Predicted Target Position Research Articles

Ship detection method based on attention guidance and multi-sample decision making

Single-stage target detection methods have the characteristics of fast training speed and short detection time. However, its feature pyramid network is difficult to suppress the background and noise information of SAR ship images, and the detection head has prediction errors. To address this problem, this paper proposes a detection model based on attention guidance and multi-sample decision for synthetic aperture radar ship detection. Firstly, an attention guidance network is proposed and added to the highest level of the feature pyramid to suppress background and noise interference, thereby improving the representation ability of features. Secondly, a multi-sample decision network is proposed to participate in the prediction of target position. This network alleviates the impact of prediction errors on detection results by increasing the number of samples output in the regression branch. Finally, a novel maximum likelihood loss function is designed. This loss function constructs a maximum likelihood function using the samples output from the multi-sample decision network, which is used to standardize the training of the decision network and further improve the accuracy of target positioning. Taking the RetinaNet network model as the baseline method, compared with the baseline method and the current advanced target detection methods, this method shows the highest detection accuracy on the ship detection dataset SSDD, with AP reaching 52.8%. Compared with the baseline method, the proposed method improves the AP evaluation index 3.4% ∼ 5.7%, and the training parameter Params only increases by 2.03 M, and the frame rate FPS only decreases 0.5Iter/s.

Application of fuzzy logic control theory combined with target tracking algorithm in unmanned aerial vehicle target tracking

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

A lightweight target tracking algorithm based on online correction for meta-learning

The traditional Siamese network based object tracking algorithms suffer from high computational complexity, making them difficult to run on embedded devices. Moreover, when faced with long-term tracking tasks, their success rates significantly decline. To address these issues, we propose a lightweight long-term object tracking algorithm called Meta-Master-based Ghost Fast Tracking (MGTtracker),which based on meta-learning. This algorithm integrates the Ghost mechanism to create a lightweight backbone network called G-ResNet, which accurately extracts target features while operating quickly. We design a tiny adaptive weighted fusion feature pyramid network (TiFPN) to enhance feature information fusion and mitigate interference from similar objects. We introduce a lightweight region regression network, the Ghost Decouple Net (GDNet) for target position prediction. Finally, we propose a meta-learning-based online template correction mechanism called Meta-Master to overcome error accumulation in long-term tracking tasks and the difficulty of reacquiring targets after loss. We evaluate the algorithm on public datasets OTB100, VOT2020, VOT2018LT, and LaSOT and deploy it for performance testing on Jetson Xavier NX. Experimental results demonstrate the effectiveness and superiority of the algorithm. Compared to existing classic object tracking algorithms, our approach achieves a faster running speed of 25 FPS on NX, and real-time correction enhances the algorithm’s robustness. Although similar in accuracy and EAO metrics, our algorithm outperforms similar algorithms in speed and effectively addresses the issues of significant cumulative errors and easy target loss during tracking. Code is released at https://github.com/ygh96521/MGTtracker.git.

A novel external/internal tumor tracking approach to compensate for respiratory motion baseline drifts

Objective. Real-time respiratory tumor tracking as implemented in a robotic treatment unit is based on continuous optical measurement of the position of external markers and a correlation model between them and internal target positions, which are established with X-ray imaging of the tumor, or fiducials placed in or around the tumor. Correlation models are created with fifteen simultaneously measured external/internal marker position pairs divided over the respiratory cycle. Every 45–150 s, the correlation model is updated by replacing the three first acquired data pairs with three new pairs. Tracking simulations for >120.000 computer-generated respiratory tracks demonstrated that this tracking approach resulted in relevant inaccuracies in internal target position predictions, especially in case of presence of respiratory motion baseline drifts. Approach. To better cope with drifts, we introduced a novel correlation model with an explicit time dependence, and we proposed to replace the currently applied linear-motion tracking (LMT) by mixed-model tracking (MMT). In MMT, the linear correlation model is extended with an explicit time dependence in case of a detected baseline drift. MMT prediction accuracies were then established for the same >120.000 computer-generated patients as used for LMT. Main results. For 150 s update intervals, MMT outperformed LMT in internal target position prediction accuracy for 93.7 ∣ 97.2% of patients with 0.25 ∣ 0.5 mm min−1 linear respiratory motion baseline drifts with similar numbers of X-ray images and similar treatment times. For the upper 25% of patients, mean 3D internal target position prediction errors reduced by 0.7 ∣ 1.8 mm, while near maximum reductions (upper 10% of patients) were 0.9 ∣ 2.0 mm. Significance. For equal numbers of acquired X-ray images, MMT greatly improved tracking accuracy compared to LMT, especially in the presence of baseline drifts. Even with almost 50% less acquired X-ray images, MMT still outperformed LMT in internal target position prediction accuracy.

Autonomous landing algorithm of UAV based on Target tracking

The target tracking technology in the UAV autonomous landing process is studied. Based on the analysis of the occlusion, background change, and variable scale problems in the target tracking process, a scale adaptive method for UAV target tracking based on KCF (Kernelized Correlation Filter) algorithm is proposed. The original KCF target tracking framework is adopted to achieve the target position prediction function. Secondly, to address the variable scale problem of moving targets and the target loss problem caused by scale changes, the scale filter of the DSST target tracking algorithm is used. The algorithm is for estimating the scale of moving targets and generating a small-scale filter estimate to improve the tracking efficiency of the UAV for moving targets and reduce the target loss rate. The final test on the OTB dataset showed a 4% improvement in variable scale accuracy and a 2.9% improvement in variable scale success rate. A Gazebo 3D environment scene was set up under the Linux system, and UAV autonomous landing simulation experiments were conducted to verify the improved algorithm’s real-time effectiveness in the UAV autonomous landing application. The simulation results show that the improved KCF target tracking algorithm has real-time performance in the UAV autonomous landing trajectory tracking process.

An IPDA based target existence assisted Bayesian detector for target tracking in clutter

In this paper, an integrated probabilistic data association (IPDA) based target existence assisted Bayesian detector (TE-BD) is derived for target tracking in the clutter. The key idea of the proposed method is to feed back the predicted target position information and the existence probability from the tracker to the detector to facilitate target detection and tracking, while ensuring radar can terminate track quickly when target disappears. We first derive the prior information assisted target detection model, and then present the general steps of the TE-BD scheme based on the IPDA framework. Simulation results verify the effectiveness of the proposed IPDA-TE-BD w.r.t. the existing works.

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy

BackgroundIn infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient’s body surface using a prediction model. In this work, we developed two artificial intelligence (AI)-driven prediction models to improve RTTT radiotherapy, namely, a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS) model. The models aim to improve the accuracy in predicting three-dimensional tumor motion.MethodsFrom patients whose respiration-induced motion of the tumor, indicated by the fiducial markers, exceeded 8 mm, 1079 logfiles of IR marker-based hybrid RTTT (IR Tracking) with the gimbal-head radiotherapy system were acquired and randomly divided into two datasets. All the included patients were breathing freely with more than four external IR markers. The historical dataset for the CNN model contained 1003 logfiles, while the remaining 76 logfiles complemented the evaluation dataset. The logfiles recorded the external IR marker positions at a frequency of 60 Hz and fiducial markers as surrogates for the detected target positions every 80–640 ms for 20–40 s. For each logfile in the evaluation dataset, the prediction models were trained based on the data in the first three quarters of the recording period. In the last quarter, the performance of the patient-specific prediction models was tested and evaluated. The overall performance of the AI-driven prediction models was ranked by the percentage of predicted target position within 2 mm of the detected target position. Moreover, the performance of the AI-driven models was compared to a regression prediction model currently implemented in gimbal-head radiotherapy systems.ResultsThe percentage of the predicted target position within 2 mm of the detected target position was 95.1%, 92.6% and 85.6% for the CNN, ANFIS, and regression model, respectively. In the evaluation dataset, the CNN, ANFIS, and regression model performed best in 43, 28 and 5 logfiles, respectively.ConclusionsThe proposed AI-driven prediction models outperformed the regression prediction model, and the overall performance of the CNN model was slightly better than that of the ANFIS model on the evaluation dataset.

Следящая фильтрация показаний радиолокационной станции при обнаружении БПЛА

Unsanctioned intrusion of unmanned aerial vehicle (UAV) on the territory of the guarded object is primarily detected by specialized radio surveillance systems. The results obtained by radio surveillance systems are used for aiming of UAV visual identification and radio jamming systems. In this work, the problems of UAV detection and tracking of the target trajectory are considered. The known tracking filter systems for radio surveillance application were analyzed and a specialized matrix tracking filter system was proposed, which uses in its algorithm a dynamically changing energy potential of the radio surveillance system. The developed tracking filter system efficiency is evaluated using methods of matrix calculation, mathematical modeling, and probability theory. It has been established that the developed tracking filter system lets the radio surveillance equipment most effectively initiate trajectories of UAV, set its movement window, consider radio surveillance equipment characteristics, and approximate the trajectory of UAV at times of missed detections connected to radar cross-section fluctuations of moving targets. A high efficiency of the developed system has been achieved by decreasing the inaccuracy of the target position prediction two times in comparison with the known tracking filter systems. The obtained results allow easy scaling of the developed tracking filter system for its application as a part of any radio surveillance system.

Comparison of modeling accuracy between Radixact® and CyberKnife® Synchrony® respiratory tracking system

Synchrony Respiratory Tracking system adapted from CyberKnife has been introduced in Radixact to compensate the tumor motion caused by respiration. This study aims to compare the modeling accuracy of the Synchrony system between Radixact and CyberKnife. Two Synchrony plans based on fiducial phantoms were created for CyberKnife and Radixact, respectively. Different respiratory motion traces were used to drive a motion platform to move along the superoinferior and left-right direction. The cycle time and the amplitude of target/surrogate motion of one selected motion trace were scaled to investigate the dependence of modeling accuracy on the motion characteristic. The predicted target position, the correlation error, potential difference (Radixact only) and standard error (CyberKnife only) were extracted from raw data or log files of the two systems. The modeling accuracy was evaluated by calculating the root-mean-square (RMS) error between the predicted target positions and the input motion trace. A threshold T95 within which 95% of the potential difference or the standard error lay was defined and evaluated. Except for the motion trace with a small amplitude and a good (linear) correlation between target and surrogate motion, Radixact showed smaller RMS errors than CyberKnife. The RMS error of both systems increased with the motion amplitude and showed a decreasing trend with the increasing cycle time. No correlation was found between the RMS error and the amplitude of surrogate motion. T95 could be a good estimator of modeling accuracy for CyberKnife rather than Radixact. The correlation error defined in Radixact were largely affected by the number of fiducial markers and the setup error. In general, the modeling accuracy of the Radixact Synchrony system is better than that of the CyberKnife Synchrony system under unfavorable conditions.

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy.

The purpose of this work is to show the usefulness of a prediction method of tumor location based on partial least squares regression (PLSR) using multiple fiducial markers. The trajectory data of respiratory motion of four internal fiducial markers inserted in lungs were used for the analysis. The position of one of the four markers was assumed to be the tumor position and was predicted by other three fiducial markers. Regression coefficients for prediction of the position of the tumor-assumed marker from the fiducial markers’ positions is derived by PLSR. The tracking error and the gating error were evaluated assuming two possible variations. First, the variation of the position definition of the tumor and the markers on treatment planning computed tomograhy (CT) images. Second, the intra-fractional anatomical variation which leads the distance change between the tumor and markers during the course of treatment. For comparison, rigid predictions and ordinally multiple linear regression (MLR) predictions were also evaluated. The tracking and gating errors of PLSR prediction were smaller than those of other prediction methods. Ninety-fifth percentile of tracking/gating error in all trials were 3.7/4.1 mm, respectively in PLSR prediction for superior–inferior direction. The results suggested that PLSR prediction was robust to variations, and clinically applicable accuracy could be achievable for targeting tumors.

A Real-Time Structured Output Tracker with Scale Adaption for Visual Target Tracking

The structured output tracking algorithm is a visual target tracking algorithm with excellent comprehensive performance in recent years. However, the algorithm classifier will produce error information and result in target loss or tracking failure when the target is occluded or the scale changes in the process of tracking. In this work, a real-time structured output tracker with scale adaption is proposed: (1) the target position prediction is added in the process of target tracking to improve the real-time tracking performance; (2) the adaptive scheme of target scale discrimination is proposed in the structured support to improve the overall tracking accuracy; and (3) the Kalman filter is used to solve the occlusion problem of continuous tracking. Extensive evaluations on the OTB-2015 benchmark dataset with 100 sequences have shown that the proposed tracking algorithm can run at a highly efficient speed of 84 fps and perform favorably against other tracking algorithms.

Improved Hierarchical Convolutional Features for Robust Visual Object Tracking

The target and background will change continuously in the long-term tracking process, which brings great challenges to the accurate prediction of targets. The correlation filter algorithm based on manual features is difficult to meet the actual needs due to its limited feature representation ability. Thus, to improve the tracking performance and robustness, an improved hierarchical convolutional features model is proposed into a correlation filter framework for visual object tracking. First, the objective function is designed by lasso regression modeling, and a sparse, time-series low-rank filter is learned to increase the interpretability of the model. Second, the features of the last layer and the second pool layer of the convolutional neural network are extracted to realize the target position prediction from coarse to fine. In addition, using the filters learned from the first frame and the current frame to calculate the response maps, respectively, the target position is obtained by finding the maximum response value in the response map. The filter model is updated only when these two maximum responses meet the threshold condition. The proposed tracker is evaluated by simulation analysis on TC-128/OTB2015 benchmarks including more than 100 video sequences. Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.829 and 0.695, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes.

Multi-scale Correlation Filter Tracking Algorithm Based on Feature Fusion

For the Kernel Correlation Filter (KCF) algorithm using a single feature can not fully describe the tracking target, and is susceptible to the change of the target scale, lacking the estimation of the target scale size, an improved feature fusion and multi-scale correlation filter tracking algorithm based on KCF is proposed. Firstly, in the stage of location prediction, color features and directional gradient histogram hog features are fused by feature series, and feature dimensions are added to realize target location prediction. Then the scale filter is introduced into the predicted target position to estimate the optimal target scale as the tracking result. Experimental results show that the accuracy and success rate of the improved algorithm are significantly improved compared with other classical tracking algorithms, and it can deal with scale change, occlusion, deformation and other complex situations robustly.

Maneuver Strategy Generation of UCAV for within Visual Range Air Combat Based on Multi-Agent Reinforcement Learning and Target Position Prediction

With the development of unmanned combat air vehicles (UCAVs) and artificial intelligence (AI), within visual range (WVR) air combat confrontations utilizing intelligent UCAVs are expected to be widely used in future air combats. As controlling highly dynamic and uncertain WVR air combats from the ground stations of the UCAV is not feasible, it is necessary to develop an algorithm that can generate highly intelligent air combat strategies in order to enable UCAV to independently complete air combat missions. In this paper, a 1-vs.-1 WVR air combat strategy generation algorithm is proposed using the multi-agent deep deterministic policy gradient (MADDPG). A 1-vs.-1 WVR air combat is modeled as a two-player zero-sum Markov game (ZSMG). A method for predicting the position of the target is introduced into the model in order to enable the UCAV to predict the target’s actions and position. Moreover, to ensure that the UCAV is not limited by the constraints of the basic fighter maneuver (BFM) library, the action space is considered to be a continuous one. At the same time, a potential-based reward shaping method is proposed in order to improve the efficiency of the air combat strategy generation algorithm. Finally, the efficiency of the air combat strategy generation algorithm and the intelligence level of the resulting strategy is verified through simulation experiments. The results show that an air combat strategy using target position prediction is superior to the one that does not use target position prediction.

Realization of the Airborne System for Moving Target Detection and Tracking Based on Quadrotor

In this paper, taking quadrotor as the research object, a kind of airborne vision system is presented to detect and track the moving target. Lower cost and better performance are the remarkable characteristics of the system. Firstly, the overall scheme of the detection and tracking system is designed. Secondly, a new image detection algorithm based on HSV color model and template matching is proposed, then according to image principle, the relative distance from quadrotor to target is calculated. In order to solve the time delay caused by image processing, the target position prediction is made by adding Kalman filter. Then, according to the position information of quadrotor and target, a segmented PID controller is designed to improve the tracking effect. Finally, the algorithm is applied to the actual flight of the quadrotor to verify its feasibility.

Integrated Guidance and Control for the Fixed-trim Vehicle against the Maneuvering Target

From the view of the fixed-trim reentry vehicle (FTRV) application, the integrated guidance and control (IGC) design is a key challenge. In order to improve the performance of the FTRV against the maneuvering target, an IGC system combining with the virtual target and the filter is proposed in this paper. To investigate the dynamics of the FTRV against the maneuvering target, a 7-DOF mathematical model is established and the error angle is employed to describe the relative motion between the FTRV and the target. Considering the controllability of the FTRV, a nonlinear differentiable error angle command is presented and its influence on the terminal velocity is discussed. To deal with the contradiction between the limited maneuverability and the demand of high guidance accuracy, the actual target is taken place with the virtual target, of which acceleration is estimated by the ESO. For improving the performance of the virtual target position prediction, a rolling updating strategy is proposed and derived analytically. Then, the back-stepping based IGC system is designed with the virtual target and the filter. The finite-time convergence of the IGC system is proved via the Lyapunov stability theorem. The numerical simulation results show the effectiveness of the proposed IGC system for the FTRV against the maneuvering target with time-varying acceleration.

POSE.3C: Prediction-Based Opportunistic Sensing Using Distributed Classification, Clustering, and Control in Heterogeneous Sensor Networks

This paper presents a distributed algorithm, called prediction-based opportunistic sensing using distributed classification, clustering, and control (POSE.3C), for self adaptation of sensor networks for energy management. The underlying 3C network autonomy concept enables utilization of the target classification information to form dynamic clusters around the predicted target position via selection of sensor nodes with the highest energies and maximum geometric diversity. Furthermore, the nodes can probabilistically control their heterogeneous devices to track targets of interest and minimize energy consumption in a completely distributed manner. Theoretical properties of the POSE.3C network are established and derived in terms of the network lifetime and missed detection characteristics. The algorithm is validated through extensive simulations that demonstrate a significant increase in the network lifetime as compared to other network control approaches, while providing high tracking accuracy and low missed detection rates.

A Pseudo-Spectrum Approach for Weak Target Detection and Tracking

Conventional velocity-filtering-based track-before-detect (VF-TBD) methods integrate the energy of a cell in a frame with that of the cell closest to the predicted target position in the last frame of the processing batch, assuming a certain target velocity. However, the target may not exactly be on the quantized cell and its echo envelope may occupy multiple adjacent cells. This often leads to significant energy loss and echo envelope degradation. In this paper, a novel VF-TBD method based on pseudo-spectrum (PS-VF-TBD) is presented to address this problem. For every cell, a pseudo-spectrum is constructed around the predicted position according to the assumed velocity using a truncated point spread function. Samples of the pseudo-spectrum on the cells that are located in the truncated spread area are added onto the last frame of the processing batch to integrate the target energy within multiple cells. Due to the use of the point spread model and the accurate sampling of the predicted spectrum, energy loss can be mitigated and the echo envelope is well maintained. This approach simultaneously maximizes the signal-to-noise ratio (SNR) gain and enables improved parameter estimation utilizing the envelope characteristics. The procedure for pseudo-spectrum construction and multiframe accumulation is derived in detail and the output SNR is analyzed theoretically. It is found that the proposed PS-VF-TBD can achieve an SNR gain greater than that by the conventional VF-TBD method. To deal with a target with unknown velocity, a bank of pseudo-spectrum-based velocity filters is proposed. The signal gain loss resulting from velocity mismatch is investigated and the $\mu$ -width of the envelope in the velocity domain is analyzed. Finally, a method for improved position and velocity estimation is presented. Simulation results demonstrate the superiority of the proposed method in terms of SNR gain, detection probability, and estimation accuracy at the expense of increased computational complexity.

Link distance-support vector regression (LD-SVR) based device free localization technique in indoor environment

Indoor localization using device free localization (DFL) in wireless sensor networks is gaining momentum nowadays due to the potential benefits of DFL. The techniques used in DFL can be broadly classified as statistical methods, compressive sensing, machine learning, radio tomographic method etc. Whenever loss factor and noise involved in the setup is unpredictable, techniques based on machine learning for target detection improves the result to a greater extend. The adaptability nature of support vector machines eased our choice of machine learning algorithm and support vector machine regression (SVR) is the proposed machine learning approach to address prediction of target position. Proposed link distance-support vector machine (LD-SVR) model uses link distance based DFL of single and multiple targets in indoor environment. Performance of the proposed model using SVR is analysed using parameters mean error and probability distribution function of mean error for various number of nodes and targets by imparting measurement error. The simulation results are found to be very much promising in a 3D room environment. The maximum value of mean error due to measurement error effect on link distance is less than 1 m.

Occluded Target Tracking Method Based on Multi-feature JPDA

Aiming at the problem of tracking instability or misalignment when the target is occluded or disturbed by similar targets, this paper proposes an improved occluded target tracking method based on JPDA (Joint Probability Data Association) based on the analysis of JPDA algorithm which is prone to misalignment when the target is occluded. The algorithm judges by adding multi-dimensional attributes (area, gray level, location, etc.). Accurate correlation of inter-frame targets is achieved. the accuracy of occluded target position prediction is improved to enhance the anti-occlusion ability. The measurement image sequence is used to simulate at finally. The simulation results show that the accuracy of occluded target location extraction is significantly improved by using the improved multi-feature based algorithm JPDA, which can meet the target tracking requirements in most occluded scenarios.