This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle (UAV) swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs ’ actuator and sensor. The fixed-wing UAV swarm under consideration is organized as a “multi-leader-multi-follower” structure, in which only several leaders can obtain the dynamic target information while others only receive the neighbors’ information through the communication network. To simultaneously realize the formation, containment, and dynamic target tracking, a two-layer control framework is adopted to decouple the problem into two subproblems: reference trajectory generation and trajectory tracking. In the upper layer, a distributed finite-time estimator (DFTE) is proposed to generate each UAV ’s reference trajectory in accordance with the control objective. Subsequently, a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer, where a novel adaptive extended super-twisting (AESTW) algorithm with a finite-time extended state observer (FTESO) is involved in solving the robust trajectory tracking control problem under model uncertainties, actuator, and sensor faults. The proposed controller simultaneously guarantees rapidness and enhances the system ’s robustness with fewer chattering effects. Finally, corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.

With the rapid development of informatization, autonomy and intelligence, unmanned swarm formation intelligent operations will become the main combat mode of future wars. Typical unmanned swarm formations such as ground-based directed energy weapon formations, space-based kinetic energy weapon formations, and sea-based carrier-based formations have become the trump card for winning future wars. In a complex confrontation environment, these sophisticated weapon formation systems can precisely strike mobile threat group targets, making them extreme deterrents in joint combat applications. Based on this, first, this paper provides a comprehensive summary of the outstanding advantages, strategic position and combat style of unmanned clusters in joint warfare to highlight their important position in future warfare. Second, a detailed analysis of the technological breakthroughs in four key areas, situational awareness, heterogeneous coordination, mixed combat, and intelligent assessment of typical unmanned aerial vehicle (UAV) swarms in joint warfare, is presented. An in-depth analysis of the UAV swarm communication networking operating mechanism during joint warfare is provided to lay the theoretical foundation for subsequent cooperative tracking and control. Then, an in-depth analysis of the shut-in technology requirements of UAV clusters in joint warfare is provided to lay a theoretical foundation for subsequent cooperative tracking control. Finally, the technical requirements of UAV clusters in joint warfare are analysed in depth so the key technologies can form a closed-loop kill chain system and provide theoretical references for the study of intelligent command operations.

By deploying the ubiquitous and reliable coverage of low Earth orbit (LEO) satellite networks using optical inter satellite link (OISL), computation offloading services can be provided for any users without proximal servers, while the resource limitation of both computation and storage on satellites is the important factor affecting the maximum task completion time. In this paper, we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs, such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood. To satisfy the delay requirement of delay-sensitive task, we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline, and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites. Simulation results demonstrate the effectiveness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.

In this paper, a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented, focusing on the challenges posed by sea clutter and multipath scattering. The performance of the radar detection methods under sea clutter, multipath, and combined conditions is categorized and summarized, and future research directions are outlined to enhance radar detection performance for low–altitude targets in maritime environments.

In the process of performing a task, autonomous unmanned systems face the problem of scene changing, which requires the ability of real-time decision-making under dynamically changing scenes. Therefore, taking the unmanned system coordinative region control operation as an example, this paper combines knowledge representation with probabilistic decision-making and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences. Firstly, according to utility value decision theory, the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned. Then, multi-entity Bayesian network is introduced for situation assessment, by which scenes and their uncertainty related to the operation are semantically described, so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty. Finally, the effectiveness of the proposed method is verified in a virtual task scenario. This research has important reference value for realizing scene cognition, improving cooperative decision-making ability under dynamic scenes, and achieving swarm level autonomy of unmanned systems.

Air-to-air combat tactical decisions for multiple unmanned aerial vehicles (ACTDMU) are a key decision-making step in beyond visual range combat. Complex influencing factors, strong antagonism and real-time requirements need to be considered in the ACTDMU problem. In this paper, we propose a multicriteria game approach to ACTDMU. This approach consists of a multicriteria game model and a Pareto Nash equilibrium algorithm. In this model, we form the strategy profiles for the integration of air-to-air combat tactics and weapon target assignment strategies by considering the correlation between them, and we design the vector payoff functions based on predominance factors. We propose a algorithm of Pareto Nash equilibrium based on preference relations using threshold constraints (PNE-PRTC), and we prove that the solutions obtained by this algorithm are refinements of Pareto Nash equilibrium solutions. The numerical experiments indicate that PNE-PRTC algorithm is considerably faster than the baseline algorithms and the performance is better. Especially on large-scale instances, the Pareto Nash equilibrium solutions can be calculated by PNE-PRTC algorithm at the second level. The simulation experiments show that the multicriteria game approach is more effective than one-side decision approaches such as multiple-attribute decision-making and randomly chosen decisions.

To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle (UAV) real-time path planning problem, a real-time UAV path planning algorithm based on long short-term memory (RPP-LSTM) network is proposed, which combines the memory characteristics of recurrent neural network (RNN) and the deep reinforcement learning algorithm. LSTM networks are used in this algorithm as Q-value networks for the deep Q network (DQN) algorithm, which makes the decision of the Q-value network has some memory. Thanks to LSTM network, the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment. Besides, the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning, so that the UAV can more reasonably perform path planning. Simulation verification shows that compared with the traditional feed-forward neural network (FNN) based UAV autonomous path planning algorithm, the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.

Deep learning has achieved excellent results in various tasks in the field of computer vision, especially in fine-grained visual categorization. It aims to distinguish the subordinate categories of the label-level categories. Due to high intra-class variances and high inter-class similarity, the fine-grained visual categorization is extremely challenging. This paper first briefly introduces and analyzes the related public datasets. After that, some of the latest methods are reviewed. Based on the feature types, the feature processing methods, and the overall structure used in the model, we divide them into three types of methods: methods based on general convolutional neural network (CNN) and strong supervision of parts, methods based on single feature processing, and methods based on multiple feature processing. Most methods of the first type have a relatively simple structure, which is the result of the initial research. The methods of the other two types include models that have special structures and training processes, which are helpful to obtain discriminative features. We conduct a specific analysis on several methods with high accuracy on public datasets. In addition, we support that the focus of the future research is to solve the demand of existing methods for the large amount of the data and the computing power. In terms of technology, the extraction of the subtle feature information with the burgeoning vision transformer (ViT) network is also an important research direction.

A generalized multiple-mode prolate spherical wave functions (PSWFs) multi-carrier with index modulation approach is proposed with the purpose of improving the spectral efficiency of PSWFs multi-carrier systems. The proposed method, based on the optimized multi-index modulation, does not limit the number of signals in the first and second constellations and abandons the concept of limiting the number of signals in different constellations. It successfully increases the spectrum efficiency of the system while expanding the number of modulation symbol combinations and the index dimension of PSWFs signals. The proposed method outperforms the PSWFs multi-carrier index modulation method based on optimized multiple indexes in terms of spectrum efficiency, but at the expense of system computational complexity and bit error performance. For example, with $n $=10 subcarriers and a bit error rate of 1×10^?5, spectral efficiency can be raised by roughly 12.4%.

Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver. To reduce the computational complexity and latency of code acquisition, this paper proposes an efficient scheme employing sparse Fourier transform (SFT) and the relevant hardware architecture for field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementation. Efforts are made at both the algorithmic level and the implementation level to enable merged searching of code phase and Doppler frequency without incurring massive hardware expenditure. Compared with the existing code acquisition approaches, it is shown from theoretical analysis and experimental results that the proposed design can shorten processing latency and reduce hardware complexity without degrading the acquisition probability.

Thinning of antenna arrays has been a popular topic for the last several decades. With increasing computational power, this optimization task acquired a new hue. This paper suggests a genetic algorithm as an instrument for antenna array thinning. The algorithm with a deliberately chosen fitness function allows synthesizing thinned linear antenna arrays with low peak sidelobe level (SLL) while maintaining the half-power beamwidth (HPBW) of a full linear antenna array. Based on results from existing papers in the field and known approaches to antenna array thinning, a classification of thinning types is introduced. The optimal thinning type for a linear thinned antenna array is determined on the basis of a maximum attainable SLL. The effect of thinning coefficient on main directional pattern characteristics, such as peak SLL and HPBW, is discussed for a number of amplitude distributions.

The perception module of advanced driver assistance systems plays a vital role. Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer. This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme. A binocular stereo vision sensor composed of two cameras and a light deterction and ranging (LiDAR) sensor is used to jointly perceive the environment, and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map. This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors. Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.

Deep neural networks (DNNs) have achieved great success in many data processing applications. However, high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices, and it is not environmental-friendly with much power cost. In this paper, we focus on low-rank optimization for efficient deep learning techniques. In the space domain, DNNs are compressed by low rank approximation of the network parameters, which directly reduces the storage requirement with a smaller number of network parameters. In the time domain, the network parameters can be trained in a few subspaces, which enables efficient training for fast convergence. The model compression in the spatial domain is summarized into three categories as pre-train, pre-set, and compression-aware methods, respectively. With a series of integrable techniques discussed, such as sparse pruning, quantization, and entropy coding, we can ensemble them in an integration framework with lower computational complexity and storage. In addition to summary of recent technical advances, we have two findings for motivating future works. One is that the effective rank, derived from the Shannon entropy of the normalized singular values, outperforms other conventional sparse measures such as the $ \ell_1 $ norm for network compression. The other is a spatial and temporal balance for tensorized neural networks. For accelerating the training of tensorized neural networks, it is crucial to leverage redundancy for both model compression and subspace training.

This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering, i.e., unmanned aerial vehicle (UAV) formation flight system. Firstly, from the theoretical point of view, consider one nonlinear closed-loop system with a nonlinear plant and nonlinear feed-forward controller simultaneously. To avoid the complex identification process for that nonlinear plant, a nonlinear direct data-driven control strategy is proposed to design that nonlinear feed-forward controller only through the input-output measured data sequence directly, whose detailed explicit forms are model inverse method and approximated analysis method. Secondly, from the practical point of view, after reviewing the UAV formation flight system, nonlinear direct data-driven control is applied in designing the formation controller, so that the followers can track the leader’s desired trajectory during one small time instant only through solving one data fitting problem. Since most natural phenomena have nonlinear properties, the direct method must be the better one. Corresponding system identification and control algorithms are required to be proposed for those nonlinear systems, and the direct nonlinear controller design is the purpose of this paper.

The acquisition, analysis, and prediction of the radar cross section (RCS) of a target have extremely important strategic significance in the military. However, the RCS values at all azimuths are hardly accessible for non-cooperative targets, due to the limitations of radar observation azimuth and detection resources. Despite their efforts to predict the azimuth-dimensional RCS value, traditional methods based on statistical theory fails to achieve the desired results because of the azimuth sensitivity of the target RCS. To address this problem, an improved neural basis expansion analysis for interpretable time series forecasting (N-BEATS) network considering the physical model prior is proposed to predict the azimuth-dimensional RCS value accurately. Concretely, physical model-based constraints are imposed on the network by constructing a scattering-center module based on the target scattering-center model. Besides, a superimposed seasonality module is involved to better capture high-frequency information, and augmenting the training set provides complementary information for learning predictions. Extensive simulations and experimental results are provided to validate the effectiveness of the proposed method.

This paper concerns minimum-energy leader-following formation design and analysis problems of distributed multi-agent systems (DMASs) subjected to randomly switching topologies and aperiodic communication pauses. The critical feature of this paper is that the energy consumption during the formation control process is restricted by the minimum-energy constraint in the sense of the linear matrix inequality. Firstly, the leader-following formation control protocol is proposed based on the relative state information of neighboring agents, where the total energy consumption is considered. Then, minimum-energy leader-following formation design and analysis criteria are presented in the form of the linear matrix inequality, which can be checked by the generalized eigenvalue method. Especially, the value of the minimum-energy constraint is determined. An illustrative simulation is provided to show the effectiveness of the main results.

Beam-hopping technology has become one of the major research hotspots for satellite communication in order to enhance their communication capacity and flexibility. However, beam hopping causes the traditional continuous time-division multiplexing signal in the forward downlink to become a burst signal, satellite terminal receivers need to solve multiple key issues such as burst signal rapid synchronization and high-performance reception. Firstly, this paper analyzes the key issues of burst communication for traffic signals in beam hopping systems, and then compares and studies typical carrier synchronization algorithms for burst signals. Secondly, combining the requirements of beam-hopping communication systems for efficient burst and low signal-to-noise ratio reception of downlink signals in forward links, a decoding assisted bidirectional variable parameter iterative carrier synchronization technique is proposed, which introduces the idea of iterative processing into carrier synchronization. Aiming at the technical characteristics of communication signal carrier synchronization, a new technical approach of bidirectional variable parameter iteration is adopted, breaking through the traditional understanding that loop structures cannot adapt to low signal-to-noise ratio burst demodulation. Finally, combining the DVB-S2X standard physical layer frame format used in high throughput satellite communication systems, the research and performance simulation are conducted. The results show that the new technology proposed in this paper can significantly shorten the carrier synchronization time of burst signals, achieve fast synchronization of low signal-to-noise ratio burst signals, and have the unique advantage of flexible and adjustable parameters.

Most of the existing direction of arrival (DOA) estimation algorithms are applied under the assumption that the array manifold is ideal. In practical engineering applications, the existence of non-ideal conditions such as mutual coupling between array elements, array amplitude and phase errors, and array element position errors leads to defects in the array manifold, which makes the performance of the algorithm decline rapidly or even fail. In order to solve the problem of DOA estimation in the presence of amplitude and phase errors and array element position errors, this paper introduces the first-order Taylor expansion equivalent model of the received signal under the uniform linear array from the Bayesian point of view. In the solution, the amplitude and phase error parameters and the array element position error parameters are regarded as random variables obeying the Gaussian distribution. At the same time, the expectation-maximization algorithm is used to update the probability distribution parameters, and then the two error parameters are solved alternately to obtain more accurate DOA estimation results. Finally, the effectiveness of the proposed algorithm is verified by simulation and experiment.

In order to obtain better inverse synthetic aperture radar (ISAR) image, a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband. The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices. To analyse the superiority of the modified algorithm, the mathematical expression of equivalent signal to noise ratio (SNR) is derived, which can validate our proposed algorithm theoretically. In addition, compared with the conventional matrix pencil (MP) algorithm and the conventional root-multiple signal classification (Root-MUSIC) algorithm, the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations. Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.

Discrete event system (DES) models promote system engineering, including system design, verification, and assessment. The advancement in manufacturing technology has endowed us to fabricate complex industrial systems. Consequently, the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative. Moreover, industrial systems are no longer quiescent, thus the intelligent operations of the systems should be dynamically specified in the model. In this paper, the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model, and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the model. In traditional modeling approaches, the change or addition of specifications always necessitates the complete resubmission of the system model, a resource-consuming and error-prone process. Compared with traditional approaches, our approach has three remarkable advantages: (i) an established Boolean semantic can be fitful for all kinds of systems; (ii) there is no need to resubmit the system model whenever there is a change or addition of the operations; (iii) multiple specifying tasks can be easily achieved by continuously adding a new semantic. Thus, this general modeling approach has wide potential for future complex and intelligent industrial systems.

The electric-controlled metasurface antenna array (ECMSAA) with ultra-wideband frequency reconfigurable reflection suppression is proposed and realized. Firstly, an electric- controlled metasurface with ultra-wideband frequency reconfigurable in-phase reflection characteristics is designed. The element of the ECMSAA is constructed by loading the single electric-controlled metasurface unit on the conventional patch antenna element. The radiation properties of the conventional patch antenna and the reflection performance of electric-controlled metasurface are maintained when the antenna and the metasurface are integrated. Thus, the ECMSAA elements have excellent radiation properties and ultra-wideband frequency reconfigurable in-phase reflection characteristics simultaneously. To take a further step, a 6×10 ECMSAA is realized based on the designed metasurface antenna element. Simulated and measured results prove that the reflection of the ECMSAA is dynamically suppressed in the P and L bands. Meanwhile, high-gain and multi-polarization radiation properties of the ECMSAA are achieved. This design method not only realizes the frequency reconfigurable reflection suppression of the antenna array in the ultra-wide frequency band but also provides a way to develop an intelligent low-scattering antenna.

A low-Earth-orbit (LEO) satellite network can provide full-coverage access services worldwide and is an essential candidate for future 6G networking. However, the large variability of the geographic distribution of the Earth’s population leads to an uneven service volume distribution of access service. Moreover, the limitations on the resources of satellites are far from being able to serve the traffic in hotspot areas. To enhance the forwarding capability of satellite networks, we first assess how hotspot areas under different load cases and spatial scales significantly affect the network throughput of an LEO satellite network overall. Then, we propose a multi-region cooperative traffic scheduling algorithm. The algorithm migrates low-grade traffic from hotspot areas to coldspot areas for forwarding, significantly increasing the overall throughput of the satellite network while sacrificing some latency of end-to-end forwarding. This algorithm can utilize all the global satellite resources and improve the utilization of network resources. We model the cooperative multi-region scheduling of large-scale LEO satellites. Based on the model, we build a system testbed using OMNET++ to compare the proposed method with existing techniques. The simulations show that our proposed method can reduce the packet loss probability by 30% and improve the resource utilization ratio by 3.69%.

In the field of deep space exploration, the rapid development of terahertz spectrometer has put forward higher requirements to the back-end chirp transform spectrometer (CTS) system. In order to simultaneously meet the measurement requirements of wide bandwidth and high accuracy spectral lines, we built a CTS system with an analysis bandwidth of 1 GHz and a frequency resolution of 100 kHz around the surface acoustic wave (SAW) chirp filter with a bandwidth of 1 GHz. In this paper, the relationship between the CTS nonlinear phase error shift model and the basic measurement parameters is studied, and the effect of CTS phase mismatch on the pulse compression waveform is analyzed by simulation. And the expander error optimization method is proposed for the problem that the large nonlinear error of the expander leads to the unbalanced response of the CTS system and the serious distortion of the compressed pulse waveform under large bandwidth. It is verified through simulation and experiment that the method is effective for reducing the root mean square error (RMSE) of the phase of the expander from 18.75° to 6.65°, reducing the in-band standard deviation of the CTS frequency resolution index from 8.43 kHz to 4.72 kHz, solving the problem of serious distortion of the compressed pulse waveform, and improving the uneven CTS response under large bandwidth.

Time synchronization is one of the base techniques in wireless sensor networks (WSNs). This paper proposes a novel time synchronization protocol which is a robust consensus-based algorithm in the existence of transmission delay and packet loss. It compensates for transmission delay and packet loss firstly, and then, estimates clock skew and clock offset in two steps. Simulation and experiment results show that the proposed protocol can keep synchronization error below 2 μs in the grid network of 10 nodes or the random network of 90 nodes. Moreover, the synchronization accuracy in the proposed protocol can keep constant when the WSN works up to a month.

A dynamic multi-beam resource allocation algorithm for large low Earth orbit (LEO) constellation based on on-board distributed computing is proposed in this paper. The allocation is a combinatorial optimization process under a series of complex constraints, which is important for enhancing the matching between resources and requirements. A complex algorithm is not available because that the LEO on-board resources is limited. The proposed genetic algorithm (GA) based on two-dimensional individual model and uncorrelated single paternal inheritance method is designed to support distributed computation to enhance the feasibility of on-board application. A distributed system composed of eight embedded devices is built to verify the algorithm. A typical scenario is built in the system to evaluate the resource allocation process, algorithm mathematical model, trigger strategy, and distributed computation architecture. According to the simulation and measurement results, the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91% in a typical scene. The response time is decreased by 40% compared with the conditional GA.

Based on the characteristics of high-end products, crowd-sourcing user stories can be seen as an effective means of gathering requirements, involving a large user base and generating a substantial amount of unstructured feedback. The key challenge lies in transforming abstract user needs into specific ones, requiring integration and analysis. Therefore, we propose a topic mining-based approach to categorize, summarize, and rank product requirements from user stories. Specifically, after determining the number of story categories based on pyLDAvis, we initially classify “I want to” phrases within user stories. Subsequently, classic topic models are applied to each category to generate their names, defining each post-classification user story category as a requirement. Furthermore, a weighted ranking function is devised to calculate the importance of each requirement. Finally, we validate the effectiveness and feasibility of the proposed method using 2 966 crowd-sourced user stories related to smart home systems.

Aiming at evaluating and predicting rapidly and accurately a high sensitivity receiver’s adaptability in complex electromagnetic environments, a novel testing and prediction method based on dual-channel multi-frequency is proposed to improve the traditional two-tone test. Firstly, two signal generators are used to generate signals at the radio frequency (RF) by frequency scanning, and then a rapid measurement at the intermediate frequency (IF) output port is carried out to obtain a huge amount of sample data for the subsequent analysis. Secondly, the IF output response data are modeled and analyzed to construct the linear and nonlinear response constraint equations in the frequency domain and prediction models in the power domain, which provide the theoretical criteria for interpreting and predicting electromagnetic susceptibility (EMS) of the receiver. An experiment performed on a radar receiver confirms the reliability of the method proposed in this paper. It shows that the interference of each harmonic frequency and each order to the receiver can be identified and predicted with the sensitivity model. Based on this, fast and comprehensive evaluation and prediction of the receiver’s EMS in complex environment can be efficiently realized.

Imaging detection is an important means to obtain target information. The traditional imaging detection technology mainly collects the intensity information and spectral information of the target to realize the classification of the target. In practical applications, due to the mixed scenario, it is difficult to meet the needs of target recognition. Compared with intensity detection, the method of polarization detection can effectively enhance the accuracy of ground object target recognition (such as the camouflage target). In this paper, the reflection mechanism of the target surface is studied from the microscopic point of view, and the polarization characteristic model is established to express the relationship between the polarization state of the reflected signal and the target surface parameters. The polarization characteristic test experiment is carried out, and the target surface parameters are retrieved using the experimental data. The results show that the degree of polarization (DOP) is closely related to the detection zenith angle and azimuth angle. The (DOP) of the target is the smallest in the direction of light source incidence and the largest in the direction of specular reflection. Different materials have different polarization characteristics. By comparing their DOP, target classification can be achieved.

With the extensive application of large-scale array antennas, the increasing number of array elements leads to the increasing dimension of received signals, making it difficult to meet the real-time requirement of direction of arrival (DOA) estimation due to the computational complexity of algorithms. Traditional subspace algorithms require estimation of the covariance matrix, which has high computational complexity and is prone to producing spurious peaks. In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements, this paper proposes a DOA estimation method based on Krylov subspace and weighted $ {l}_{1} $-norm. The method uses the multistage Wiener filter (MSWF) iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace, further uses the measurement matrix to reduce the dimensionality of the signal subspace observation, constructs a weighted matrix, and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted $ {l}_{1} $-norm to solve the target DOA. Simulation results show that the proposed method has high resolution under large array conditions, effectively suppresses spurious peaks, reduces computational complexity, and has good robustness for low signal to noise ratio (SNR) environment.

Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and further reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and amplitude of the signal kurtosis (FA-SK) is further proposed. Simulation and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.

In this paper, we propose an effective full array and sparse array adaptive beamforming scheme that can be applied for multiple desired signals based on the branch-and-bound algorithm. Adaptive beamforming for the multiple desired signals is realized by the improved Capon method. At the same time, the sidelobe constraint is added to reduce the sidelobe level. To reduce the pointing errors of multiple desired signals, the array response phase of the desired signal is firstly optimized by using auxilary variables while keeping the response amplitude unchanged. The whole design is formulated as a convex optimization problem solved by the branch-and-bound algorithm. In addition, the beamformer weight vector is penalized with the modified reweighted ${l_1}$-norm to achieve sparsity. Theoretical analysis and simulation results show that the proposed algorithm has lower sidelobe level, higher SINR, and less pointing error than the state-of-the-art methods in the case of a single expected signal and multiple desired signals.

The detection of hypersonic targets usually confronts range migration (RM) issue before coherent integration (CI). The traditional methods aiming at correcting RM to obtain CI mainly considers the narrow-band radar condition. However, with the increasing requirement of far-range detection, the time bandwidth product, which is corresponding to radar’s mean power, should be promoted in actual application. Thus, the echo signal generates the scale effect (SE) at large time bandwidth product situation, influencing the intra and inter pulse integration performance. To eliminate SE and correct RM, this paper proposes an effective algorithm, i.e., scaled location rotation transform (ScLRT). The ScLRT can remove SE to obtain the matching pulse compression (PC) as well as correct RM to complete CI via the location rotation transform, being implemented by seeking the actual rotation angle. Compared to the traditional coherent detection algorithms, ScLRT can address the SE problem to achieve better detection/estimation capabilities. At last, this paper gives several simulations to assess the viability of ScLRT.

In this paper, the reactive splitter network and metasurface are proposed to radiate the wide-beam isolated element pattern and suppress mutual coupling (MC) of the low-profile phased array with the triangular lattice, respectively. Thus, broadband wide-angle impedance matching (WAIM) is implemented to promote two-dimensional (2D) wide scanning. For the isolated element, to radiate the wide-beam patterns approximating to the cosine form, two identical slots backed on one substrate integrated cavity are excited by the feeding network consisting of a reactive splitter and two striplines connected with splitter output paths. For adjacent elements staggered with each other, with the metasurface superstrate, the even-mode coupling voltages on the reactive splitter are cancelled out, yielding reduced MC. With the suppression of MC and the compensation of isolated element patterns, WAIM is realized to achieve 2D wide-angle beam steering up to ± 65° in E-plane, ± 45° in H-plane and ± 60° in D-plane from 4.9 GHz to 5.85 GHz.

The warhead of a ballistic missile may precess due to lateral moments during release. The resulting micro-Doppler effect is determined by parameters such as the target’s motion state and size. A three-dimensional reconstruction method for the precession warhead via the micro-Doppler analysis and inverse Radon transform (IRT) is proposed in this paper. The precession parameters are extracted by the micro-Doppler analysis from three radars, and the IRT is used to estimate the size of targe. The scatterers of the target can be reconstructed based on the above parameters. Simulation experimental results illustrate the effectiveness of the proposed method in this paper.