Advancements in electronic technology have made the battlefield electromagnetic environment increasingly complex, posing challenges for radar systems in accurately detecting targets and countering jamming. Especially, lightweight platforms, such as unmanned aerial vehicles and individual soldier devices, have limited computational resources, and there is an urgent need for jamming recognition models with low power consumption and strong real-time performance. However, existing deep-learning-based methods suffer from high computational costs, large model sizes in the algorithms, and poor adaptability to composite jamming scenarios. To address these limitations, this article proposes a lightweight collaborative optimization binary neural network (LCO-BNN) for radar jamming recognition. First, the LCO-BNN integrates a two-stream input that combines cross-ambiguity function contours and range-Doppler spectrograms to extract complementary spatiotemporal features. Second, although binary neural networks with 1-bit weights and activations significantly reduce computational costs, their severe information degradation hinders deployment. To mitigate this, we propose two mechanisms: variance-aligned adaptive scaling (VAS) in forward propagation, which enables the binary weights to approximate the statistical distribution of the original weights and effectively preserve a greater amount of information, and gradient-adaptive coordination in backward propagation to guarantee stable gradient updates. Furthermore, the network employs binary depthwise separable convolutions and real-valued retention skip, which efficiently balance efficiency and accuracy while lowering computational complexity and information loss. Experimental results demonstrate that the proposed LCO-BNN achieves a recognition accuracy of 99.33% under mixed jamming-to-noise ratio conditions. Compared with other full-precision models such as 2-D convolutional neural network, it significantly reduces the number of parameters to 4.30 Mbit and computational 1.34 & times; 10(6) floating point operations (FLOPs), minimizing resource overhead by orders of magnitude. Furthermore, it outperforms existing binary methods with an accuracy improvement of at least 5.92%, providing a robust and low-power solution for real-time jamming recognition in resource-limited battlefield scenarios.

The escalating spectrum congestion has driven the rapid development of radar-communication coexistence (RCC) systems, where resource allocation is critical to addressing constraints such as power budgets and inter-system interference. This paper proposes a joint radar-target association, communication user association, and power allocation (JRTACUAPA) strategy for a distributed RCC network. For the scenario of resource being enough to support performance requirements, a direct resource minimization optimization model is established. For resource scarce scenarios, the optimization objective is to minimize the deviation between the actual performance of target tracking and communication services and their predefined performance thresholds. In two optimization models, the Bayesian Cram´er-Rao lower bound is adopted to quantify target tracking accuracy, and the communication data ratio is used to evaluate communication quality. The formulated problems are nonconvex and mixed integer programming problems, which are solved by a two stage iterative expansion-based optimization method. This method iteratively expands the feasible solution domains for radar-target association and communication-user association, while optimizing power allocation using the primal-dual interior point method to ensure efficiency. Extensive simulations validate that the proposed JRTACUAPA strategy exhibits strong adaptability to both resource-abundant and resource-scarce scenarios, addressing the limitation of existing direct resource minimization models that fail to provide feasible solutions under resource constraints. The results also confirm the effectiveness and efficiency of the proposed method compared to state-of-the-art methods.

The space-based early warning radar (SBEWR) boasts an extensive surveillance range and holds significant strategic value. However, it faces the challenge of severe range-ambiguous clutter, resulting in considerable difficulties in clutter suppression. In view of the large number of range ambiguity numbers of the SBEWR clutter and the limitations of algorithm's real-time performance and the number of training samples, a prior-knowledge-based weight solution criterion in the carrier-frequency domain, termed linear constraint-sidelobe control (LC-SC) is proposed in this paper. The weight constructed based on the LC-SC criterion can form a deep notch in the slant range of near-range clutter through linear constraints, and performs low sidelobe control in the slant ranges corresponding to other range ambiguous clutter. Using this weight for prefiltering can effectively suppress range ambiguous clutter and extracts the echoes in the required range area. Then, the remaining clutter is suppressed through two-dimensional (2D) adaptive processing in azimuth-Doppler domain. The results of the simulation attest to the efficiency of the proposed algorithm.

In complex operational environments, radar systems often encounter clutter exhibiting nonzero-mean characteristics, while simultaneously receiving target signals subject to mismatch. These factors can severely degrade detection performance. To address the combined challenges of signal mismatch and nonzero-mean clutter, we propose two adaptive detectors with enhanced sensitivity to mismatched signals. Our approach involves reformulating the original binary hypothesis test by incorporating artificial interference under the null hypothesis and imposing appropriate constraints on this interference. We further derive the statistical properties of both detectors and analyze how signal mismatch and nonzero-mean clutter influence their detection performance. The results demonstrate that both detectors effectively suppress nonzero-mean clutter while maintaining sensitivity to mismatched signals. Extensive simulations and real-data experiments validate the effectiveness of the proposed detectors.

As a cost-effective and robust technology, automotive radar has seen steady improvement during the last years. Radio frequency (RF) images, serving as a radar data format with rich semantic information, have attracted considerable interest in radar object detection. Previous RF-based models heavily rely on convolutional neural networks, leading to the high computational cost. To solve this problem, we propose a model called Mask-RadarNet to fully utilize the hierarchical semantic features from the RF image sequences. Mask-RadarNet exploits the combination of interleaved convolution and attention operations in the encoder. In addition, patch shift is introduced to Mask-RadarNet for efficient spatial-temporal feature learning. By shifting part of patches with a specific mosaic pattern in the temporal dimension, Mask-RadarNet achieves competitive performance while reducing the computational burden of the spatial-temporal modeling. In order to capture the spatial-temporal semantic contextual information, we design the class masking attention module (CMAM) in our encoder. Moreover, a lightweight auxiliary decoder is added to our model to aggregate prior maps generated from the CMAM. Experiments on the CRUW dataset demonstrate that the proposed Mask-RadarNet achieves state-of-the-art performance with relatively lower computational complexity and fewer parameters.

The bistatic space-based early warning radar (SBEWR) needs to detect air moving targets in the presence of strong ground clutter. Meanwhile, the rapidly changing geometric relationship between the satellite and the targets, particularly for highly maneuvering targets, results in severe range cell migration and Doppler cell migration. The combined effects of clutter and target defocus significantly degrade target detection performance, especially for weak targets. To address these challenges, this article investigates a long-time coherent integration approach for maneuvering target detection in strong clutter environments. First, to suppress grating lobe clutter and nonstationary clutter, we propose a weighted 3-D adjacent subarray synthesis space-time adaptive processing (STAP) method. In this approach, grating lobe clutter is eliminated through weighted subarray synthesis, while the elevation degree of freedom is introduced into adaptive processing to suppress the elevation sidelobe clutter that contributes to clutter nonstationarity. Second, following STAP processing within each subcoherent processing interval (CPI), an adaptive method is introduced to identify the residual mainlobe clutter region, which is subsequently zeroed out to mitigate its adverse effect on target coherent integration. Finally, we derive and discuss the inter-CPI coherence of the target after clutter suppression in each sub-CPI and, based on this, propose the use of generalized Radon transform to achieve target coherent integration across sub-CPIs. Simulation results validate the effectiveness of the proposed method.

In spaceborne bistatic radar systems, the large spatial separation between the transmitter and the receiver, combined with the high-velocity motion of satellite platforms, causes the clutter to exhibit pronounced range-dependent behavior in both the Doppler and spatial domains. This range dependence severely limits the number of independent identically distributed training samples available for space-time adaptive processing, thereby degrading clutter suppression performance. To address this issue, a generalized space-time clutter model applicable to arbitrary bistatic satellite geometries and array configurations is first established based on an unperturbed orbital motion model. Building on this foundation, the clutter suppression characteristics of high-transmitter low-receiver configurations are examined. Considering key factors such as observation-region latitude, maximum common dwell time, and Doppler spread divergence, four representative spaceborne bistatic configurations are designed to evaluate clutter suppression performance, through which the most and least favorable orbital geometries are identified. Furthermore, to mitigate nonstationary clutter and enhance airborne moving target indication capability, a dimensionality-reduced clutter suppression method based on a 3-D sum-difference beam structure is proposed. Simulation results demonstrate that the joint optimization of bistatic configuration and the proposed algorithm effectively alleviates nonstationary clutter and substantially improves the minimum detectable velocity for airborne moving targets.

Interrupted sampling repeater jamming (ISRJ) is a coherent jamming technique combining both deception and suppression capabilities, posing a serious threat to pulse-Doppler radar. Although existing studies have proposed various waveform design methods to counter the ISRJ, these approaches often struggle to balance jamming suppression performance and Doppler tolerance, limiting their practical application in high-speed target detection scenarios. To address this issue, a joint waveform and filter design method is proposed based on a time-domain masking principle. Precisely, the operational waveform and the operational filter adopt a linear frequency modulation (LFM) scheme to preserve Doppler tolerance, meeting the detection requirements for moving targets; whereas the masking waveform and the masking filter are designed using phase-coded sequences. An objective function is formulated by combining the jamming integration energy and the squared absolute deviation of the pulse compression peak gain from predefined thresholds. Further, a joint optimization framework based on Block Coordinate Descent (BCD) and Majorization-Minimization (MM) are employed to iteratively solve for the masking waveform and filter. In addition, the Lanczos method is introduced to reduce the computational burden of the proposed algorithm. Finally, simulation results demonstrate that the proposed joint design algorithm effectively suppresses ISRJ while maintaining satisfied Doppler tolerance. Compared with existing methods, the designed waveform and filter exhibit superior performance in both anti-jamming capability and Doppler adaptability.

In the adaptive detection process of target signal under interference and Gaussian clutter, the issue of insufficient training samples often degrades the performance of detectors. To this end, we exploit the persymmetry of clutter covariance matrix to enhance the detection performance. The interference and target signal are described as subspace models, namely, it is assumed to lie in different deterministic subspaces, but with unknown coordinates. Above that, two persymmetric adaptive detectors are designed resorting to the Gradient and Durbin criteria, respectively. Numerical Monte Carlo experimental examples indicate that these persymmetric detectors exhibit superior performance to existing counterparts in some scenarios and possess the constant false alarm rate (CFAR) property.

Drone swarm array configuration varies with the unmanned aerial vehicle (UAV) positions, and thus provides extra antenna position degree-of-freedom (DOF) comparing to fixed array configuration. In this paper, we utilize the DOFs in both drone swarm waveform and antenna positions to jointly design waveform and array for integrated sensing and interfering (ISI), where a generalized likelihood ratio test (GLRT) is devised to decide if there exists a target at a known location or not; while a range profile-based electromagnetic disguise is carried out for the interference purpose. Then, the matrix inversion lemma is applied to eliminate the challenging fraction term resulted from the GLRT signal-absence and signal-presence hypotheses, and derive simplified detection metric expression. Moreover, to tackle the extremely complicated inversion operator in the projection matrix of the clutter steering matrix, we derive a recursive scheme to reformulate a series of generalized Rayleigh entropy problems. While utilizing the rank-1 and orthogonal properties, the entropy problem is simplified as a constant modulus optimization problem. Numerical results demonstrate the excellent performance of the proposed solutions.

Space-time adaptive processing (STAP) has proven to be a critical technique for clutter suppression in space-based radar (SBR) systems. For SBR, platform high-speed motion and the large antenna apertures induce baseline geometric distortions, creating range-varying space-time coupled phase errors. These errors interact with inherent channel amplitude-phase errors, significantly aggravating channel decorrelation effects and degrading clutter covariance matrix (CCM) estimation accuracy, ultimately leading to severe performance degradation of STAP processors. Existing studies primarily analyze error impacts on STAP performance, effective calibration methods to enhance clutter suppression performance remain underdeveloped. This article proposes a clutter suppression algorithm for SBR based on phase error calibration. A three-dimensional (3D) space-time-baseline coupling model under Earth rotation constraints is established, which reveals the range-dependent propagation characteristics of phase errors and their destructive mechanisms on CCM estimation. Based on this foundation, a two-stage processing framework is constructed. In the first stage, the joint processing of orthogonal coded waveform design and compressed sensing is implemented to achieve near-range nonstationary clutter suppression and ambiguous range cell decoupling. The second stage develops a hierarchical error calibration mechanism. Initially, a coarse correlation-based estimator is built by exploiting the local stationarity of phase errors within ambiguous range cells to extract locally consistent features. Subsequently, a full-range phase error characterization framework with low-order polynomial parameterization is established, where high-precision error estimation and compensation across all range dimensions are realized through the model coefficient optimization. Simulation results demonstrate that the proposed algorithm effectively calibrates the impact of phase errors on CCM estimation accuracy, resulting in significant improvement of clutter suppression performance in subsequent STAP processing.

The problem of detecting a point-like target in the presence of signal mismatch is considered in this paper. To design selective detectors, a random fictitious signal is introduced under the null hypothesis. This signal is designed to capture mismatched components through its specific structure, thereby enhancing the plausibility of the null hypothesis when signal mismatch occurs. The generalized likelihood ratio test (GLRT) criterion is adopted to solve the detection problem. Furthermore, a tunable detector is proposed based on the derived GLRT statistic to enable flexible enhancement of the selectivity. Closed-form expressions for the probabilities of detection (PDs) and false alarm (PFAs) are derived for both detectors, confirming their constant false alarm rate (CFAR) property. In the absence of signal mismatch, the proposed GLRT, with appropriate parameters, achieves a signal-to-noise ratio (SNR) gain of nearly 4 dB compared to the well-known whitened adaptive beamformer orthogonal rejection test (W-ABORT) at a PD of 0.9. When signal mismatch occurs, the proposed tunable GLRT exhibits superior selectivity against mismatched signals once the tuning parameter exceeds 0.4, outperforming the W-ABORT. The effectiveness of the proposed detectors has been validated through both simulations and real-data experiments.

This paper addresses the issue of target detection under unknown Gaussian interference using a frequency diverse array multi-input multi-output (FDA-MIMO) radar system. To solve the detection problem where the position of the target within each range cell is unknown, we utilize the Rao and Wald tests to derive two detection frameworks regarding the target incremental range. Then, we equivalently transform the detection problem into an optimization problem of the semidefinite programming (SDP), which can obtain the maximum likelihood estimate (MLE) of the target incremental range within the radar range cell. Moreover, detection performance comparisons are carried out among two newly-proposed adaptive detectors based on Rao and Wald tests, as well as the existing one-step generalized likelihood ratio test (1S-GLRT) based detector. Simulation results demonstrate that the proposed Rao and Wald detectors possess performance improvement under some parameter settings.

To effectively suppress interrupted sampling repeater jamming (ISRJ), this paper proposes an ISRJ suppression method based on time-frequency analysis combined with frequency-dimension projection, leveraging the distinct time-frequency amplitude responses of target echo signals and ISRJ signals after dechirping. Firstly, a bidirectional sliding window detection method is used to locate the echo pulses. Subsequently, the time-frequency matrix of the localized region is projected onto the frequency dimension, and a differential method is applied to preliminarily identify peak points of jamming and targets. Then, the standard deviation from statistics is utilized as a discriminant metric to distinguish between target and jamming peaks, followed by the construction of a time-frequency filter for jamming suppression. Finally, performing inverse Short-Time Fourier Transform (STFT) processing on the jamming-suppressed time-frequency matrix to obtain the final suppression results. Simulation experiments demonstrate the proposed algorithm exhibiting superior jamming suppression performance, particularly addressing the limitations of traditional methods under low signal-to-noise ratio (SNR) conditions.

By applying linear prediction in adaptive signal processing to fractional Fourier transform (FRFT), a signal processing technique, named fractional bandwidth extrapolation (FBWE) is proposed to solve the problem of resolution enhancement in the fractional domain. The technique consists of a three-step process, firstly calculating the p+1 order FRFT, then predicting the past and future of the signal in fractional domain, and finally calculating the inverse Fourier transform of the extrapolated signal, which can obtain a sharpened FRFT image without distortion. For linear-frequency-modulated signal, the peaks in image are narrowed in both the fractional domain and order dimension, and signal parameters can be estimated with smaller errors. The similar results for two-dimensional (2D) signal can be obtained by 2D-FBWE. Interestingly, 2D-FBWE can also enhance image resolution by narrowing the lines and spots. Using both the simulated and measured data, the performance of FBWE is fully verified. The results show that FBWE is valuable and can be used in many fields, such as radar and speech signal processing, image processing, etc.

Space-time adaptive processing (STAP) suffers severe performance degradation in airborne bistatic radar, as range-ambiguous clutter exacerbates the inherent clutter nonstationarity. This article addresses the limitations of conventional beamforming (CBF)-based range-disambiguation methods. We first clarify the core trade-off between range disambiguation accuracy and STAP-accessible degrees of freedom in subarray design. Guided by this, an initial solution utilizing a blocking matrix cascaded with adaptive beamforming (ABF) is proposed. However, in-depth analysis reveals a critical flaw: Increasing the subarray size leads to the blocking-matrix-induced noise distortion broadening the mainlobe clutter-suppression notch width. To resolve this, we integrate an innovative zero-phase component analysis (ZCA) whitening technique, developing a blocking matrix-ZCA-ABF joint processing framework. Simulations demonstrate that the proposed method effectively separates range-ambiguous clutter, laying a solid foundation for subsequent nonstationary clutter compensation and STAP suppression. It delivers superior, robust clutter suppression across diverse subarray parameters, narrows the mainlobe clutter-suppression notch, limits the output signal-to-clutter-plus-noise ratio loss to within 3 dB, and maintains target detection power and accuracy simultaneously. Accordingly, it offers reliable technical support for airborne bistatic radar target detection in complex scenarios.

Resource allocation and detection threshold adaption are critical to unlocking the full potential of Phased Array Radar (PAR) network for maneuvering target tracking under clutter environment. A Dwell Time Allocation (DTA) incorporating Interacting Multiple Model-Bayesian Detector (IMM-BD) is proposed in this background. The IMM-BD is derived to tune detection thresholds. The information reduction factor influenced by IMM-BD is calculated, which is then embedded in the derived posterior Cramér-Rao lower bound in IMM framework. The DTA optimization model is formulated as minimizing the sum of weighted predicted PCRLBs under dwell time budget of each PAR. It is shown that the optimization model is nonconvex. The modified gradient projection method is proposed for solution. Simulation results confirm the effectiveness and efficiency of proposed strategy in terms of lower tracking errors and lower track loss ratios, compared with state-of-the-art algorithms.

Existing deep learning (DL)-based direction of arrival (DOA) estimation methods have achieved varying degrees of robustness to undesirable scenarios, but are limited by unrealistic prior knowledge of the number of sources and can only locate a small number of sources. Considering that, a deep neural network composed of three cascaded subnetworks is developed to extend the advantages and overcome the limitations. The proposed method adopts a processing flow of data rectification, source number estimation, and DOA estimation, which are accomplished by the developed three subnetworks, i.e., an autoencoder (AE), a classification convolutional neural network (CNN), and a regression CNN, respectively. The AE is used to improve the DOA estimation performance in practical scenarios afflicted by low signal-to-noise-ratio (SNR), array imperfections, Gaussian colored noise, and source correlation. The classification CNN is used to estimate the number of sources based on the power feature of the preprocessed signal. The regression CNN, which incorporates modified inception blocks with dilated convolutions for angle feature aggregation, is used for multi-source DOA regression with greatly improved accuracy. Despite the assembly of three subnetworks, the proposed method still maintains a reasonable model complexity due to the special network design for small sample sizes and the decomposition of ordinary convolutions into pointwise and depthwise convolutions. Extensive simulations are presented to illustrate the superiority of the proposed method over the state-of-the-art methods.

To solve the problem of detecting subspace signals in nonzero-mean clutter, we propose adaptive detectors, based on the strategies of generalized likelihood ratio test (GLRT), Rao test, Wald test, gradient test, and Durbin test. The results show that the detectors based on GLRT, Rao and Wald are structurally consistent with the subspace detectors in zero-means clutter. The analytic expressions for the probability of detection (PD) and probability of false alarm (PFA) of each detector are derived, and two major performance differences in the nonzero-mean clutter scenario are revealed. One is the loss of degree of freedom (DOF), which is reduced by 1 compared with the zero-mean clutter scenario. The second is the loss of signal-to-clutter (SCR) ratio. Simulation and measured data verify the effectiveness of the proposed detectors and demonstrate their practical value in real-world radar systems.

In radar maritime target detection tasks, most radars need to cover a wide observation area by scanning. The beam can not stay in one direction for a long time to obtain multiple accumulated pulses. Therefore, the detection capability of weak targets is limited. In addition, affected by the complex characteristics of strong sea clutter, the detection method based on statistical theory and model features are insufficient to distinguish between target and clutter signals. By transmitting omnidirectional radar signals, digital array ubiquitous radar can realize simultaneous continuous observation in a wide area. Long-term pulse accumulation can be achieved, improving the detection performance of weak targets. In this paper, we propose a Spatial-Temporal Graph Fourier Transform (ST-GFT) target detection method, which extract Eigenvalue-Doppler (E-D) features from high-dimensional signals of digital array radar. The proposed method is not affected by the direction of target echo. Through separating target and sea clutter signals, target features are enhanced, and target detection performance is improved. Comparing with the conventional Digital Beam Forming and Moving Target Detection (DBF-MTD) method, the proposed method increases the target Signal to Clutter Ratio (SCR) by 21.59dB in the Quadcopter detection experiment in sea clutter background.