fusion Network (MMFFNet) was designed, incorporating an improved convolutional long short-term memory network for temporal feature extraction, along with an attention fusion module and a multiscale feature fusion module. The proposed architecture improves target classification accuracy by integrating features from both bands and angles. Validation using a real-world dataset showed that compared with methods relying on single radar features, the proposed approach improved the classification accuracy for seven types of LSS targets by 3.1% under a high Signal-to-Noise Ratio (SNR) of 5 dB and by 12.3% under a low SNR of −3 dB.
In recent years, target recognition systems based on radar sensor networks have been widely studied in the field of automatic target recognition. These systems observe the target from multiple angles to achieve robust recognition, which also brings the problem of using the correlation and difference information of multiradar sensor echo data. Furthermore, most existing studies used large-scale labeled data to obtain prior knowledge of the target. Considering that a large amount of unlabeled data is not effectively used in target recognition tasks, this paper proposes an HRRP unsupervised target feature extraction method based on Multiple Contrastive Loss (MCL) in radar sensor networks. The proposed method combines instance level loss, Fisher loss, and semantic consistency loss constraints to identify consistent and discriminative feature vectors among the echoes of multiple radar sensors and then use them in subsequent target recognition tasks. Specifically, the original echo data are mapped to the contrast loss space and the semantic label space. In the contrast loss space, the contrastive loss is used to constrain the similarity and aggregation of samples so that the relative and absolute distances between different echoes of the same target obtained by different sensors are reduced while the relative and absolute distances between different target echoes are increased. In the semantic loss space, the extracted discriminant features are used to constrain the semantic labels so that the semantic information and discriminant features are consistent. Experiments on an actual civil aircraft dataset revealed that the target recognition accuracy of the MCL-based method is improved by 0.4% and 1.4%, respectively, compared with the most advanced unsupervised algorithm CC and supervised target recognition algorithm PNN. Further, MCL can effectively improve the target recognition performance of radar sensors when applied in conjunction with the sensors.
Bistatic Synthetic Aperture Radar (SAR), with the separated transmitter and receiver working in coordination, cannot only achieves high-resolution imaging in the forward-looking mode, but also possesses outstanding concealment and anti-interference capabilities. Therefore, bistatic SAR thrives in both civilian and military applications, such as ocean monitoring or reconnaissance imaging. However, ship targets are typically influenced by sea waves, generating unknown and complex three-dimensional oscillations. These random oscillations and radar motions vary with slow time, making the imaging view of bistatic SAR ship targets strongly time-dependent, so that it is extremely difficult to extract effective target features from final imaging results. Moreover, target oscillations are also coupled with the motion of bistatic platforms, which causes severe nonlinear spatial Doppler shifts in target echoes, and thus bistatic SAR images are usually defocused. To address these problems, this paper proposes an imaging method for bistatic SAR ship target by imaging time optimization, which generates well-focused bistatic SAR ship target images with the optimal views. Firstly, short-time Fourier transform is utilized to extract the time-frequency information of the ship. Secondly, based on this time-frequency information from multiple strong scatterers, the optimal three-dimensional rotation parameters are estimated, revealing the time-varying characteristics of the imaging projection plane. Then, the optimal imaging time centers are selected based on the optimal imaging projection planes, while the corresponding optimal imaging time intervals are chosen based on the optimal imaging resolutions. Finally, with the selected optimal imaging times, the desired images of the bistatic SAR ship target are produced. Simulation experiments verify the accuracy of target rotation parameter estimation under different bistatic configurations and noise conditions, as well as the effectiveness of imaging projection plane selection. In general, this method tackles with the issues of the time-varying imaging views of bistatic SAR ship targets and nonlinear spatial Doppler shifts, obtaining well-focused and optimally viewed target images, which significantly enhances the accuracy of subsequent target feature extraction.
This paper addresses the task allocation problem in swarm Unmanned Aerial Vehicle (UAV) Synthetic Aperture Radar (SAR) systems and proposes a method based on low-redundancy chromosome encoding. It starts with a thorough analysis of the relationship between imaging performance and geometric configurations in SAR imaging tasks and accordingly constructs a path function that reflects imaging resolution performance. The task allocation problem is then formulated as a generalized, balanced multiple traveling salesman problem. To enhance the search efficiency and accuracy of the algorithm, a two-part chromosome encoding scheme with low redundancy is introduced. Additionally, considering possible unexpected situations and dynamic changes in practical applications, a dynamic task allocation strategy integrating a contract net protocol and attention mechanisms is proposed. This method can flexibly adjust task allocation strategies based on actual conditions, ensuring the robustness of the system. Simulation experiments validate the effectiveness of the proposed method.
The miniature multistatic Synthetic Aperture Radar (SAR) system uses a flexible configuration of transceiver division compared with the miniature monostatic SAR system, thereby affording the advantages of multi-angle imaging. As the transceiver-separated SAR system uses mutually independent oscillator sources, phase synchronization is necessary for high-precision imaging of the miniature multistatic SAR. Although current research on phase synchronization schemes for bistatic SAR is relatively mature, these schemes are primarily based on the pulse SAR system. However, a paucity of research exists on phase synchronization for the miniature multistatic Frequency Modulated Continuous Wave (FMCW) SAR. In comparison with the pulse SAR, the FMCW SAR system lacks a temporal interval between the transmitted pulses. Consequently, some phase synchronization schemes developed for the pulse SAR system cannot be directly applied to the FMCW SAR system. To this end, this study proposes a novel phase synchronization method for the miniature multistatic FMCW SAR, effectively resolving the problem of the FMCW SAR. This method uses the generalized Short-Time Shift-Orthogonal (STSO) waveform as the phase synchronization signal of disparate radar platforms. The phase error between the radar platforms can be effectively extracted through pulse compression to realize phase synchronization. Compared with the conventional linear frequency-modulated waveform, after the generalized STSO waveform is pulsed by the same pulse compression function, the interference signal energy is concentrated away from the peak of the matching signal and the phase synchronization accuracy is enhanced. Furthermore, the proposed method is adapted to the characteristics of dechirp reception in FMCW miniature multistatic SAR systems, and ground and numerical simulation experiments verify that the proposed method has high synchronization accuracy.
The ionosphere can distort received signals, degrade imaging quality, and decrease interferometric and polarimetric accuracies of spaceborne Synthetic Aperture Radars (SAR). The low-frequency systems operating at L-band and P-band are very susceptible to such problems. From another viewpoint, low-frequency spaceborne SARs can capture ionospheric structures with different spatial scales over the observed scope, and their echo and image data have sufficient ionospheric information, offering great probability for high-precision and high-resolution ionospheric probing. The research progress of ionospheric probing based on spaceborne SARs is reviewed in this paper. The technological system of this field is summarized from three aspects: Mapping of background ionospheric total electron content, tomography of ionospheric electron density, and probing of ionospheric irregularities. The potential of the low-frequency spaceborne SARs in mapping ionospheric local refined structures and global tendency is emphasized, and the future development direction is prospected.