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.

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.

To analyze the influence of time synchronization error, phase synchronization error, frequency synchronization error, internal delay of the transceiver system, and range error and angle error between the unit radars on the target detection performance, firstly, a spatial detection model of distributed high-frequency surface wave radar (distributed-HFSWR) is established in this paper. In this model, a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio (SNR), and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error. The direct wave component is extracted from the spectrum, the range estimation error and Doppler estimation error are reduced by the method of curve fitting, and the fitting accuracy of the parameters is improved. Then, the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed. The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given. Finally, the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting, the experimental data is compensated to correct the shift of the target, and finally the correct target parameter information is obtained. Simulations and experimental results demonstrate the superiority and correctness of the proposed method, theoretical derivation and detection model proposed in this paper.

To reduce the negative impact of the power amplifier (PA) nonlinear distortion caused by the orthogonal frequency division multiplexing (OFDM) waveform with high peak-to-average power ratio (PAPR) in integrated radar and communication (RadCom) systems is studied, the channel estimation in passive sensing scenarios. Adaptive channel estimation methods are proposed based on different pilot patterns, considering nonlinear distortion and channel sparsity. The proposed methods achieve sparse channel results by manipulating the least squares (LS) frequency-domain channel estimation results to preserve the most significant taps. The decision-aided method is used to optimize the sparse channel results to reduce the effect of nonlinear distortion. Numerical results show that the channel estimation performance of the proposed methods is better than that of the conventional methods under different pilot patterns. In addition, the bit error rate performance in communication and passive radar detection performance show that the proposed methods have good comprehensive performance.

A generalized labeled multi-Bernoulli (GLMB) filter with motion mode label based on the track-before-detect (TBD) strategy for maneuvering targets in sea clutter with heavy tail, in which the transitions of the mode of target motions are modeled by using jump Markovian system (JMS), is presented in this paper. The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBD model. In update, we derive a tractable GLMB density, which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence (KLD), to enable the next recursive cycle. The relevant simulation results prove that the proposed multiple-model GLMB-TBD (MM-GLMB-TBD) algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background. Additionally, the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD (DP-TBD) algorithm. Meanwhile, the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD (JMS-MeMBer-TBD) filter in estimation error with the basically same computational cost. Finally, the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.

A novel variable step-size modified super-exponential iteration (MSEI) decision feedback blind equalization (DFE) algorithm with second-order digital phase-locked loop is put forward to improve the convergence performance of super-exponential iteration DFE algorithm. Based on the MSEI-DFE algorithm, it is first proposed to develop an error function as an improvement to the error function of MSEI, which effectively achieves faster convergence speed of the algorithm. Subsequently, a hyperbolic tangent function variable step-size algorithm is developed considering the high variation rate of the hyperbolic tangent function around zero, so as to further improve the convergence speed of the algorithm. In the end, a second-order digital phase-locked loop is introduced into the decision feedback equalizer to track and compensate for the phase rotation of equalizer input signals. For the multipath underwater acoustic channel with mixed phase and phase rotation, quadrature phase shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM) modulated signals are used in the computer simulation of the algorithm in terms of convergence and carrier recovery performance. The results show that the proposed algorithm can considerably improve convergence speed and steady-state error, make effective compensation for phase rotation, and efficiently facilitate carrier recovery.

Unmanned aerial vehicles (UAVs) may be subjected to unintentional radio frequency interference (RFI) or hostile jamming attack which will lead to fail to track global navigation satellite system (GNSS) signals. Therefore, the simultaneous realization of anti-jamming and high-precision carrier phase difference positioning becomes a dilemmatic problem. In this paper, a distortionless phase digital beamforming (DBF) algorithm with self-calibration antenna arrays is proposed, which enables to obtain distortionless carrier phase while suppressing jamming. Additionally, architecture of high precision Beidou receiver based on anti-jamming antenna arrays is proposed. Finally, the performance of the algorithm is evaluated, including antenna calibration accuracy, carrier phase distortionless accuracy, and carrier phase measurement accuracy without jamming. Meanwhile, the maximal jamming to signal ratio (JSR) and real time kinematic (RTK) positioning accuracy under wideband jamming are also investigated. The experimental results based on the real-life Beidou signals show that the proposed method has an excellent performance for precise relative positioning under jamming when compared with other anti-jamming methods.

In modern war, radar countermeasure is becoming increasingly fierce, and the enemy jamming time and pattern are changing more randomly. It is challenging for the radar to efficiently identify jamming and obtain precise parameter information, particularly in low signal-to-noise ratio (SNR) situations. In this paper, an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue. Firstly, a joint algorithm based on YOLOv5 convolutional neural networks (CNNs) is proposed, which is used to achieve the jamming signal classification and preliminary parameter estimation. Furthermore, an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test, feature region search, position regression, spectrum interpolation, etc., which realizes the accurate estimation of jamming carrier frequency, relative delay, Doppler frequency shift, and other parameters. Finally, the approach has improved performance for complex jamming recognition and parameter estimation under low SNR, and the recognition rate can reach 98% under ?15 dB SNR, according to simulation and real data verification results.

There is a growing body of research on the swarm unmanned aerial vehicle (UAV) in recent years, which has the characteristics of small, low speed, and low height as radar target. To confront the swarm UAV, the design of anti-UAV radar system based on multiple input multiple output (MIMO) is put forward, which can elevate the performance of resolution, angle accuracy, high data rate, and tracking flexibility for swarm UAV detection. Target resolution and detection are the core problem in detecting the swarm UAV. The distinct advantage of MIMO system in angular accuracy measurement is demonstrated by comparing MIMO radar with phased array radar. Since MIMO radar has better performance in resolution, swarm UAV detection still has difficulty in target detection. This paper proposes a multi-mode data fusion algorithm based on deep neural networks to improve the detection effect. Subsequently, signal processing and data processing based on the detection fusion algorithm above are designed, forming a high resolution detection loop. Several simulations are designed to illustrate the feasibility of the designed system and the proposed algorithm.

With the rapid development of cloud manufacturing technology and the new generation of artificial intelligence technology, the new cloud manufacturing system (NCMS) built on the connotation of cloud manufacturing 3.0 presents a new business model of “Internet of everything, intelligent leading, data driving, shared services, cross-border integration, and universal innovation”. The network boundaries are becoming increasingly blurred, NCMS is facing security risks such as equipment unauthorized use, account theft, static and extensive access control policies, unauthorized access, supply chain attacks, sensitive data leaks, and industrial control vulnerability attacks. Traditional security architectures mainly use information security technology, which cannot meet the active security protection requirements of NCMS. In order to solve the above problems, this paper proposes an integrated cloud-edge-terminal security system architecture of NCMS. It adopts the zero trust concept and effectively integrates multiple security capabilities such as network, equipment, cloud computing environment, application, identity, and data. It adopts a new access control mode of “continuous verification + dynamic authorization”, classified access control mechanisms such as attribute-based access control, role-based access control, policy-based access control, and a new data security protection system based on blockchain, achieving “trustworthy subject identity, controllable access behavior, and effective protection of subject and object resources”. This architecture provides an active security protection method for NCMS in the digital transformation of large enterprises, and can effectively enhance network security protection capabilities and cope with increasingly severe network security situations.

To address the current problems of poor generality, low real-time, and imperfect information transmission of the battlefield target intelligence system, this paper studies the battlefield target intelligence system from the top-level perspective of multi-service joint warfare. First, an overall planning and analysis method of architecture modeling is proposed with the idea of a bionic analogy for battlefield target intelligence system architecture modeling, which reduces the difficulty of the planning and design process. The method introduces the Department of Defense architecture framework (DoDAF) modeling method, the multi-living agent (MLA) theory modeling method, and other combinations for planning and modeling. A set of rapid planning methods that can be applied to model the architecture of various types of complex systems is formed. Further, the liveness analysis of the battlefield target intelligence system is carried out, and the problems of the existing system are presented from several aspects. And the technical prediction of the development and construction is given, which provides directional ideas for the subsequent research and development of the battlefield target intelligence system. In the end, the proposed architecture model of the battlefield target intelligence system is simulated and verified by applying the colored Petri nets (CPN) simulation software. The analysis demonstrates the reasonable integrity of its logic.

Remote sensing data plays an important role in natural disaster management. However, with the increase of the variety and quantity of remote sensors, the problem of “knowledge barriers” arises when data users in disaster field retrieve remote sensing data. To improve this problem, this paper proposes an ontology and rule based retrieval (ORR) method to retrieve disaster remote sensing data, and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge, on this basis, and realizes the task suitability reasoning of earthquake disaster remote sensing data, mining the semantic relationship between remote sensing metadata and disasters. The prototype system is built according to the ORR method, which is compared with the traditional method, using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.

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.

A new approach is proposed in this study for accountable capability improvement based on interpretable capability evaluation using the belief rule base (BRB). Firstly, a capability evaluation model is constructed and optimized. Then, the key sub-capabilities are identified by quantitatively calculating the contributions made by each sub-capability to the overall capability. Finally, the overall capability is improved by optimizing the identified key sub-capabilities. The theoretical contributions of the proposed approach are as follows. (i) An interpretable capability evaluation model is constructed by employing BRB which can provide complete access to decision-makers. (ii) Key sub-capabilities are identified according to the quantitative contribution analysis results. (iii) Accountable capability improvement is carried out by only optimizing the identified key sub-capabilities. Case study results show that “Surveillance”, “Positioning”, and “Identification” are identified as key sub-capabilities with a summed contribution of 75.55% in an analytical and deducible fashion based on the interpretable capability evaluation model. As a result, the overall capability is improved by optimizing only the identified key sub-capabilities. The overall capability can be greatly improved from 59.20% to 81.80% with a minimum cost of 397. Furthermore, this paper also investigates how optimizing the BRB with more collected data would affect the evaluation results: only optimizing “Surveillance” and “Positioning” can also improve the overall capability to 81.34% with a cost of 370, which thus validates the efficiency of the proposed approach.

In the aircraft control system, sensor networks are used to sample the attitude and environmental data. As a result of the external and internal factors (e.g., environmental and task complexity, inaccurate sensing and complex structure), the aircraft control system contains several uncertainties, such as imprecision, incompleteness, redundancy and randomness. The information fusion technology is usually used to solve the uncertainty issue, thus improving the sampled data reliability, which can further effectively increase the performance of the fault diagnosis decision-making in the aircraft control system. In this work, we first analyze the uncertainties in the aircraft control system, and also compare different uncertainty quantitative methods. Since the information fusion can eliminate the effects of the uncertainties, it is widely used in the fault diagnosis. Thus, this paper summarizes the recent work in this aera. Furthermore, we analyze the application of information fusion methods in the fault diagnosis of the aircraft control system. Finally, this work identifies existing problems in the use of information fusion for diagnosis and outlines future trends.

A framework that integrates planning, monitoring and replanning techniques is proposed. It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan execution. The framework consists of three parts: the hierarchical task network (HTN) planner based on Monte Carlo tree search (MCTS), hybrid plan monitoring based on forward and backward and norm-based replanning method selection. The HTN planner based on MCTS selects the optimal method for HTN compound task through pre-exploration. Based on specific objectives, it can identify the best solution to the current problem. The hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action, thus trigger the replanning. The norm-based replanning selection method can measure the difference between the expected state and the actual state, and then select the best replanning algorithm. The experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way.

In this paper, a method for spoofing detection based on the variation of the signal’s carrier-to-noise ratio (CNR) is proposed. This method leverages the directionality of the antenna to induce varying gain changes in the signals across different incident directions, resulting in distinct CNR variations for each signal. A model is developed to calculate the variation value of the signal CNR based on the antenna gain pattern. This model enables the differentiation of the variation values of the CNR for authentic satellite signals and spoofing signals, thereby facilitating spoofing detection. The proposed method is capable of detecting spoofing signals with power and CNR similar to those of authentic satellite signals. The accuracy of the signal CNR variation value calculation model and the effectiveness of the spoofing detection method are verified through a series of experiments. In addition, the proposed spoofing detection method works not only for a single spoofing source but also for distributed spoofing sources.

The quantum entangled photon-pair source, as an essential component of optical quantum systems, holds great potential for applications such as quantum teleportation, quantum computing, and quantum imaging. The current workhorse technique for preparing photon pairs involves performing spontaneous parametric down conversion (SPDC) in bulk nonlinear crystals. However, the current power consumption and cost of preparing entangled photon-pair sources are relatively high, posing challenges to their integration and scalability. In this paper, we propose a low-power system model for the quantum entangled photon-pair source based on SPDC theory and phase matching technology. This model allows us to analyze the performance of each module and the influence of component characteristics on the overall system. In our experimental setup, we utilize a 5 mW laser diode and a typical type-II barium metaborate (BBO) crystal to prepare an entangled photon-pair source. The experimental results are in excellent agreement with the model, indicating a significant step towards achieving the goal of low-power and low-cost entangled photon-pair sources. This achievement not only contributes to the practical application of quantum entanglement lighting, but also paves the way for the widespread adoption of optical quantum systems in the future.

Final velocity and impact angle are critical to missile guidance. Computationally efficient guidance law with comprehensive consideration of the two performance merits is challenging yet remains less addressed. Therefore, this paper seeks to solve a type of optimal control problem that maximizes final velocity subject to equality point constraint of impact angle constraint. It is proved that the crude problem of maximizing final velocity is equivalent to minimizing a quadratic-form cost of curvature. The closed-form guidance law is henceforth derived using optimal control theory. The derived analytical guidance law coincides with the widely-used optimal guidance law with impact angle constraint (OGL-IAC) with a set of navigation parameters of two and six. On this basis, the optimal emission angle is determined to further increase the final velocity. The derived optimal value depends solely on the initial line-of-sight angle and impact angle constraint, and thus practical for real-world applications. The proposed guidance law is validated by numerical simulation. The results show that the OGL-IAC is superior to the benchmark guidance laws both in terms of final velocity and missing distance.

To solve the problem that multiple missiles should simultaneously attack unmeasurable maneuvering targets, a guidance law with temporal consistency constraint based on the super-twisting observer is proposed. Firstly, the relative motion equations between multiple missiles and targets are established, and the topological model among multiple agents is considered. Secondly, based on the temporal consistency constraint, a cooperative guidance law for simultaneous arrival with finite-time convergence is derived. Finally, the unknown target maneuvering is regarded as bounded interference. Based on the second-order sliding mode theory, a super-twisting sliding mode observer is devised to observe and track the bounded interference, and the stability of the observer is proved. Compared with the existing research, this approach only needs to obtain the sliding mode variable which simplifies the design process. The simulation results show that the designed cooperative guidance law for maneuvering targets achieves the expected effect. It ensures successful cooperative attacks, even when confronted with strong maneuvering targets.

To solve the finite-time error-tracking problem in missile guidance, this paper presents a unified design approach through error dynamics and free-time convergence theory. The proposed approach is initiated by establishing a desired model for free-time convergent error dynamics, characterized by its independence from initial conditions and guidance parameters, and adjustable convergence time. This foundation facilitates the derivation of specific guidance laws that integrate constraints such as leading angle, impact angle, and impact time. The theoretical framework of this study elucidates the nuances and synergies between the proposed guidance laws and existing methodologies. Empirical evaluations through simulation comparisons underscore the enhanced accuracy and adaptability of the proposed laws.

As a dynamic projection to latent structures (PLS) method with a good output prediction ability, dynamic inner PLS (DiPLS) is widely used in the prediction of key performance indicators. However, due to the oblique decomposition of the input space by DiPLS, there are false alarms in the actual industrial process during fault detection. To address the above problems, a dynamic modeling method based on autoregressive-dynamic inner total PLS (AR-DiTPLS) is proposed. The method first uses the regression relation matrix to decompose the input space orthogonally, which reduces useless information for the prediction output in the quality-related dynamic subspace. Then, a vector autoregressive model (VAR) is constructed for the prediction score to separate dynamic information and static information. Based on the VAR model, appropriate statistical indicators are further constructed for online monitoring, which reduces the occurrence of false alarms. The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.