In synthetic aperture radar (SAR) target recognition, the interpretation of SAR images and the accurate recognition of targets are significantly affected by speckle noise. Traditional recognition methods often fail to meet the high accuracy requirements. Therefore, this letter proposes a dual-ended fusion network (DEFNet) for SAR target recognition. The network consists of three main components: the local refinement feature extraction (LRFE) network, the large-scale feature extraction (LSFE) network, and the adaptive feature interaction fusion (AFIF) module. It employs a parallel structure, utilizing the LRFE and LSFE branch modules to extract local and large-scale features, respectively. These two features are then adaptively fused through the AFIF module to further enhance feature representation. Experimental results on the moving and stationary target acquisition and recognition dataset indicate that DEFNet demonstrates significant performance improvement compared to traditional methods, showcasing its effectiveness and adaptability in SAR target recognition tasks.

Large Language Models (LLMs) generate realistic and coherent text, boosting efficiency and decision-making in various fields. However, their generative capabilities pose risks of intellectual property abuse. Watermarking technology offers a solution for information hiding in LLMs, with large model watermarking gaining attention for its unique methods. A critical challenge is embedding watermarks with minimal impact on text quality while ensuring rapid detection, making it an urgent issue to address. In this paper, to address these challenge, we propose a universal and robust tagged watermark technology for LLMs (TWT-LLM). Firstly, the method of TWT embeds a certain amount of watermark information in the sampling process while generating subsequent word text based on the prompt. Then, to enhance the quality of the generated text, we have proposed a group-based local watermark embedding method, which significantly reduces the impact on text quality. The method involves tagging tokens within each group, only the tokens that have been tagged will embed the watermark information. Moreover, to detect the watermark information in the generated text, we have designed a detection method specifically for this watermark embedding technique. Finally, we conducted experiments using the C4 and HC3 datasets, demonstrating that TWT-LLM achieves a lower False Negative Rate and is lighter compared to state-of-the-art methods.

The autonomous underwater vehicle (AUV) are experiencing widely deployment for the advantages of low cost and flexibility. However, the open light-of-sight communication links between clusters to clusters (C2C) in flying ad-hoc networks (FANETs) are vulnerable to malicious jamming in communication denied environments (CDEs), leading to the C2C communication with low-signal-to-interference-plus-noise ratio (SINR). Aiming at the collaborative search inefficiency resulting from unreliable C2C communication caused by low SINR in existing researches, we propose a distributed collaborative search planning method based on anti-jamming strategy by virtual multiple input-multiple output (VMIMO) in FANETs. First, we formulate the anti-jamming C2C collaborative communication model based on VMIMO to combat the malicious jamming utilizing the advantage of large-scale AUVs under limited resource. Then, we model the overall search objective function, considering search, connectivity and collision avoidance between AUV clusters. And we establish a distributed collaborative search optimization problem model based on distributed model predictive control (DMPC) framework, so that all AUV clusters can optimize the overall search objective by interacting with neighbor clusters. Finally, we propose a three-level collaborative optimization problem model with joint considering search, anti-jamming communication and network connectivity. To calculate the optimal solution, an alternating optimization method based on distributed stochastic algorithm is proposed to solve the three-level collaborative optimization problem model. Moreover, we conduct extensive simulations and the results show that the proposed method is efficient in terms of the anti-jamming ability and search efficiency compared with other state-of-the-art algorithms without cooperative strategy.

Traditional radar signal modulation recognition (RSMR) methods struggle to achieve the required accuracy under low signal-to-noise ratio (SNR) conditions. To address this issue, a hybrid network architecture integrating deformable convolution and mamba (DCMNet) is proposed. Specifically, DCMNet employs a multi-view feature extraction structure that combines inverted deformable convolution (IDC) with a state space model (SSM), enabling dynamic adjustment of convolution kernel positions and capturing global information and dependencies in long sequence data. The cross-gated feature fusion (CGFF) mechanism effectively modulates and dynamically aggregates features from different perspectives. The lightweight design provides significant advantages in terms of network scale and deployment. Experimental results demonstrate that the proposed method achieves excellent performance on a dataset with ten different waveforms. Notably, at an SNR of -8 dB, the recognition accuracy exceeds 90%, significantly outperforming existing methods.

To tackle the issue of space-time adaptive processing (STAP) performance degradation caused by inaccurate estimation of the clutter covariance matrix (CCM) with limited sample support, a knowledge-aided (KA) CCM estimation algorithm based on the symmetric alternating direction method of multiplier (KA-SADMM) is proposed. The CCM estimation problem is constructed based on the knowledge of persymmetric structure, the low-rank structure, and the prior covariance matrix, and the solution to the resulting problem is derived. Moreover, the contraction property of the sequence generated by KA-SADMM with respect to the solution set of the estimation problem is analyzed. With limited training samples, the simulation results show that the proposed algorithm improves the STAP performance over other similar algorithms by at least about 0.2 dB when the prior knowledge is accurate and by at least 0.3 dB when the prior knowledge is inaccurate.

Excellent performance in supervised learning-based radar signal modulation recognition (RSMR) techniques relies on the quantity and quality of labeled datasets, while the high cost and difficulty involved in analyzing and labeling radar signal samples limits its development. An RSMR algorithm using self-supervised contrastive learning (SSCL) methodology is proposed to address this issue. Specifically, within the classical contrastive learning (CL) framework MoCo V2, a customized data augmentation method is devised to capture time-frequency features of the radar signal. In addition, the feature extraction network ResNet50 is improved by decoupling spatial and channel filters, resulting in greater sensitivity to the time-frequency features. To enhance the recognition accuracy, two loss functions, alignment and uniformity, are used in place of the info noise contrastive estimation (InfoNCE) loss, and both loss functions are optimized directly. The recognition accuracy of the proposed method can reach 97.66% at a signal-to-noise ratio (SNR) of 4 dB.

Target search, localization and tracking by unmanned aerial vehicle (UAV) swarms have attracted much attention recently in research and industrial applications. Consequently, there is a high demand for effective mission planning methods for UAV swarms in air transportation. However, it’s difficult to obtain the real-time mission planning in the uncertain dynamic environment. In response to tackle the problem, we model the mission planning problem in the uncertain dynamic environment as a dynamic multi-constraint and multi-objective optimization problems (DMCMOP) and propose the dynamic constrained two-archive evolutionary algorithm (DCTAEA) to realize the efficient mission planning. The proposed method can reconstruct the convergence archive (CA) and the diversity archive (DA) adaptively, and introduce the dynamic self-adaptive penalty mechanism into the CA updating, DA updating and the mating selection, which utilizes valuable infeasible solutions and promote population convergence. Consequently, the proposed algorithm can balance convergence, diversity and feasibility simultaneously. Comprehensive experiments on the real scenes and benchmark problem demonstrate that, compared with state-of-the-art algorithms, the proposed algorithm has the superiority and effectiveness.

Multiple unmanned aerial vehicle (Multi-UAV) cooperative area search is an important and effective means of intelligence acquisition and disaster rescue. Search path planning is a critical factor to improve multi-UAV search performance. Aiming at the search inefficiency resulting from insufficient cooperation between UAVs in existing researches, we present a novel distributed real-time search path planning method based on distributed model predictive control (DMPC) framework. Firstly, we formulate the overall search objective function in finite time domain, considering not only repeated searches, but also maintenance of connectivity and collision avoidance between UAVs. Secondly, we decompose the overall search objective function to establish a distributed constrained optimization problem (DCOP) model, so that all UAVs optimize the overall search objective by interacting with neighbors. Thirdly, aiming at the problem of falling into the local optima in existing algorithms, distributed stochastic algorithm based on enhanced genetic algorithm (DSA-EGA) is proposed to solve the established DCOP model. We design a point crossover operator and introduce anytime local search (ALS) framework that stores the global optimal solution explored. Finally, the simulation results of different benchmark problems demonstrate that the proposed DSA-EGA outperforms other state-of-the-art algorithms in terms of the quality of solution. The simulation results of cooperative area search problems illustrate that the established DCOP model improves the search efficiency by 7.7%, and DSA-EGA improves the search efficiency by 4.3% at least. In addition, we also verify that our method has high scalability.

Unmanned aerial vehicle (UAV) swarms have shown great potential for Internet of Things (IoT). Meantime, its malicious use may cause huge threat to the national security. UAV swarms show the characteristic of high density which poses formidable challenges to radar resolution in the defense of critical areas. In this article, we consider a radar equipped with the coprime array, and then, use the coherent long-time integration (LTI) technique and gridless sparse technique to detect and localize UAVs in a swarm. This strategy takes full account of advantages of the coprime array, coherent LTI technique, and gridless sparse technique, i.e.: 1) the coprime array can provide a larger array aperture than the uniform linear array with the same number of array elements to relieve the stress of the gridless sparse technique and 2) the combination of coherent LTI technique and gridless sparse technique can maximize their advantages and make up for their shortcomings. By mathematical analyses and extensive numerical examples, we show the superiority of the proposed strategy in terms of accurate detection and localization of UAV swarms.

In this letter, considering the inverse synthetic aperture radar (ISAR) imaging of nonuniformly rotating targets under a low signal-to-noise ratio (SNR) environment, an effective ISAR imaging algorithm based on the coherently integrated nonuniform trilinear autocorrelation function (CINTAF) is proposed. Because the definition of a nonuniform trilinear autocorrelation function (NTAF) which enables a coherent accumulation of the signal energy in both the time and lag-time domains, the proposed method has a significant antinoise performance improvement in comparison with other algorithms, while the computational complexity remains similar. The effectiveness and superiority of this new method have been demonstrated through several simulation results.

Various kinds of data are generated from industrial Internet of Things, and these data can be applied for connecting production equipment, identifying and locating items, etc. These data should be forwarded to the decision center for further analyses, especially in wartime. Thus, the channel status information (CSI) for industrial big data transmission has to be acquired. In this article, we develop a system architecture for industrial big data (BD) transmission based on radar-communication integration with arbitrary geometrical array. The traditional channel estimation method, which usually utilizes the regular antenna array to estimate the CSI, cannot be applied to the arbitrary geometrical array. Here, we use the manifold separation technique to transform the complex array configuration into regular array and the downlink channel covariance matrix is estimated by exploiting the frequency calibration technique when the uplink channel covariance matrix is received. The computational complexity for the proposed method and other state-of-the-art methods are analyzed. The simulation results prove that the proposed method can achieve excellent estimation performance for its application in radar-communication integration.

Unmanned aerial vehicle (UAV) swarms-enabled mobile edge computing system can be deployed in critical industrial zones for monitoring. Meanwhile, its malicious use may bring great threat to the security, and the accurate detection, and localization are important. UAV swarms show characteristics of the high density, small radar cross section, far range, and time-varying motion, and have posed formidable challenges to the accurate detection and localization. In this article, the accurate detection and localization of UAV swarms are investigated, and an effective method is proposed based on the Dechirp-keystone transform, and frequency-selective reweighted trace minimization. It inherits high robustness of the coherent long-time integration technique and superresolution of the gridless sparse technique. Mathematical analyzes and numerical simulations validate its superiorities in accurate detection and localization of UAV swarms.

To solve target recognition under extended environments, this paper proposes sparse representation in the transformed domain. Since the signal energy in the frequency domain is mainly concentrated on a small portion of low frequencies, this part of spectrum therefore carry the vital information that distinguishes a class of target from the other. We intend to define a frequency descriptor by the bag of low frequencies. The defined descriptor is used to build sparse signal modeling. The frequency descriptors of the training are concatenated to form an over-complete dictionary. It is used to encode the counterpart of query as a linear combination of themselves. Sparsity has been harnessed to generate the optimal representation, from which the inference can be reached.

For desired resolution, high-squint SAR has a large coherent processing interval (CPI). In this case, the high signal-to-clutter ratio (SCR) echo of moving target can not be guaranteed due to the presence of ground clutter, and the maneuvering motion of moving target usually causes high-order phase terms, which can not be neglected for precise focusing. In this paper, the climb method is applied to the range-compressed echo containing moving target to improve the SCR. For the subsequent focusing, we assume that the target of interest has constant velocity in subaperture CPI, but maneuvering motion parameters for the whole CPI. Within the short subaperture CPI, the target signal can be simplified as a three-order polynomial function with the unknown coefficients to be estimated by some time-frequency analysis tools, including Hough transform and fractional Fourier transform. Then the subaperture Doppler parameters are combined to form a total least square problem, outputting the high-order phase terms. The effectiveness of the proposed GMTIm method is validated by real-measured high-squint SAR data.

This letter addresses the feature detection design for the target embedded in sea clutter. Three polarization features (the relative surface scattering power, the relative volume scattering power, and the relative dihedral scattering power) are obtained based on the observed multipolarization channel returns. Then, 3-D feature detector is constructed to detect the sea-surface small floating target based on the three polarization features. The experiments based on the measured radar data show that the proposed method attains better detection performance and better robustness than do several existed feature-class detectors.

This paper proposes a new method to focus a ground moving target with complex motions and estimate its motion parameters in a synthetic aperture radar (SAR) system. In this method, the second-order Keystone transform is applied to correct the range curvature. Then, the Hough transform is applied to estimate the slope of the range walk trajectory, from which the target cross-track velocity is obtained. Finally, a generalized Hough-high-order ambiguity function (GHHAF) transform is applied to transform the target signal into a 2-D time-frequency plane and estimate its slope associated with the third-order Doppler parameter. Compared with the conventional SAR imaging methods using the second-order phase model, the proposed method can obtain better imaging quality since the third-order Doppler frequency migration is effectively eliminated. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

In inverse synthetic aperture radar (ISAR) imaging of nonuniformly rotating targets, such as highly maneuvering airplanes and ships fluctuating with oceanic waves, azimuth echoes have to be modeled as cubic phase signals (CPSs) after the range migration compensation and the translational-induced phase error correction. For the CPS model, the chirp rate and the quadratic chirp rate, which deteriorate the azimuth focusing quality due to the Doppler frequency shift, need to be estimated with a parameter estimation algorithm. In this paper, by employing the proposed generalized scaled Fourier transform (GSCFT) and the nonuniform fast Fourier transform (NUFFT), a fast parameter estimation algorithm is presented and utilized in the ISAR imaging of the nonuniformly rotating target. Compared to the scaled Fourier transform-based algorithm, advantages of the fast parameter estimation algorithm include the following: 1) the computational cost is lower due to the utilization of the NUFFT, and 2) the GSCFT has a wider applicability in ISAR imaging applications. The CPS model and the algorithm implementation are verified with the real radar data of a ship target. In addition, the cross-term, which plays an important role in correlation algorithms, is analyzed for the fast parameter estimation algorithm. Through simulations of the synthetic data and the real radar data, we verify the effectiveness of the fast parameter estimation algorithm and the corresponding ISAR imaging algorithm.

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motions such as fluctuating ships with oceanic waves and high maneuvering airplanes, the azimuth echo signals can be modeled as cubic phase signals (CPSs). In this letter, a new ISAR imaging algorithm based on the keystone time-chirp rate distribution (KTCRD) is proposed for the targets with complex motions. Compared with the recently published algorithms for the CPSs, the KTCRD can estimate the parameters of multicomponent CPSs without searching procedures and can acquire high antinoise performance with a relatively low computational load. With the estimated motion parameters, high-quality ISAR images can be obtained. Several simulation examples on the synthetic model are shown to validate the effectiveness of the new algorithm presented in this letter.

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motion such as fluctuating ships with oceanic waves and high maneuvering airplanes, the azimuth echo signals can be modeled as cubic phase signals (CPSs) after the migration compensation. The chirp rate (CR) and the quadratic chirp rate (QCR) are two important physical quantities of the CPS, which deteriorate the azimuth focusing quality due to the Doppler frequency shift. With these two quantities, other parameters can be estimated by using the fast Fourier transform (FFT). Therefore, the CPS can be uniquely determined by both CR and QCR. In this paper, based on the proposed generalized keystone transform and the parametric instantaneous autocorrelation function, a novel distribution of the CPS, known as the CR-QCR distribution (CRQCRD), is presented and applied in a newly proposed ISAR imaging algorithm for targets with complex motion. The CRQCRD is simple and only requires the FFT and the nonuniform FFT (NUFFT). Owing to the application of the NUFFT, the computational cost is saved, and the searching procedure is unnecessary for the nonuniformly spaced signal. Compared to other four representative methods for CPSs, the CRQCRD, which can acquire higher antinoise performance and no error propagation, is searching-free and more suitable for the situation of multitargets. Several simulation examples, analyses of the antinoise performance, and ISAR images validate the effectiveness of the CRQCRD and the corresponding ISAR imaging algorithm.