Evolutionary algorithms (EAs) have been used in high utility itemset mining (HUIM) to address the problem of discovering high utility itemsets (HUIs) in the exponential search space. EAs have good running and mining performance, but they still require huge computational resource and may miss many HUIs. Due to the good combination of EA and graphics processing unit (GPU), we propose a parallel genetic algorithm (GA) based on the platform of GPU for mining HUIM (PHUI-GA). The evolution steps with improvements are performed in central processing unit (CPU) and the CPU intensive steps are sent to GPU to evaluate with multi-threaded processors. Experiments show that the mining performance of PHUI-GA outperforms the existing EAs. When mining 90% HUIs, the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.
In this paper, an efficient unequal error protection (UEP) scheme for online fountain codes is proposed. In the build-up phase, the traversing-selection strategy is proposed to select the most important symbols (MIS). Then, in the completion phase, the weighted-selection strategy is applied to provide low overhead. The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme. Simulation results show that in terms of MIS and the least important symbols (LIS), when the bit error ratio is $ {10^{ - 4}} $, the proposed scheme can achieve $ 85{\text{% }} $ and $ 31.58{\text{% }} $ overhead reduction, respectively.
To maintain the stability of the inter-satellite link for gravitational wave detection, an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed. Different from the traditional fault diagnosis optimization algorithms, the fault intelligent learning method proposed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong coupling nonlinearity. By constructing a two-layer learning network, the method enables efficient joint diagnosis of fault areas and fault parameters. The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s, and the fault diagnosis efficiency is improved by 99.8% compared with the traditional algorithm.
With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess, the cloud control system (CCS) has exhibited formidable potential in the realm of connected assisted driving, such as the adaptive cruise control (ACC). Based on the CCS architecture, this paper proposes a cloud-based predictive ACC (PACC) strategy, which fully considers the road slope information and the preceding vehicle status. In the cloud, based on the dynamic programming (DP), the long-term economic speed planning is carried out by using the slope information. At the vehicle side, the real-time fusion planning of the economic speed and the preceding vehicle state is realized based on the model predictive control (MPC), taking into account the safety and economy of driving. In order to ensure the safety and stability of the vehicle-cloud cooperative control system, an event-triggered cruise mode switching method is proposed based on the state of each subsystem of the vehicle-cloud-network-map. Simulation results indicate that the PACC system can still ensure stable cruising under delays and some complex conditions. Moreover, under normal conditions, compared to the ACC system, the PACC system can further improve economy while ensuring safety and improve the overall energy efficiency of the vehicle, thus achieving fuel savings of 3% to 8%.
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.
Autonomous umanned aerial vehicle (UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decision-making policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods. Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes (MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.
In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF) master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.
Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion. Nevertheless, when fusing highly conflicting evidence it may produce counterintuitive outcomes. To address this issue, a fusion approach based on a newly defined belief exponential divergence and Deng entropy is proposed. First, a belief exponential divergence is proposed as the conflict measurement between evidences. Then, the credibility of each evidence is calculated. Afterwards, the Deng entropy is used to calculate information volume to determine the uncertainty of evidence. Then, the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence. Ultimately, initial evidences are amended and fused using Dempster’s rule of combination. The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic examples. Additionally, the proposed approach is applied to aerial target recognition and iris dataset-based classification to validate its efficacy. Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.
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.
For bistatic multiple-input multiple-output (MIMO) radar, this paper presents a robust and direction finding method in strong impulse noise environment. By means of a new lower order covariance, the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood (ML) estimation method. A quantum equilibrium optimizer algorithm (QEOA) is devised to resolve the corresponding objective function for efficient and accurate direction finding. The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations, e.g., locating coherent signal sources with very few snapshots in strong impulse noise. Other than that, the Cramér-Rao bound (CRB) under impulse noise environment has been drawn to test the capability of the presented method.
To investigate the real-time mean orbital elements (MOEs) estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit transfer, a modified augmented square-root unscented Kalman filter (MASUKF) is proposed. The MASUKF is composed of sigma points calculation, time update, modified state jumping detection, and measurement update. Compared with the filters used in the existing literature on MOEs estimation, it has three main characteristics. Firstly, the state vector is augmented from six to nine by the added thrust acceleration terms, which makes the filter additionally give the state-jumping-thrust-acceleration estimation. Secondly, the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency. Thirdly, when sate jumping is detected, the covariance matrix inflation will be done, and then an extra time update process will be conducted at this time instance before measurement update. In this way, the relatively large estimation error at the detection moment can significantly decrease. Finally, typical simulations are performed to illustrated the effectiveness of the method.
Natural events have had a significant impact on overall flight activity, and the aviation industry plays a vital role in helping society cope with the impact of these events. As one of the most impactful weather typhoon seasons appears and continues, airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms. This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation. The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules, and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction. With more dependable data accuracy, problems can be analysed rapidly and more efficiently, enabling better decision-making with a proactive versus reactive posture. When multiple modalities coexist, features can be extracted from them simultaneously to supplement each other’s information. An actual case study, the typhoon Lichma that swept China in 2019, has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.
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.
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.
As the “engine” of equipment continuous operation and repeated operation, equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems. As the basis and guide for the planning and implementation of equipment maintenance tasks, the equipment damage measurement is an important guarantee for the effective implementation of maintenance support. Firstly,this article comprehensively analyses the influence factors to damage measurement from the enemy’s attributes, our attributes and the battlefield environment starting from the basic problem of wartime equipment damage measurement. Secondly, this article determines the key factors based on fuzzy comprehensive evaluation (FCE) and performed principal component analysis (PCA) on the key factors. Finally, the principal components representing more than 85% of the data features are taken as the input and the equipment damage quantity is taken as the output. The data are trained and tested by artificial neural network (ANN) and random forest (RF). In a word, FCE-PCA-RF can be used as a reference for the research of equipment damage estimation in wartime.
It is important to calculate the reachable domain (RD) of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient database-generation method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node (RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than ${0.01^ \circ }$ on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.
To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion, but the expert knowledge is not fully utilized, a heterogeneous information fusion recognition method based on belief rule structure is proposed. By defining the continuous probabilistic hesitation fuzzy linguistic term sets (CPHFLTS) and establishing CPHFLTS distance measure, the belief rule base of the relationship between feature space and category space is constructed through information integration, and the evidence reasoning of the input samples is carried out. The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition. Compared with the other methods, the proposed method has a higher correct recognition rate under different noise levels.
In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume. When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph. However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.
Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling. Finally, by employing the proposed effector and the controller, numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.
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.
In this paper, an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle. Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time. The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatio-temporal aligned hull deformation measurement model. In addition, two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation. The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.
Based on the wave attack task planning method in static complex environment and the rolling optimization framework, an online task planning method in dynamic complex environment based on rolling optimization is proposed. In the process of online task planning in dynamic complex environment, online task planning is based on event triggering including target information update event, new target addition event, target failure event, weapon failure event, etc., and the methods include defense area reanalysis, parameter space update, and mission re-planning. Simulation is conducted for different events and the result shows that the index value of the attack scenario after re-planning is better than that before re-planning and according to the probability distribution of statistical simulation method, the index value distribution after re-planning is obviously in the region of high index value, and the index value gap before and after re-planning is related to the degree of posture change.
Survivability is used to evaluate the ability of the satellite to complete the mission after failure, while the duration of maintaining performance is often ignored. An effective backup strategy can restore the constellation performance timely, and maintain good network communication performance in case of satellite failure. From the perspective of network utility, the low Earth orbit (LEO) satellite constellation survivable graphical evaluation and review technology (GERT) network with backup satellites is constructed. A network utility transfer function algorithm based on moment generating function and Mason formula is proposed, the network survivability evaluation models of on-orbit backup strategy and ground backup strategy are established. The survivable GERT model can deduce the expected maintenance time of LEO satellite constellation under different fault states and the network utility generated during the state maintenance period. The case analysis shows that the proposed survivable GERT model can consider the satellite failure rate, backup satellite replacement rate, maneuver control replacement ability and life requirement, and effectively determine the optimal survivable backup strategy for LEO satellite constellation with limited resources according to the expected network utility.
In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory (LSTM) neural network is nested into the extended Kalman filter (EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states, an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF (AEKF) when there exist large uncertainties in the system model.
This paper mainly focuses on stability analysis of the nonlinear active disturbance rejection control (ADRC)-based control system and its applicability to real world engineering problems. Firstly, the nonlinear ADRC(NLADRC)-based control system is transformed into a multi-input multi-output (MIMO) Lurie-like system, then sufficient condition for absolute stability based on linear matrix inequality (LMI) is proposed. Since the absolute stability is a kind of global stability, Lyapunov stability is further considered. The local asymptotical stability can be determined by whether a matrix is Hurwitz or not. Using the inverted pendulum as an example, the proposed methods are verified by simulation and experiment, which show the valuable guidance for engineers to design and analyze the NL ADRC-based control system.
For the multicopter with more than four rotors, the rotor fault information is unobservable, which limits the application of active fault-tolerant on multicopters. This paper applies an existing fault-tolerant control method for quadcopter to multicopter with more than four rotors. Without relying on rotor fault information, this method is able to stabilize the multicopter with multiple rotor failures, which is validated on the hexacopter and octocopter using the hardware-in-the-loop simulations. Additionally, the hardware-in-the-loop simulations demonstrate that a more significant tilt angle in flight will inhibit the maximum tolerable number of rotor failures of a multicopter. The more significant aerodynamic drag moment will make it difficult for the multicopter to regain altitude control after rotor failure.
In consideration of the field-of-view (FOV) angle constraint, this study focuses on the guidance problem with impact time control. A deep reinforcement learning guidance method is given for the missile to obtain the desired impact time and meet the demand of FOV angle constraint. On basis of the framework of the proportional navigation guidance, an auxiliary control term is supplemented by the distributed deep deterministic policy gradient algorithm, in which the reward functions are developed to decrease the time-to-go error and improve the terminal guidance accuracy. The numerical simulation demonstrates that the missile governed by the presented deep reinforcement learning guidance law can hit the target successfully at appointed arrival time.
Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment, the key technologies such as machine vision and manipulator control are studied, and a complete manipulator vision tracking system is designed. Firstly, Denavit-Hartenberg (D-H) parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator. At the same time, a binocular camera is used to obtain the three-dimensional position of the target. Secondly, in order to make the manipulator track the target more accurately, the fuzzy adaptive square root unscented Kalman filter (FSRUKF) is proposed to estimate the target state. Finally, the manipulator tracking system is built by using the position-based visual servo. The simulation experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter (SRUKF), which meets the application requirements of the manipulator tracking system, and basically meets the application requirements of the manipulator tracking system in the practical experiments.
Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection performance. This paper proposes a method to handle false alarms in heterogeneous change detection. A lightweight network of two channels is bulit based on the combination of convolutional neural network (CNN) and graph convolutional network (GCN). CNNs learn feature difference maps of multitemporal images, and attention modules adaptively fuse CNN-based and graph-based features for different scales. GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels, generating change maps. Experimental evaluation on two datasets validates the efficacy of the proposed method in addressing false alarms.
The quality of synthetic aperture radar (SAR) image degrades in the case of multiple imaging projection planes (IPPs) and multiple overlapping ship targets, and then the performance of target classification and recognition can be influenced. For addressing this issue, a method for extracting ship targets with overlaps via the expectation maximization (EM) algorithm is proposed. First, the scatterers of ship targets are obtained via the target detection technique. Then, the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP. Afterwards, a novel image amplitude estimation approach is proposed, with which the radar image of a single target with a single IPP can be generated. The proposed method can accomplish IPP selection and targets separation in the image domain, which can improve the image quality and reserve the target information most possibly. Results of simulated and real measured data demonstrate the effectiveness of the proposed method.
The industrial Internet of Things (IIoT) is a new industrial idea that combines the latest information and communication technologies with the industrial economy. In this paper, a cloud control structure is designed for IIoT in cloud-edge environment with three modes of 5G. For 5G based IIoT, the time sensitive network (TSN) service is introduced in transmission network. A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration. For a transmission control protocol (TCP) model with nonlinear disturbance, time delay and uncertainties, a robust adaptive fuzzy sliding mode controller (AFSMC) is given with control rule parameters. IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows. IIoT workflow scheduling is a non-deterministic polynomial (NP)-hard problem in cloud-edge environment. An adaptive and non-local-convergent particle swarm optimization (ANCPSO) is designed with nonlinear inertia weight to avoid falling into local optimum, which can reduce the makespan and cost dramatically. Simulation and experiments demonstrate that ANCPSO has better performances than other classical algorithms.
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.
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.
The application scope of the forward scatter radar (FSR) based on the Global Navigation Satellite System (GNSS) can be expanded by improving the detection capability. Firstly, the forward-scatter signal model when the target crosses the baseline is constructed. Then, the detection method of the forward-scatter signal based on the Rényi entropy of time-frequency distribution is proposed and the detection performance with different time-frequency distributions is compared. Simulation results show that the method based on the smooth pseudo Wigner-Ville distribution (SPWVD) can achieve the best performance. Next, combined with the geometry of FSR, the influence on detection performance of the relative distance between the target and the baseline is analyzed. Finally, the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate (CFAR) detection.
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.
Small video satellites have unique advantages of short development cycle, agile attitude maneuver, real-time video imaging. They have broad application prospects in space debris, faulty spacecraft, and other space target detection and tracking. However, when a space target first enters the camera’s visual field, it has a relatively large angular velocity relative to the satellite, which makes it easy to deviate from the visual field and cause off-target problems. This paper proposes a novel visual tracking control method based on potential function preventing missed targets in space. Firstly, a circular area in the image plane is designed as a mandatory restricted projection area of the target and a visual tracking controller based on image error. Then, a potential function is designed to ensure continuous and stable tracking of the target after entering the visual field. Finally, the stability of the control is proved using Barbarat’s lemma. By setting the same conditions and comparing with the simulation results of the proportion-derivative (PD) control method, the results show that when there is a large relative attitude motion angular velocity between the target and the satellite, the tracking method based on potential function can ensure that the target does not deviate from the field-of-view during the tracking control process, and the projection of target is controlled to the desired position. The proposed control method is effective in eliminating tracking error and preventing off-target simultaneously.
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.
With the development of positioning technology, location services are constantly in demand by people. As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation. The pedestrian navigation based on radio is subject to environmental occlusion leading to the degradation of positioning accuracy. The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit (MIMU) is less susceptible to environmental interference, but its errors dissipate over time. In this paper, a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods. To suppress attitude errors, optimal feedback coefficients are established by pedestrian motion characteristics. To extend navigation time and improve positioning accuracy, the step length in subsequent movements is compensated by the first step length. The experimental results show that the positioning accuracy of the proposed method is improved by more than 47% and 44% compared with the pure inertia-based method combined with step compensation and the traditional complementary filtering combined method with step compensation. The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.
Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics. Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel, execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.
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.