Trajectory Of Moving Target Research Articles

Enhancing underwater target detection: Fusion of spatio‐temporal incompletely‐aligned AIS and sonar information via DTW and multi‐head attention mechanism

AbstractIn the field of underwater target detection, the passive sonar is an important means of long‐distance target detection. The sonar detection information typically includes both surface and underwater targets, whereas it is a great challenge on effectively distinguishing between surface and underwater targets solely based on sonar information. Effective fusion of sonar and AIS (Automatic Identification System) data can leverage their complementary nature to compensate for the limitation of sonar information. However, the sonar information and AIS information are acquired based on different detection principles and systems, which are essentially multi‐source heterogeneous information with obvious spatio‐temporal misalignment in nature. Existing fusion methods normally struggle to effectively align sonar and AIS data in both time and space subject to the complexity of the problem. In this study, the Dynamic Time Warping (DTW) algorithm is applied to align sonar and AIS data in the time domain. In addition, a deep learning algorithm with multi‐head attention mechanism is proposed to achieve the spatial alignment of sonar and AIS data, where the matching between the surface targets in AIS data and the same surface targets in sonar data can also be successfully achieved. It provides a priori knowledge to enhance the underwater target detection of the passive sonar by eliminating the interference of the surface targets. Based on the attention mechanism, the abstract features extracted from the intermediate‐layer of the neural networks are found to be effective to represent the typical features of the target motion trajectories, which also demonstrates the effectiveness of the attention mechanism. The experiment results show that the proposed method can successfully achieve a MatchingSucccessRate of over 95% between the AIS targets and sonar detection targets.

Target detection and obstacle avoidance for UAV based on dynamic vision sensor

Aiming at the problem of UAV sensing dynamic targets and avoiding high-speed dynamic obstacles in dynamic environments, a target detection and obstacle avoidance algorithm based on dynamic vision sensor is designed. A filtering method and motion compensation algorithm are designed to filter out background noise and hot spot noise in the event stream as well as redundant events caused by the camera's own motion. A dynamic target fusion detection algorithm that fuses event images and RGB images is designed to ensure the reliability of detection. The target motion trajectory is estimated based on the detection results, and the speed obstacle method is improved to avoid dynamic obstacles by combining the obstacle motion characteristics and UAV dynamic constraints. A large number of simulation tests, handheld tests and flight tests verify the feasibility of the proposed algorithm.

Research on the Motion Control Strategy of a Lower-Limb Exoskeleton Rehabilitation Robot Using the Twin Delayed Deep Deterministic Policy Gradient Algorithm.

The motion control system of a lower-limb exoskeleton rehabilitation robot (LLERR) is designed to assist patients in lower-limb rehabilitation exercises. This research designed a motion controller for an LLERR-based on the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to control the lower-limb exoskeleton for gait training in a staircase environment. Commencing with the establishment of a mathematical model of the LLERR, the dynamics during its movement are systematically described. The TD3 algorithm is employed to plan the motion trajectory of the LLERR's right-foot sole, and the target motion curve of the hip (knee) joint is deduced inversely to ensure adherence to human physiological principles during motion execution. The control strategy of the TD3 algorithm ensures that the movement of each joint of the LLERR is consistent with the target motion trajectory. The experimental results indicate that the trajectory tracking errors of the hip (knee) joints are all within 5°, confirming that the LLERR successfully assists patient in completing lower-limb rehabilitation training in a staircase environment. The primary contribution of this study is to propose a non-linear control strategy tailored for the staircase environment, enabling the planning and control of the lower-limb joint motions facilitated by the LLERR.

Mining Abnormal Patterns in Moving Target Trajectories Based on Multi-Attribute Classification

As a type of time series data, trajectory data objectively record the location information and corresponding time information of an object’s activities. It not only describes the spatial activity trajectory of a moving object but also contains the unique attributes, states, and behavioral characteristics of the moving object itself. It can also reflect the interaction relationship between the object’s activities and various elements in the environment to a certain extent. Therefore, mining from moving target trajectory data to discover implicit, effective, and potentially useful spatiotemporal behavior patterns of moving targets, such as anomaly detection, will have significant research significance. This paper proposes a method for mining abnormal patterns in the trajectory of moving targets based on multi-attribute classification. Firstly, to explore the activity location patterns of single moving targets, a frequent sequence discovery method for moving targets based on sequence patterns is proposed. Furthermore, for moving target trajectory data sets containing multiple attributes, numerical attributes are extracted, and the data are clustered according to attribute classification to extract a set of normal behavior patterns of moving targets. Then, combining the activity location patterns and normal behavior patterns of the moving target, the original trajectory data are compared with them to achieve the goal of detecting abnormal behavior of the moving target. Finally, an incremental anomaly detection scheme is proposed to address the characteristics of fast updates and large numbers of data in trajectory data sets. This involves synchronously updating the frequency of moving target activity patterns and the range of values for normal behavior patterns while updating the trajectory data set, in order to meet the needs of database updates and improve the accuracy and credibility of results.

Motion-guided large aperture ULA to enhance DOA estimation: An inverse synthetic aperture perspective

Direction-of-arrival (DOA) estimation is a critical issue in array signal processing, especially in radar applications. However, the array aperture of high-frequency radars is constrained by the site and platform, which in turn leads to limited DOA estimation performance. In this paper, inspired by the inverse synthetic aperture radar, we design a novel DOA estimation method based on motion-guided large-aperture uniform linear array (ULA). Referring to the concept of inverse synthetic aperture, the novel method utilizes the relative motion between the radar and the target to construct a larger ULA. We demonstrate that the key condition for reconstructing ULAs is suitable data segmentation and reshaping within the coherent integration time. However, the correct segmentation position is related to the target's motion trajectory. For non-cooperative targets, the uncertain segmentation position is regarded as a mismatch problem between the steering and weighting vectors, such that the spatial spectrum becomes a product of two sinc-like functions. With the analysis of the main lobe and grating lobes, the maximum peak-to-grating-lobe ratio criterion is introduced to find the ambiguity-free DOA. The experimental results demonstrate the performance advantages of the proposed DOA estimation algorithm in terms of noise robustness and resolution.

Target Motion Trajectory and Wall Surface Shape Estimation in Complex L-Shaped Corner Scene with Recessed Structures

Target Motion Trajectory and Wall Surface Shape Estimation in Complex L-Shaped Corner Scene with Recessed Structures

Research on Guidance Methods of Digital Twin Robotic Arms Based on User Interaction Experience Quantification.

The evolution of the manufacturing sector coupled with advancements in digital twin technology has precipitated the extensive integration of digital twin robotic arms within the industrial domain. Notwithstanding this trend, there exists a paucity of studies examining the interaction of these robotic arms in virtual reality (VR) contexts from the user's standpoint. This paper delves into the virtual interaction of digital twin robotic arms by concentrating on effective guidance methodologies for the input of their target motion trajectories. Such a focus is pivotal to optimize input precision and efficiency, thus contributing to research on the virtual interaction interfaces of these robotic arms. During empirical evaluations, metrics related to human-machine interaction, such as objective operational efficiency, precision, and subjective workload, were meticulously quantified. Moreover, the influence of disparate guidance methods on the interaction experience of digital twin robotic arms and their corresponding scenarios was investigated. Consequent findings offer pivotal insights regarding the efficacy of these guidance methods across various scenarios, thereby serving as an invaluable guide for future endeavors aiming to bolster interactive experiences in devices akin to digital twin robotic arms.

Research on the Estimation Method of Target Motion Parameters Based on Ternary Array

The ternary array passive positioning technique performs the estimation of target distance and bearing by measuring the time delay difference between array elements. The estimates are limited by the geometric model and time delay measurement errors, and the positioning accuracy and reliability are poor. In this paper, we propose an orientation matching-based motion target localization model. By introducing the target motion model, and establish a matching objective function for the time delay difference obtained by calculating the predefined target motion trajectory and the actual received data of the ternary array separately. The matching objective function is optimally solved by using Burg algorithm in the AR spectrum estimation method, and a highly accurate and reliable target parameter estimation can be obtained in a short time. The results of computer simulation experiments verify the effectiveness of the method.

SiamST: Siamese network with spatio-temporal awareness for object tracking

Recently, Siamese trackers have achieved remarkable tracking performance. However, challenges such as accurate feature representation of targets with different spatial regions and the utilization of diverse target temporal states still need to be addressed. Here, we proposed a Siamese network with spatio-temporal awareness called SiamST. The standard square convolutional kernels and the single feature matching operation hardly represent the targets with different shapes accurately. Therefore, we designed a refined region fusion module that combines multiple convolutional kernels to fit targets with different aspect ratios. Furthermore, we proposed a multi-granularity matching module to obtain more robust feature matching results by combining fine-grained and coarse-grained matching results. However, most existing Siamese trackers do not adequately employ target temporal states. They usually only update the templates, which automatically causes motion information loss. Therefore, we built dynamic templates by screening high-quality samples to describe the target appearance changes accurately. In addition, we designed a trend guidance module to adjust the location prior constraint appropriately to match the tracking results to the target's motion trajectory. Extensive experimental results on eight tracking benchmarks demonstrate the competitive performance of SiamST compared to many advanced trackers.

Line of Sight Tracking Method Based on Feedback Linearization and Command Planning

When the target just enters the field of view of the optoelectronic tracking system, there is a big pointing deviation. If the closed-loop control is directly performed at this time, overshoot or even oscillation will occur, which will easily cause the target to leave the field of view again. At present, most control algorithms are concerned about enhancing the steady-state tracking precision of the system, but seldom consider the transient performance of the system. In this paper, a new definition of pointing deviation is given, in addition, the model of the line-of-sight (LOS) tracking system is realized. On this basis, a method based on feedback linearization is raised, which can compensate measurable disturbance. For both transient response and steady-state performance, a new LOS planning method is presented, which accomplishes the planning and design of the target motion trajectory on the imaging plane. It can be able to avoid the target departing from the field of view and speed up the system response based on fixed control parameters. Through the analysis of experimental results, it can be indicated that the LOS tracking method proposed in this paper can availably promote the transient performance of the system and is existent of real value.

Research on autonomous dynamic real-time capture of robot based on hybrid prediction model

The trajectory of the dynamic target is an important basis for the robot to capture the action, and its advanced prediction precision plays a decisive role in achieving efficient capture. In this paper, a dynamic adaptive weighting method is proposed to establish a hybrid prediction model of GM (1,1) and SESM. The weights are allocated in real time, according to the prediction accuracy to improve the prediction accuracy of a single model and enhance the applicability of spatial trajectory prediction. Then, based on the rolling prediction principle, a high-precision advanced prediction method of the hybrid model is created. Compared with the advanced prediction of the model itself, the average prediction errors of the spatial arc and straight line verification trajectories are reduced by 40.26% and 49.77% respectively. The simulation model of robot dynamic target capture is built in Matlab, and the measured trajectory is used as the target trajectory. The robot capture experiment platform is built, and the target motion trajectory is collected by the camera in real time to capture the experiment, which verifies the effectiveness of the proposed method in actual capture.

SiamMDM: An Adaptive Fusion Network With Dynamic Template for Real-Time Satellite Video Single Object Tracking

Tracking moving targets in satellite videos has attracted wide attention recently. However, the development of target tracking in satellite videos is much slower than that in general videos for the following key reasons. First, typical moving objects in satellite videos consist of few pixels and lose most of their appearance features, making it difficult for the tracker to distinguish the target from the background. Second, the appearance of satellite video objects often changes due to occlusion, illumination variation, or other factors. Classic Siamese tracking networks only use the first frame as the target template, leading to poor tracking results. Third, when the target is fully occluded, it is difficult for the tracker to recapture the target. To address the above problems, we propose a Siamese tracking network based on multiple(M) response map fusion and spatiotemporal constraints in this paper. By generating response maps at different layers of the tracking network and fusing them adaptively, small objects in the satellite videos can be tracked more accurately. Furthermore, a dynamic(D) template update strategy is proposed to cope with possible changes in the appearance of objects in satellite videos, preventing the high dependence on the initial frame. To recapture the target, a score-guided target motion(M) trajectory prediction model is proposed. We call the proposed Siamese tracking network SiamMDM for short. We conducted complete experiments on SatSOT and SV248S, two large satellite video target tracking datasets. The results show that our method achieves state-of-the-art tracking performance while running at over 110 FPS.

Target Trajectory Estimation Algorithm Based on Time–Frequency Enhancement

Doppler through-wall radar (TWR) is one of the mainstream target positioning equipment, acquiring the target instantaneous frequency (IF) to achieve trajectory tracking. However, severe time–frequency interference leads to a significant deviation of the estimated trajectory from the actual position. Hence, in this work, we propose a target trajectory estimation algorithm based on time–frequency enhancement. First, the time–frequency analysis (TFA) of the echo signal is carried out by the short-time Fourier transform (STFT). By analyzing the time–frequency distribution (TFD) of the echo signal, an overcomplete time–frequency dictionary is obtained that can reflect the characteristics of the target. Then, the Wigner–Ville distribution (WVD) is employed to process the radar echo signal, and the predefined dictionary is used to suppress the time–frequency interference in the WVD to achieve time–frequency enhancement. Finally, the target IF curve is obtained from the enhanced TFD to synthesize the target motion trajectory. The experiment results show that the proposed method can suppress time–frequency interference and improve the estimation accuracy of IF values. Compared with the existing methods, the proposed method can effectively improve the accuracy of TWR localization.

Estimation of States Under Colored Measurement Noise (CMN) Using UFIR and Kalman Filters Modified

The estimate process of a moving target trajectory is a well-known problem, where the main objective is to improve the estimation of object position. During the tracking are presented errors or variations between the true position and the estimated. In this paper, we treat such variations as a Gauss-Markov colored measurement noise (CMN). The estimation process is performed in predict and update, where the prediction indicates the next position of the bounding box, and the update is a correction step, which includes the new measurement of the tracking model and helps to improve the estimation. Looking for this improvement we use Kalman and Unbiased Finite Impulse Response filters in the standard version and modified for CMN to demonstrate the filter with the best performance. To test the most robust filter we use a high coloredness factor. The tests were carried out with simulated data (ideal and no ideal conditions) and with benchmark data (no ideal conditions). The UFIR modified for the CMN algorithm showed favorable results with high precision and low RMSE in the object tracking process with benchmark data and under no ideal conditions. While KF CMN showed better results under ideal conditions.

A moving target trajectory prediction method based on neighborhood fitting

In order to solve the influence of time delay and data frame loss on the tracking accuracy of high mobility target, a moving target trajectory prediction method based on neighborhood fitting is proposed. The influence of neighborhood width, polynomial order and prediction steps on prediction accuracy are analyzed, and suggestions on parameter selection are given. Four typical moving target trajectories, such as parabola, climb line, spiral line and gyratory line are predicted. The experimental results show that the trajectory prediction method has high accuracy and good stability, and can meet the requirements of moving target tracking.

Ground Moving Target Detection and Trajectory Reconstruction Methods for Multichannel Airborne Circular SAR

Synthetic aperture radar ground moving target indication (SAR-GMTI) is an attractive mode for radar system to obtain high-resolution regional images and detection of moving targets simultaneously. Moreover, the 360° observation capability of circular SAR (CSAR) makes it possible for moving target trajectory reconstruction. In order to address the problem that slow-moving targets, which are buried in strong stationary ground clutter, are difficult to be detected, a framework of multichannel SAR-GMTI is presented in this article. For ground moving target detection in the presence of stationary clutter, the proposed method first adopts clutter suppression interferometry and relaxation-based cyclic algorithm to suppress clutterand retrieve parameters (e.g., radial velocity and Doppler information). Then, to further reduce the false alarm rate, multiple moving target tracking algorithm is performed in range–Doppler domain. Finally, the moving target trajectory is reconstructed by using the proposed two-stage parameter estimation method based on Doppler characteristic and CSAR geometry, and the mathematical analysis of this method is performed. The proposed method is robust and insensitive to the signal-to-noise ratio. It does not rely on priori road information, so it has wide applicability, especially in military application. In addition, the framework is performed in echo domain and independent of imaging processing. The experimental results on simulated data are presented to evaluate the estimation accuracy of the proposed method, and the real data processing results are provided to demonstrate the validity and feasibility of the proposed method.

(Retracted) Swimming pool safety detection device based on computer vision

Computer vision (CV) technology refers to the process of computer simulation of human vision and has the ability to perceive the environment and technology of the human visual function. In reality, CV is essentially the study of visual perception, a discipline that studies how to make computers see as humans do. Machine vision mainly uses computers to simulate human visual functions, extract information from images of objective things, process and understand them, and finally use them for actual detection, measurement, and control. With the frequent occurrence of safety accidents, especially in swimming, it is necessary to make adequate preparations to reduce the occurrence of accidents. Swimming is affected by many external factors. While completing swimming technical movements, trainees must overcome the stimulation of water on the body and the interference of breathing, which can lead to accidents. We aim to study the safety detection device of swimming pools based on CV and use the current popular CV technology to monitor the safety problems of swimming pools. We study the target motion trajectory detection system based on CV and use Zhang’s plane calibration method to locate the camera. This research is based on how the computer back-end server realizes abnormal behavior detection, which greatly saves hardware costs. The experimental data shows that water quality is most important issue in swimming safety, accounting for 29% of the population, which indicates that water quality is the focus of users’ choosing swimming as an activity. Of those surveyed, 19 people could not deal with the risks when swimming, accounting for 24% of the total group surveyed, which indicates that people’s swimming safety coping ability needs to be improved.

Data Mining Algorithm for Moving Object Trajectory

Aiming at the poor effect and long recognition time of data mining algorithm for moving target trajectory recognition, a data mining algorithm based on improved Hausdorff distance is proposed. The position and angle of abnormal trajectory data are detected by calculating the distance between trajectory classification and sub trajectory line segments, and the trajectory unit is established by using the improved Hausdorff distance algorithm to optimize the similarity matching structure. Experimental results show that the algorithm has low error pruning rate in identifying moving target trajectory, improves the detection efficiency of moving target trajectory recognition data, and ensures the quality of moving target trajectory recognition data mining

Dynamic simulation platform of infrared moving target trajectory reconstruction

Dynamic simulation platform of infrared moving target trajectory reconstruction

Automatic Manipulator Tracking Control Based on Moving Target Trajectory Prediction

The core issue of automatic manipulator tracking control is how to ensure the given moving target follows the expected trajectory and adapts to various uncertain factors. However, the existing moving target trajectory prediction methods rely on highly complex and accurate models, lacking the ability to generalize different automatic manipulator tracking scenarios. Therefore, this study tries to find a way to realize automatic manipulator tracking control based on moving target trajectory prediction. In particular, a moving target trajectory prediction model was established, and its parameters were optimized. Next, a tracking-training-testing algorithm was proposed for manipulator’s automatic moving target tracking, and the operating flows were detailed for training module, target detection module, and target tracking module. The proposed model and algorithm were proved effective through experiments.