Strong Anti-jamming Ability Research Articles

Error analysis and calibration based on division-of-aperture bionic polarization navigation systems

Bionic polarization navigation technology has become an important navigation technology due to its non-dependence on satellite technology, and strong anti-jamming ability. However, in practical applications, the performance parameters of the structure of division-of-aperture polarization navigation systems limit the accuracy of polarization navigation. Therefore, this paper investigates the measurement error model and the corresponding calibration method for the multi-channel structure of division-of-aperture polarization navigation systems. In this paper, we first establish a mathematical model between the error of skylight polarization information and the performance parameters of navigation systems for the unique multi-channel structure of division-of-aperture polarization navigation systems. Secondly, for the first time, a mathematical model is used to quantitatively analyze and give the performance parameters suitable for the acquisition of skylight polarization information (polarizer installation errors ≤2°, grayscale non-uniformity ≤10(DN)). Then, a polarizer installation errors calibration algorithm based on Singular Value Decomposition (SVD) with linear fitting for grayscale non-uniformity response calibration algorithm is proposed and validated through indoor and outdoor calibration experiments. The experimental results show that the azimuth accuracy after calibration of polarizer installation errors can reach 0.382°, and the azimuth accuracy after calibration of grayscale non-uniformity response can reach 0.101°.

Flywheel rotor cross-filter tracking feedback control

In light of the matter that the fixed-parameter cross-filter circuit cannot satisfy the phase compensation requirement in the full speed range, a cross-feedback tracking control method based on cross-filter feedback, in which the high-pass cutoff frequency varies with the nutation frequency, is proposed. The stability analysis of the designed control method is carried out by using the phase angle margin and the root trajectory diagram, and the results show that the cross-feedback tracking control can increase the phase angle margin and significantly enhance the stability of the system. Simulation results of the magnetic levitation rotor system show that the designed control method possesses the benefits of high stability, rapid response time, and strong anti-jamming ability.

Hierarchical porous UiO-66 composites modified by dual competitive strategy for adsorption of oxytetracycline

Adsorption is a popular technology for solving water-pollution problems because it is highly efficient, affordable and environmentally friendly. In this study, a dual competition strategy was developed to improve oxytetracycline adsorption by modulating the UiO-66 defects. A series of defect-rich hierarchical porous UiO-66 was prepared using the competitive strategy of bi-metal (Zn2+/Zr4+) and bi-ligand (terephthalic acid/monocarboxylic acid). Compared with the pristine UiO-66, the defect-rich hierarchical porous UiO-66 has a higher adsorption efficiency and faster adsorption rate. Among them, the greatest adsorption ability of But-HP-UiO-66 was 209.97 mg∙g−1 at pH= 6.0, which was 9.89 times greater than UiO-66. The kinetic and isotherm fitting results showed that the adsorption was consistent with the Elovich and Freundlich models. According to the response surface model design based on four factors (OTC concentration, pH, humic acid and SO42- intensity), But-HP-UiO-66 exhibited an excellent adsorption effect and strong anti-jamming ability. The prominent advantages of this composite are its rich pore structure and abundant defects. The adsorption mechanism showed that the electrostatic interactions, metal-organic complexation, pore adsorption, hydrogen bonding and π-π interactions performed vital functions. This study provides a promising method for manufacturing strongly hierarchical porous MOFs and strengthens their potential applications in antibiotic elimination.

DCS-CTN: Subtle Gesture Recognition Based on TD-CNN-Transformer via Millimeter-Wave Radar

Gesture recognition has been a hot research topic in human-computer interaction, since contactless gesture recognition will provide increasing applications in many fields. Millimeter-wave (mmWave) radar well serves this technology because of its high accuracy, easy integration, and strong anti-jamming ability in moving object detection. However, it is still challenging to meet the requirement of high precision in subtle gesture recognition based on traditional methods via point cloud or Range-Doppler heat map of millimeter-wave radar. Considering the raw data from millimeter-wave radar with more information such as phase, we propose a system that uses the constructed millimeter-wave radar data cube sequence and Timedistributed-CNN-Transformer network (CTN), called DCS-CTN system, to get higher hand gesture recognition accuracy. In this system, we introduce a time-distributed wrapper (TD) and convolution neural network (CNN) to extract local features of the data cube sequence, a position encoder to retain time information of the sequence, and a transformer network to get global features of the sequence. The experiments results show that this system can achieve hand gesture recognition accuracy of 99.75%, which is significantly higher than the other traditional approaches.

Leader-Follower UAV formation flight control based on feature modelling

To solve the problems of backstepping error and poor dynamic tracking approach rate in traditional PID neural network control in UAV formation flight control, a Leader-Follower UAV formation flight control method based on feature modelling is proposed,and the pose relationship model between virtual follower and pilot is established by trajectory tracking and pose dynamic fitting. The pose distribution of thefollower is analyzed in the ground coordinate system, and the parameter information of linear velocity and angular velocity control of UAV is obtained, and the backstepping sliding mode formation controller is formed. The variable structure PID neural network controller is used to design the flight control law of UAV formation, and the fast piecewise power approaching factor is introduced into the PID controller to eliminate the chattering of sliding mode control. The simulation results show that this method can ensure the rapidity of UAV formation flight control also show strong anti-jamming ability. Due to the fast piecewise power approach rate, the UAVs can complete the UAV formation reorganization under disturbance and buffeting in a short time, and the trajectory tracking error approaches zero, and it has good anti-buffeting ability.

Attitude Control of Quadrotor UAV Using Improved Active Disturbance Rejection Control

The quadrotor UAV has complex aerodynamics, unstable altitude, and easy to be disturbed by internal and external uncertainties during flight, so good attitude control becomes a challenge, this paper improves the active disturbance rejection controller to improve the system's anti-jamming ability and response speed in response to the above problems. Firstly, a quadrotor UAV dynamics model is established; then the Ifal function is constructed, which solves the problems that the inflection point of the fal function is not smooth, which is easy to cause the system jitter and the system gain is large when the error is large, and finally the performance is verified by using MATLAB/Simulink simulation software. The simulation results show that the constructed Ifal function is smoother and has better convergence, and compared with the traditional active disturbance rejection control, the improved active disturbance rejection control system has faster response speed, stronger anti-jamming ability and robustness in the attitude control of quadrotor UAV.

2chADCNN: A Template Matching Network for Season-Changing UAV Aerial Images and Satellite Imagery

Visual navigation based on image matching has become one of the most important research fields for UAVs to achieve autonomous navigation, because of its low cost, strong anti-jamming ability, and high performance. Currently, numerous positioning and navigation methods based on visual information have been proposed for UAV navigation. However, the appearance, shape, color, and texture of objects can change significantly due to different lighting conditions, shadows, and surface coverage during different seasons, such as vegetation cover in summer or ice and snow cover in winter. These changes pose greater challenges for feature-based image matching methods. This encouraged us to overcome the limitations of previous works, which did not consider significant seasonal changes such as snow-covered UAV aerial images, by proposing an image matching method using season-changing UAV aerial images and satellite imagery. Following the pipeline of a two-channel deep convolutional neural network, we first pre-scaled the UAV aerial images, ensuring that the UAV aerial images and satellite imagery had the same ground sampling distance. Then, we introduced attention mechanisms to provide additional supervision for both low-level local features and high-level global features, resulting in a new season-specific feature representation. The similarity between image patches was calculated using a similarity measurement layer composed of two fully connected layers. Subsequently, we conducted template matching to estimate the UAV matching position with the highest similarity. Finally, we validated our proposed method on both synthetic and real UAV aerial image datasets, and conducted direct comparisons with previous popular works. The experimental results demonstrated that our method achieved the highest matching accuracy on multi-temporal and multi-season images.

A coupling model of multi-feature fusion and multi-machine learning model integration for defect recognition

As Oil and gas pipelines are vulnerable to physical defects such as dents, cracks and corrosion leading to the risk of pipeline leakage. In this context, the recognition of these pipeline defects by using magnetic flux leakage (MFL) signal can provide a reliable reference and a good evaluation standard for predicting the remaining life of in-service pipelines. Nevertheless, the complexity of the MFL signal brings severe challenges to the recognition accuracy. This study, therefore, proposes a new coupling model for defect recognition based on multi-feature fusion and multi-machine learning model integration. This model is divided into three analysis phase, namely feature extraction phase, feature fusion phase and defect recognition phase. In the first phase, three kinds of features are extracted to obtain as much defect MFL signal information as possible. In the second phase, a multi-feature fusion method based on Dempster-Shafer (DS) evidence theory and Apriori algorithm is proposed, eliminating redundant features and realizing information complementation between different feature sets. In the third phase, we introduce a multi-machine learning model integration network (Stacking) to improve the defect recognition accuracy, which is inputted by the feature subset obtained from multi-feature fusion. By combining multi-feature fusion method with the Stacking mechanism, this coupling defect recognition model realizes the recognition of defect MFL signals. Experimental work shows that the proposed model improves the recognition accuracy and has the advantages of strong anti-jamming ability.

High-Efficiency PCSK-CFFH System Design for IoT Networks

Industrial Internet of Things (IIoT) brings opportunities for innovation and upgrading to industrial production, but meanwhile, it poses unprecedented challenges on the physical layer of wireless communications. In this article, we propose a novel frequency hopping transmission scheme for IIoT, which owns much high efficiency and strong anti-jamming ability. This new scheme is built based on the combination of coherent fast frequency hopping (CFFH) and cyclic code shift key (CCSK), named phase cyclic shift-keying-based CFFH (PCSK-CFFH). For the proposed PCSK-CFFH system, the shift of orthogonal base sequence is adopted to modulate the phases among interhop, instead of transmitting each hop with the same phase as conventional CFFH. This scheme is able to further exploit the extra dimensions of CFFH signals and enables the CFFH system to transmit additional information at no extra cost on either bandwidth or power. Moreover, the bit error rate performance of the proposed PCSK-CFFH system is analyzed theoretically. To improve its ability against severe narrow-band jamming, the selection combining is introduced in our scheme. Furthermore, the anti-jamming performance of the proposed PCSK-CFFH system is also demonstrated. Finally, the simulation results are shown to demonstrate that: by introducing the interhop dimension to CFFH, PCSK-CFFH improves the power efficiency and spectrum efficiency by multiple times and simultaneously inherits the excellent anti-jamming performance of the conventional CFFH system. It can be concluded that the proposed PCSK-CFFH scheme has a great potential to be applied to low-power and highly reliable IIoT communications.

Double-Loop PID-Type Neural Network Sliding Mode Control of an Uncertain Autonomous Underwater Vehicle Model Based on a Nonlinear High-Order Observer with Unknown Disturbance

An unknown nonlinear disturbance seriously affects the trajectory tracking of autonomous underwater vehicles (AUVs). Thus, it is critical to eliminate the influence of such disturbances on AUVs. To address this problem, this paper proposes a double-loop proportional–integral–differential (PID) neural network sliding mode control (DLNNSMC). First, a double-loop PID sliding mode surface is proposed, which has a faster convergence speed than other PID sliding mode surfaces. Second, a nonlinear high-order observer and a neural network are combined to observe and compensate for the nonlinear disturbance of the AUV system. Then, the bounded stability of an AUV closed-loop system is analyzed and demonstrated using the Lyapunov method, and the time-domain method is used to verify that the velocity- and position-tracking errors of AUVs converge to zero exponentially. Finally, the radial basis function (RBF) neural network PID sliding mode control (RBFPIDSMC) and the RBF neural network PID sliding mode control (RBFPDSMC) are compared with this method in two trajectory tracking control simulation experiments. In the first experiment, the average Euclidean distance of the position-tracking error for this method was reduced by approximately 73.6% and 75.3%, respectively, compared to those for RBFPDSMC and RBFPIDSMC. In the second experiment, the average Euclidean distance of the position tracking error for this method was reduced by approximately 86.8% and 88.8%, respectively. The two experiments showed that the proposed control method has a strong anti-jamming ability and tracking effect. The simulation results obtained in the Gazebo environment validated the superiority of this method.

Detection of Direct Sequence Spread Spectrum Signals Based on Deep Learning

Direct spread spectrum communication has the advantages of strong anti-jamming ability and low probability of interception, which plays an essential role in both civil and military communications. The detection of direct sequence spread spectrum (DSSS) signal becomes very difficult because of its low power spectral density. In this paper, the detection of DSSS signals under non-cooperative conditions is carried out based on the classification technology of deep learning. Firstly, a detection scheme based on convolutional neural network (CNN) is proposed, in which the neural network is used to learn the features of DSSS signal and noise automatically without extracting the features in advance. In addition, we also propose a hybrid CNN-correlation (CORR)-based detection scheme in order to reduce the computational complexity. In this scheme, the autocorrelation of the received signal is truncated and used as the input of the neural network for training and inference. A large number of simulation results show that the detection performance of the proposed two schemes is significantly better than the traditional autocorrelation-based detection method in various scenarios.

Multi-Extended Target Tracking Algorithm Based on VBEM-CPHD

Considering that the extended targets tracking problem of the measurement is a glint noise with an unknown inverse covariance, a new algorithm from a multi-extended target tracking based on the Variational Bayesian Expectation Maximization (VBEM) is proposed. To improve the variational Bayesian technique, a modeled Student’s t distribution is proposed based on multiple Gaussian mixture terms in order to replace the probability hypothesis density (PHD) intensity. The extended target was modeled with a Student’s t distribution with the glint noise, using Gauss-Gamma distribution combining the variational Bayesian technique to obtain an approximate distribution and applying the expectation-maximization algorithm for iterative estimation. The two experiments are compared and analyzed with the VBEM-CPHD algorithm and the traditional extended target tracking algorithm. Experiment 1 estimated the trajectory of the target, compared algorithms of VBEM-CPHD and GM-CPHD with OSPA distance, varied glint noise with the three different measurement noise standard deviations. Experiment 2 completed the examination of the tracking performance and stability of the proposed method and performed to compare the VBEM-CPHD algorithm proposed with the VB-based GM-CPHD (GM-VBCPHD) algorithm under an unknown measurement noise covariance. The experimental results indicate that the algorithm of VBEM-CPHD has high tracking accuracy, good adaptability, and a strong antijamming ability for multiple extended targets under glint noise conditions.

A robust double-parallel extreme learning machine based on an improved M-estimation algorithm

To solve the problem of improving the regression accuracy and model stability of the extreme learning machine(ELM), a new approach based on an improved M-estimation optimized double-parallel extreme learning machine is proposed in this study, namely robust double-parallel extreme learning machine(RD-ELM). Firstly, RD-ELM is constructed with a double parallel forward structure, thus the information can be received from both hidden layer neurons and input layer neurons. Secondly, we use an improved M-estimation to calculate output weights of neural network by iteratively reweighted Least-Squares Estimation(LSE), with weights assigned by the least absolute residual estimation of the samples. Finally, we establish a regression prediction model utilized to test the goodness of fit in a SinC function and verify the regression ability in eight benchmark regression problems. Then the proposed method is applied to an actual operational condition of a power plant. Experimental results show that the proposed method can efficiently process the influence of outliers and noise with strong anti-jamming ability. Compared with other methods, RD-ELM has superior performance that is stronger robustness and better generalization performance in many benchmark data and practical experiments.

Research on junction temperature detection method of power electronic devices based on turn-off time and turn-off loss

The temperature-sensitive electrical parameter (TSEP) method is widely used in the extraction and prediction of junction temperature (Tj) of power semiconductor devices. In this paper, turn-off loss (Eoff) and turn-off time (toff) are taken as temperature-sensitive electrical parameters. It is proved that the junction temperature of the insulated-gate bipolar transistor (IGBT) changes abruptly during switching. The common defects of a single temperature-sensitive electrical parameter, including collector current requirements and poor junction detection accuracy, are considered. Therefore, after proving the linear relationship between junction temperature, turn-off time, and turn-off loss, a hybrid model based on turn-off loss (Eoff) and turn-off time (toff) is proposed to accurately extract junction temperature. The experimental results show that this method has the advantages of high precision and strong anti-jamming ability.

A Visual Navigation System for UAV under Diverse Illumination Conditions

ABSTRACT The high-precision navigation and positioning ability of UAV (Unmanned Aerial Vehicles) is the key factor to reflect its degree of automation. Visual navigation based on image matching has become one of the important research fields for UAV to realize autonomous navigation, because of its low cost, strong anti-jamming ability, and good location result. However, the visual quality of images captured by UAV will be seriously affected by some factors, such as weak illumination conditions or insufficient performance of its sensors. Resolving a series of degradation of low-light images can improve the visual quality and enhance the performance of UAV visual navigation. In this paper, we propose a novel fully convolutional network based on the Retinex theory to solve the degradations of low-light images captured by UAV, which can improve the visual quality of the images and visual matching performance effectively. At the same time, a visual navigation system is designed based on the proposed network. Extensive experiments demonstrate that our method outperforms the existing methods by a large margin both quantitatively and qualitatively, and effectively improves the performance of the image matching algorithms. The visual navigation system can successfully realize the self-localization of UAV under different illumination conditions. Moreover, we also prove that our method is also effective in other practical tasks (e.g. autonomous driving).

Infrared small target detection method based on multi-scale feature fusion

Infrared small target detection is widely used in aerial target detection and tracking system because of its long detection distance and strong anti-jamming ability. Aiming at the shortcomings of the current infrared small target detection algorithm, such as low precision rate and high false alarm rate when dealing with complex background. In this paper, we propose an end-to-end infrared small target detection model (called MFSSD) based on multi-scale feature fusion. Considering the trait of the targets, we propose a feature fusion module by using a refinement and fusion feature map method, and improve the correlation of different channels through the SP Module. The experimental results of three different sequences of infrared image detection show that the average detection accuracy of the MFSSD algorithm in infrared small target detection is as high as 87.8%. Compared with the traditional multi-scale feature fusion detection algorithm, both the precision rate and the recall rate have been significantly improved.

Backstepping Sliding-Mode Control of Piezoelectric Single-Piston Pump-Controlled Actuator

A piston piezoelectric (PZT) pump has many advantages for the use of light actuators. How to deal with the contradiction between the intermittent oil supplying and position control precision is essential when designing the controller. In order to accurately control the output of the actuator, a backstepping sliding-mode control method based on the Lyapunov function is introduced, and the controller is designed on the basis of establishing the mathematical model of the system. The simulation results show that, compared with fuzzy PID and ordinary sliding-mode control, backstepping sliding-mode control has a stronger anti-jamming ability and tracking performance, and improves the control accuracy and stability of the piezoelectric pump-controlled actuator system.

Incremental-data stealth-transmission method in DSSS

A direct-sequence spread-spectrum (DSSS) signal has many advantages, such as low SNR and strong anti-jamming ability; thus, it is widely used in various military and civil communication systems. However, hiding and high-data-rate transmission technologies have become the key factors affecting the future applications of the existing DSSS. To solve these problems, an incremental-data stealth-transmission (IDST) method is proposed, which stealthily transmits incremental data through variations in the spread spectrum function and alternation of the weighted fractional Fourier transform modulation order. The incremental data are not transmitted through a real channel but through the function relationship to achieve index transmission. Without affecting the spread spectrum gain and SNR tolerance of the original data, the proposed method greatly improves the amount of information transmitted and enhances the concealment of incremental data.

Research about the Sensorless Vector Control of Permanent Magnet Synchronous Motor Based on Two-stage Filter Sliding Mode Observer

In order to overcome the disadvantages of installing mechanical sensors and the problems of chattering and low observation accuracy in traditional sliding mode observer sensorless control, a two-stage filter Sliding Mode Observer (SMO) is proposed in this paper. By collecting the current and voltage of the Permanent Magnet Synchronous Motor (PMSM), the SMO algorithm is realized by using the state equation of the motor in the synchronous stationary coordinate system; the position of the rotor is estimated by the arc tangent function, the observation accuracy of the rotor position is improved by increasing phase compensation; the variable cut-off frequency filter is introduced to make the Low Pass Filter (LPF) cut-off frequency can be self-adjusted with the change of rotational speed, which improves the estimation accuracy of rotor position at different rotational speeds. Kalman filter is introduced to form a two-stage filter with variable cut-off frequency LPF, which greatly weakens the chattering of the motor and reduces the observation error. Finally, the simulation is carried out under MATLAB/Simulink. The simulation results show that the PMSM vector control system with two-stage filter sliding mode observer has high estimation accuracy, good dynamic and steady-state performance, strong anti-jamming ability and robustness.

Research on target detection technology of MIMO technology in vehicle collision avoidance

MIMO (multiple input multiple output) vehicle anti-collision radar refers to the application of MIMO system radar in vehicle anti-collision. MIMO radar is a new type of radar which has been put forward in recent years and attracted great attention. It is a kind of radar with multi transmitting and multi receiving. The characteristics of wave form diversity make it have many performance advantages, such as strong anti-jamming ability and strong resolution, compared with traditional radar. In recent years, many researchers have studied the application of MIMO Radar in the field of aviation and land transportation and made remarkable achievements. Compared with the traditional radar, the advantage of MIMO radar is due to the orthogonal performance of the transmitting waveform. The related problems of the pattern are deeply related to the resolution. As an important reference index in radar, the resolution is worthy of further study. In view of the above problems, the main work of this paper is as follows.