A method for locating targets using range only

Target positioning and tracking in wireless sensor networks (WSNs) are necessary for real ap-plications. This paper uses an artificial fish swarm algorithm (AFSA) with receive signal strength indicator (RSSI) for indoor target positioning and tracking. The performance of random and fixed artificial fish deployment topologies in AFSA for target positioning and tracking is investigated when the number of artificial fish is 12, 24, 52, 72, and 100, respectively. The simulation results show that the difference in average error between the random and fixed deployments is 5%, and the average positioning time is 2% for target position. The target tracking performance of fixed deployment is better than that of random one when the number of artificial fish is small.

Image-free single-pixel detection, which has the advantages of low computing power requirements, a wide working waveband and high detection speed, provides a new paradigm for target tracking. A dedicated mask-based four-pixel parallel detection system is proposed for ultraviolet (UV) target positioning and tracking. The system consists of four parallel branches. Four masks with linearly increasing transmittance provide one-order geometric moment modulation of the scene simultaneously. The four branches are arranged in a diamond structure to ensure complementarity and symmetry in the x and y directions, respectively. A chrome mask and a photomultiplier tube module are selected to ensure the system performance in the UV waveband. Target positioning is achieved by centroid calculation with a geometric moment and a global system calibration method is proposed to ensure accuracy. The validity of the proposed system is demonstrated with tracking experiments using a UV target. This work provides a practical and low-cost solution for real-time UV target positioning and tracking.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

The on‐board target tracking of unmanned aerial vehicle (UAV) relies on on‐board computer to process the collected ground image information, and then use the target tracking algorithm to achieve the target tracking function, which provides the user with target position, trajectory and other information. UAVs are limited by load and power consumption. Due to the high resolution and frame rate of UAV's image collection system, traditional target tracking platforms cannot meet the requirements. As a result, the real‐time nature of the tracking platform urgently needs to be resolved. This study designs and implements the airborne ground target tracking accelerator for UAV. The kernelised correlation filters (KCFs) algorithm with low computational complexity and precision is selected to achieve the airborne ground target tracking function and its performance bottleneck is analysed. In order to improve the real‐time performance of the tracking task processing, the ZYNQ platform based on ARM and field programmable gate arrays is chosen to achieve the hardware acceleration of the KCF algorithm. The KCF algorithm is implemented in hardware using the high‐level synthesis technology, and a high‐speed data transmission link is established. Experiments show that the design can achieve a tracking rate of more than 30 FPS under 960 × 540 resolution, which meets the real‐time requirements of UAV target tracking task.

An improved maximum between-cluster variance (Otsu) method based on iteration was proposed for the purpose of realizing target recognition and target tracking effectively. Improved Otsu method was used to segment image collected by CCD, so as to extract the target from image and finish the target localization. By calculating centroid of the target in a single frame and extracting feature point of outline constituted by centroids of the single-frame target within unit interval, a polygon can be formed, the gravity center of which was taken as the target position. Filter model was established of which the control input is nonzero based on Kalman filter Principle, which was combined with target position at this moment can be used to predict the gravity center of the target at the next moment. The experiment and simulation results indicated that improved Otsu method can solve the problem that target and background are segmented mistakenly when image thresholding. Kalman filtering was able to predict target trajectory effectively after target being recognized. The requirement of target tracking was satisfied and target tracking was realized through this improved Otsu target tracking algorithm based on iteration.

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.

Moving target localization and tracking are of great interest for rescue, surveillance and security operations. The radar signal processing procedure providing the target position estimation consists of such phases of signal processing as raw radar data pre-processing, background subtraction, detection, time of arrival estimation, localization and tracking. In this paper, we will focus on the localization phase where we assume that for the target localization UWB radar equipped by one transmitting and N receiving antennas is used. Following this intention, the accuracy of the target localization provided by the least-squares method, the spherical-interpolation method and the Taylor series method applied within localization phase will be compared based on processing of radar signals obtained from real target tracking by UWB radar. The acquired results confirm that the Taylor series method can provide the best accuracy of the target positioning.

Human target detection and tracking have great potential in surveillance, rescue and security applications. Traditional human detection and tracking are performed on the range profile. If targets are close or overlapped in range, it is difficult to distinguish these targets. As velocity provides another aspect to distinguish targets, a novel framework is proposed for human tracking using both range and velocity information. The tracking framework consists of six steps, including clutter reduction, range–Doppler (RD) calculation, target detection, measurement estimation, target localisation and target tracking. Primary attention is devoted to the middle four steps. The calculation process of the RD image is described in detail. The ordered statistics constant false alarm rate detector is extended to a two‐dimensional scenario for the RD target detection. An efficient approach is given for automatic measurement estimation. A minimum root‐mean‐square error pruning algorithm is proposed for multi‐target localisation. As the algorithm combines both range and velocity information for measurement association, it clearly shows a lower wrong association probability than the method using range information only. The effectiveness of the proposed tracking framework is evaluated by the experimental data in the foliage‐penetration environment.

Multistatic active target tracking in GPS-denied scenarios is complicated by the fact that the emitter and receiver locations are unknown and must be estimated jointly with the target track. Maximum a posteriori algorithms for solving this joint estimation problem are complicated by the nonlinearities in the likelihood function of the bistatic range measurement. A new integral representation of this likelihood function is presented for small measurement error variances. Remarkably, target state appears linearly in this integral. This paper presents a new approach to the basic problem of target state estimation for known sensor locations. The optimal estimator derived from the integral representation is an iteratively re-weighted linear Kalman smoother. Joint estimators for target and emitter-receiver field tracking will be reported elsewhere.

Current image-guided prostate radiotherapy often relies on the use of implanted fiducial markers (FMs) or transducers for target localization. Fiducial or transducer insertion requires an invasive procedure that adds cost and risks for bleeding, infection and discomfort to some patients. We are developing a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine projection kV X-ray images without the need for daily cone-beam computed tomography (CBCT). A deep learning model was first trained by using several thousand annotated projection X-ray images. The trained model is capable of identifying the location of the prostate target for a given input X-ray projection image. To assess the accuracy of the approach, three patients with prostate cancer received volumetric modulated arc therapy (VMAT) were retrospectively studied. The results obtained by using the deep learning model and the actual position of the prostate were compared quantitatively. The deviations between the target positions obtained by the deep learning model and the corresponding annotations ranged from 1.66 mm to 2.77 mm for anterior-posterior (AP) direction, and from 1.15 mm to 2.88 mm for lateral direction. Target position provided by deep learning model for the kV images acquired using OBI is found to be consistent that derived from the implanted FMs. This study demonstrates, for the first time, that highly accurate markerless prostate localization based on deep learning is achievable. The strategy provides a clinically valuable solution to daily patient positioning and real-time target tracking for image-guided radiotherapy (IGRT) and interventions.

Video tracking uses the semantic information between video image sequences to process, analyze and study the target to achieve target tracking. In response to the problems of sharp changes in target position, large deformation, and occlusion due to similar background interference, changes in lighting conditions, and different shapes of targets in the target tracking process, the tracking algorithm is less accurate and less robust in target appearance. An improved target tracking algorithm based on multi-domain network (MDNet) is proposed to solve this problem. By adding the Image-Align layer to the video target tracking task, a more accurate target value is obtained; applying the Directed Acyclic Graph-Recurrent Neural Network (DAG-RNN) by combining it with a convolutional neural network and modeling image neighborhood context dependencies for the target region to be tracked, we improve the problem that conventional networks only perform multi-layer extraction for the appearance features of the target, thus resulting in poor robustness to changes in target appearance; ROI Align layer is added after the convolution layer to speed up target feature extraction.

Tracking human body is an important problem in computer vision field. Tracking failures caused by occlusion can lead to wrong rectification of the target position. In this paper, a robust human tracking algorithm is proposed to address the problem of occlusion, rotation and improve the tracking accuracy. It is based on Tracking-Learning-Detection framework. The key auxiliary information is used in the framework which motivated by the fact that a tracking target is usually embedded in the context that provides useful information. First, face localization method is utilized to find key face location information. Second, the relative position relationship is established between the auxiliary information and the target location. With the relevant model, the key face information will get the current target position when a target has disappeared. Thus, the target can be stably tracked even when it is partially or fully occluded. Experiments are conducted in various challenging videos. In conjunction with online update, the results demonstrate that the proposed method outperforms the traditional TLD algorithm, and it has a relatively better tracking performance than other state-of-the-art methods.

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.

For target tracking applications, small wireless sensors provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration amongst themselves to improve the target localization and tracking accuracies. Distributed data fusion architecture provides a collaborative tracking framework. Due to the energy constraints of these small, sensing and wireless communicating devices, a common trend is to put some of them into a dormant state. In addition to a selective sensor activation strategy based on the maximum mutual information metric, in this paper, we devise the Information-Controlled Transmission Power (ICTP) adjustment in order to improve the energy savings. The essence of the proposed ICTP scheme for collaborative target tracking lies behind the idea that the sensors with more information use higher transmission powers than the sensors with less information in order to share their target state information with the neighboring sensors.

Radiotherapy of moving targets, such as lung, liver or prostate tumors, is prone to treatment errors due to the uncertainties in the target position. Minimizing such error is critical for radiotherapy treatments, especially hypofractionated SBRT treatments. On‐board real time imaging verification and tracking of moving targets has become one of the most exciting areas in radiotherapy. The tremendous developments in this area enable us to improve targeting accuracy and reduce the healthy tissue toxicity, which paves the road to further margin reduction and dose escalation in conventionally fractionated or SBRT treatments.Over the past few years, several real‐time imaging techniques have been clinically implemented, including techniques using ionizing radiation, such as x‐ray based imaging, and radiation‐free techniques such as MRI, imaging based on implanted electromagnetic transponders and optical imaging. The x‐ray imaging techniques are widely accessible in clinics, although they have limited soft tissue contrast and contribute to patient exposure. Recently, MRI has gained wide interests in radiotherapy due to its better soft tissue contrast and no radiation dose. Imaging based on electromagnetic transponders and optical imaging have also gained popularity in many clinical scenarios due to their unique capabilities.Based on these imaging techniques, various tracking approaches have been developed. The room mounted orthogonal 2D x‐ray system (CyberKnife system) was the first medical device capable to perform real‐time target tracking by combining image guidance with robotically targeted radiation delivery. In the Vero system, tumor tracking is achieved by combining image‐guidance with a two‐dimensional pivot assembly of the linear accelerator. In gantry‐based linear accelerators, target motion can be tracked in real time by repositioning the MLC based on the signal from x‐ray images or electromagnetic transponders implanted.The rapid development of new imaging and tracking technologies raises new challenges in revolutionizing our treatment procedures. It becomes important to understand the functionality, limitations and clinical impact of each technique so that they can be chosen wisely for different clinical settings. The goal of this educational session is to compare and contrast different imaging and tracking techniques implemented in CyberKnife, Vero, MRI based, and gantry‐based radiotherapy machines. The general concepts of real‐time imaging and tracking, from imaging the target to adapting the treatment, will be reviewed for each modality. While discussing the technical aspects of the systems including the overall uncertainties and residual imaging and tracking errors, emphasis will be placed on the clinical implementation and workflow. The increased patient dose due to x‐ray imaging and quality assurance procedures will be described. Additionally, the impact of tracking on treatment volume and margin reduction will be discussed. The session will close with an outlook at future developments.Learning Objectives: Understand the opportunities and challenges of real time imaging and tumor tracking. Understand the advantages and limitations of different imaging and tracking techniques, as well as the clinical implementation of each technique. Understand the clinical impact of real time tumor tracking and future directions in this area. Supported by National Institutes of Health, Varian Medical System, Accuray Inc.