Target Trajectory Tracking Research Articles

Improved Variable Structure Interacting Multimodels for Target Trajectory Tracking and Extrapolation

To improve the lengthy computation time of conventional variable structure interacting multiple model (VSIMM) algorithm and increase the precision of target prediction and extrapolation, the target state and flight intent information captured by the Automatic Dependent Surveillance-Broadcast (ADS-B) are used as the model’s prior information; combining this information with VSIMM theoretical framework, we purpose an intent variable structure interacting multiple model (INT-VSIMM) algorithm. Firstly, the motion pattern of the target in the flight phase of the flight path is decomposed, and complete sets of motion models are established. Secondly, according to the principle of directed graph switching, a model set switching method is designed, which is mainly based on “hard” switching and supplemented by “soft” switching. Finally, the INT-VSIMM algorithm is used to track the trajectory of the target aircraft, and short-term trajectory extrapolation is performed based on the target state estimation. The simulation results show that the target tracking performance computational time based on the INT-VSIMM algorithm is superior to the comparative existing methods, and the extrapolated trajectory has less error in the short term, which can satisfy the needs of conflict detection.

Fractional-order PID controller tuned by particle swarm optimization algorithm for a planar CDPR control

The use of cable-driven parallel robots (CDPRs) has been steadily increasing across various sectors due to their expansive workspaces, impressive payload-to-mass ratios, and cost-effective designs. Controlling these robots, particularly those with substantial actuation redundancy, can present challenges. This research paper proposes the implementation of a fractionalorder proportional-integral-derivative (FOPID) controller to effectively regulate the end-effector of a planar CDPR with four actuation cables. The parameters of the controller are fine-tuned using the particle swarm optimization (PSO) algorithm to ensure optimal performance. The proposed controller's performance is evaluated through two numerical experiments: target tracking and trajectory tracking using a point-to-point approach. Furthermore, a comparative study is conducted to highlight the controller's performance, comparing the proposed FOPID controller with both the classical PID controller and an optimized PID controller. The achieved results demonstrate that the proposed controller exhibits superior performance in terms of tracking accuracy and smoothness of control signals when compared to the other controllers under investigation. As a result, the proposed controller design represents a substantial advancement in control performance and can be regarded as a promising control strategy for CDPRs.

Designing an Athletic Training Assistance System under Deep Learning Guided by Kinesiology Theory

Abstract The combined application of computer artificial intelligence technology and sports promotes the positive development of the sports industry. The combined application of computer artificial intelligence technology and sports promotes the positive development of the sports industry. After completing the model construction, the structure of the external limb sports training auxiliary machinery is designed, and the system structure of the sports training auxiliary machinery is constructed by combining the needs of sports training. The optimization of external limb sports training auxiliary machinery was achieved by performing and solving mathematical modeling of a single limb. The performance of the action recognition model and the sports auxiliary machinery is examined through experiments, in which the action recognition model based on deep learning in this paper has the best effect, which is 95.75%, and the loss values of the model after convergence are all below 0.2. In the empirical testing of the performance of the sports assistive machine, the obtained target trajectory tracking error is within ±3mm and ±2mm, respectively, and the performance of the deep learning-based action recognition model and the external limb sports training assistive machine is excellent, which can effectively help the sports training. The purpose of this study is to provide design direction and practical significance for the design of a sports training assistance system.

Transmedia Performance Research and Motion Control of Unmanned Aerial–Aquatic Vehicles

This paper presents an improved motion controller based on the backstepping method to address nonlinear control challenges in unmanned aerial–aquatic vehicles (UAAVs), enabling them to navigate between two different media. The nonlinear control approach is applied to UAAV motion control, incorporating filters to improve stability. The study designs motion controllers for three UAAV phases: underwater, in the air, and transitioning between media. Fluid simulations of the emergence process of the UAAV for future field experiments were conducted. By fine-tuning the simulations, a comprehensive understanding of the vehicle’s performance is obtained, offering crucial insights for the development of subsequent control systems. Simulation results confirm the controller’s ability to achieve target trajectory tracking with control system responses that meet practical requirements. The controller’s performance in attitude control and trajectory tracking is verified in underwater gliding, transmedia transitions, and airborne phases, demonstrating its effectiveness.

Autonomous Drone Electronics Amplified with Pontryagin-Based Optimization

In the era of electrification and artificial intelligence, direct current motors are widely utilized with numerous innovative adaptive and learning methods. Traditional methods utilize model-based algebraic techniques with system identification, such as recursive least squares, extended least squares, and autoregressive moving averages. The new method known as deterministic artificial intelligence employs physical-based process dynamics to achieve target trajectory tracking. There are two common autonomous trajectory-generation algorithms: sinusoidal function- and Pontryagin-based generation algorithms. The Pontryagin-based optimal trajectory with deterministic artificial intelligence for DC motors is proposed and its performance compared for the first time in this paper. This paper aims to simulate model following and deterministic artificial intelligence methods using the sinusoidal and Pontryagin methods and to compare the differences in their performance when following the challenging step function slew maneuver.

Study on Maneuvering Target Trajectory Simulation and Tracking Based on IMM algorithm

On the background of air defense missile which use radar command guidance, this article focuses on the maneuvering target trajectory simulation and tracking problem by considering possible maneuvering modes and target overload. Taking Serpentine maneuvering (S maneuvering) as an example, the target trajectory is simulated based on overload using stepwise design method, and data are measured through sensor models. Since single model cannot model the maneuvering target motion exactly, the estimation is addressed by combining the interacting multiple model algorithm and unscented Kalman filter algorithm, so as to study the applications of maneuvering target trajectory simulation and tracking in air defense missile systems. The effectiveness of the algorithm is demonstrated with simulated data.

Слежения надводных маневрирующих целей на основе интерактивного многомодельного алгоритма

The volume of sea and river transportation is increasing every year. As a result, the requirements for security systems in water transport are increasing. Among the measures to improve the safety of navigation, one of the most important ones is the improvement of data processing methods in ship control systems, including the target trajectory tracking. In recent years, much attention has been paid to the target trajectory tracking. An interactive multi-model (IMM) algorithm is proposed for tracking maneuvering surface vessels. The simulation of the work of the algorithm for trajectory filtering of targets in the Matlab environment is given. The next direction is suggested in the conclusion.

Multi-target trajectory tracking in multi-frame video images of basketball sports based on deep learning

INTRODUCTION: There is occlusion interference in the multi-target visual tracking process of basketball video images, which leads to poor accuracy of multi-target trajectory tracking. This paper studies the multi-target trajectory tracking method in multi-frame video images of basketball sports based on deep learning. OBJECTIVES: Aiming at the problem of target occlusion in the tracking process and the problem of trajectory tracking anomaly caused by target occlusion, a modified algorithm is proposed. METHODS: The method is divided into two parts: detection and tracking. In the detection part, the YOLOv3 algorithm in deep learning technology is used to detect each target in the video, and the original YOLOv3 backbone network Darknet-53 is replaced by the lightweight backbone network MobileNetV2 to extract the target features. RESULTS: Based on the target detection results, the Kalman filter is used to predict the next position and bounding box size of the target to obtain the target trajectory prediction results according to the current target position, then a hierarchical data association algorithm is designed, and multi-target tracking of the same category is completed based on the target appearance feature similarity and feature similarity. CONCLUSION: The experimental results show that the method can accurately detect the targets in multi-frame video images in basketball sports and obtain high-precision target trajectory tracking results.

Infrared Sensation-Based Salient Targets Enhancement Methods in Low-Visibility Scenes.

Thermal imaging is an important technology in low-visibility environments, and due to the blurred edges and low contrast of infrared images, enhancement processing is of vital importance. However, to some extent, the existing enhancement algorithms based on pixel-level information ignore the salient feature of targets, the temperature which effectively separates the targets by their color. Therefore, based on the temperature and pixel features of infrared images, first, a threshold denoising model based on wavelet transformation with bilateral filtering (WTBF) was proposed. Second, our group proposed a salient components enhancement method based on a multi-scale retinex algorithm combined with frequency-tuned salient region extraction (MSRFT). Third, the image contrast and noise distribution were improved by using salient features of orientation, color, and illuminance of night or snow targets. Finally, the accuracy of the bounding box of enhanced images was tested by the pre-trained and improved object detector. The results show that the improved method can reach an accuracy of 90% of snow targets, and the average precision of car and people categories improved in four low-visibility scenes, which demonstrates the high accuracy and adaptability of the proposed methods of great significance for target detection, trajectory tracking, and danger warning of automobile driving.

Investigation of Stepped-Frequency Hybrid Modulation Radar for Respiration Monitoring, 2-D Localization, and Moving Target Tracking

This article presents a stepped-frequency hybrid modulation (SFHM) methodology, which is proposed to work in conjunction with a frequency scanning array to effectively perform respiration monitoring, 2-D localization for stationary human subjects, and target trajectory tracking for moving ones. Conventional frequency-shift keying (FSK) radar can accurately detect the absolute distance and small displacement but suffers from the problem of a maximum unambiguous range. In comparison, continuous-wave radar with a chirp signal has no range limitation but suffers from a limited displacement measurement accuracy. To solve the two problems, SFHM is proposed to make full use of the advantages of both FSK mode and chirp mode, collaboratively resolving each other’s issues. In addition, the high flexibility of SFHM facilitates the implementation of a frequency scanning array, which provides a simple and cost-efficient means for fast beam steering. A prototype of the radar system is implemented, and a new signal separation algorithm was derived based on the SFHM theory. Various types of experiments confirm the ability of the radar to accurately provide the respiration and range information of stationary subjects and trajectory information of moving subjects.

Nuclear Reactor Power Level Model Predictive Control: A Consideration of Coolant Outlet Temperature Relaxation Tracking Method

Nuclear reactor core power level control and the average coolant temperature usually adopt independent control strategies, but tracking only one single core power makes the coolant temperature control risky in a disturbance situation. To solve this problem, a reference trajectory calculation based on the coolant outlet temperature under the steady state of the reactor power is designed; then, a model predictive controller with a dual objective optimization function, which also includes a relaxation factor, is constructed. By setting the tracking weight matrix in the controller, the tendency setting of dual target trajectory tracking can be realized. Simulation experiments verify the effectiveness of core power level control considering the coolant outlet temperature trajectory tracking. Furthermore, the tracking effect of different weight matrices is simulated and analyzed. The results show that the developed control strategy is more flexible than a single control scheme.

Pedestrian motion centroid model based on robot dynamics

In recent years, with the rapid development of computer vision technology, image-based human body research has become an important task, such as pedestrian target detection, trajectory tracking, posture estimation and behaviour recognition. The centre of mass is one of the important characteristics that can reflect the phenomenon of pedestrian movement. This paper first introduces the biped robot model in robotics, starting from forward and inverse kinematics, to find the mapping relationship between the position of each joint and the pose of the end effector. Then, corresponding to the skeleton model of the human joint points, the characteristics of the bone posture and joint angle are determined. The moment of inertia factor is introduced, and the motion superposition of different joint points is considered to establish a pedestrian motion centroid model. By calculating the equivalent dynamic centroid, the pedestrian kinematics law can be explored and the pedestrian movement mechanism can be more deeply recognized.

Target Tracking Algorithm for Table Tennis Using Machine Vision.

The current table tennis robot system has two common problems. One is the table tennis ball speed, which moves fast, and it is difficult for the robot to react in a short time. The second is that the robot cannot recognize the type of the ball's movement, i.e., rotation, top rotation, no rotation, wait, etc. It is impossible to judge whether the ball is rotating and the direction of rotation, resulting in a single return strategy of the robot with poor adaptability. In this paper, these problems are solved by proposing a target trajectory tracking algorithm for table tennis using machine vision combined with Scaled Conjugate Gradient (SCG). Real human-machine game's data are obtained in the proposed algorithm by extracting ten continuous position information and speed information frames for feature selection. These features are used as input data for the deep neural network and then are normalized to create a deep neural network algorithm model. The model is trained by the position information of the successive 20 frames. During the initial sets of experiments, we found the shortcomings of the original SCG algorithm. By setting the accuracy threshold and offline learning of historical data and saving the hidden layer weight matrix, the SCG algorithm was improved. Finally, experiments verify the improved algorithm's feasibility and applicability and show that the proposed algorithm is more suitable for table tennis robots.

Artificial Fuzzy-PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms.

Mobile robots are promising devices which are dedicated to human comfort in all areas. However, the control algorithm of the wheels of mobile robot is entirely challenging due to the nonlinearity. Recently, the classical PID (proportional-integral-derivative) controllers are frequently used in robotics for their high accuracy and the smooth determination of their parameters. A robust approach called fuzzy control which is based on the conversion of linguistic inference sets in a suitable control value is a widely used method in industrial system control in our days. A new challenging method to solve the problem of intelligent navigation of nonholonomic mobile robot is suggested. In this work, the presented methodology is based on three hybrid fuzzy logic PID controllers which are adapted to guarantee target achievement and trajectory tracking. A fuzzy-PID control algorithm is designed with 2 inputs and 3 outputs. By the information given by the system response, error and error derivate can be used to extract and adopt the PID controller parameters: proportional, integral, and derivative gains. Besides, a tuning value A is introduced to improve the resulted response in terms of speeding up and reducing error, overshoot, and oscillation, as well as reducing ISE and IAE values. A modelization of a differential drive mobile robot is presented. The developed algorithm is tested and implemented to this mobile robot model via Simulink/MATLAB.

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

With the development of information technology in the network era and the popularization of the 5G era, UAV‐related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross‐validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real‐time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real‐time system performance, using the results of the TPD‐KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.

Fuzzy adaptive automatic train operation control with protection constraints: A residual nonlinearity approximation-based approach

In this study, we present fuzzy adaptive control based on residual nonlinearity approximation in the presence of protection constraints for the target trajectory tracking problem observed in automatic train operation. Herein, protection constraints refer to a condition wherein the speed and position of a controlled train are not allowed to surpass the boundaries imposed by automatic train protection and moving authority. By defining proper coordinate transformation, the protection constraints are converted to an error-prescribed performance control problem that facilitates operational efficiency by reducing the margin with respect to target trajectories. Based on the prescribed performance control methodology, we present an improved scheme using fuzzy residual nonlinearity approximation and establish the uniformly ultimately boundedness (UUB) property. A novel feature therein is that the ultimate boundary of the proposed scheme is simultaneously characterized by the prescribed performance functions and control parameters, with rigorous and analytically mathematical expressions; while pioneering the prescribed performance control methodology, the ultimate boundary is characterized solely by the prescribed performance functions. To verify the effectiveness and advantages of the proposed scheme, the controllers are applied to the automatic train operation on the Beijing Yizhuang line, which contains 13 operational intervals. Finally, comparative and simulation results are presented to validate the proposed method.

NMTLAT: A New robust mobile Multi-Target Localization and Tracking Scheme in marine search and rescue wireless sensor networks under Byzantine attack

After a shipwreck (ship collision, explosion, disappearance, etc.), people rely on the marine search and rescue wireless sensor networks (MSR-WSNs) as one key weapon to locate and track drowning targets to save lives and assets. However, due to the complex and dynamic ocean environment, marine sensors could be captured by malicious attackers and then become Byzantine sensors. Byzantine sensors may tamper with the actual sensor data at a fixed or time-varying probability and transmit it to the data fusion center (DFC), which renders the normal localization and tracking function of the search and rescue system downgrade or even fail. To address this issue, we develop a New robust marine mobile Multi-Target LocAlization and Tracking scheme called NMTLAT by eliminating abnormal measurement data from the initial measurement data. NMTLAT works in several steps. By analyzing and mining sensors data and behavior, we firstly employ the information entropy of the system composed of a single sensor and their neighbor sensors to develop an efficient dynamic threshold based Byzantine node identification method. After migrating Byzantine sensors, the DFC utilizes sensor-aware and preprocessed marine data of the beacon or honest sensors to complete target localization and trajectory tracking. Specifically, NMTLAT consists of a novel distributed and cooperative multi-target localization and tracking algorithm using both received signal strength indication (RSSI) and priori information of sensors location. NMTLAT employs the importance sampling method to approximate the posterior probability distribution of the sensors and targets location. Furthermore, when the marine target is outside MSR-WSNs coverage, a piecewise function is utilized to characterize the likelihood of finding a drowning target in the search and rescue sea area. Finally, the Lyapunov’s second stability theorem is adopted to measure the stability of the NMTLAT. Our extensive simulation experiments validate that the NMTLAT performance is superior to existing solutions in various marine search and rescue scenarios.

Adaptive trajectory tracking control strategy of intelligent vehicle

The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability.

Tracking algorithm of snowboard target in intelligent system

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

Research on Maneuvering Trajectory Tracking Accuracy of Target Missile Based on Feedback Linearization

Aiming at the trajectory tracking accuracy of missile target, a trajectory tracking guidance law is designed based on feedback linearization theory and linear quadratic optimal control theory, and the tracking accuracy of the designed trajectory tracking guidance law under different maneuvering trajectories is tested. The motion equations of missiles are typical non-linear motion models, so feedback linearization is introduced as an exact linearization method, and the definition and necessary and sufficient conditions of feedback linearization theory are listed. Firstly, the non-linear equations of the longitudinal motion of target missile are established, and the feedback linearization method is used to linearize the non-linear model accurately. Then, the linearized model is designed by using the linear quadratic optimal control theory, and a target trajectory tracking guidance law is obtained. Finally, given gust disturbance and different maneuvering trajectories, the designed trajectory tracking guidance law is simulated, and the tracking accuracy of the guidance law is studied by changing the control coefficient. The results show that the trajectory tracking guidance law based on feedback linearization has excellent trajectory tracking ability, good tracking accuracy and strong anti-jamming ability under different maneuvering trajectories.