A Multi-Objective Scheme for Collision Avoidance, Swarm Cohesion, and Target Tracking for Smart UAVs

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.

Sea-IoUTracker: A more stable and reliable maritime target tracking scheme for unmanned vessel platforms

This paper presents a strategy of sustainable target tracking by unmanned aerial vehicle (UAV) when the target lost in the environment with uncertain shelters. The strategy includes target tracking method, target searching method, and switch tactics between them. In the target tracking stage, the method of reference point guidance (RPG) is used to standoff target tracking. To continue the tracking when the target is lost, the target state is predicted by the target model with the former target information. To search the lost target in the region which is calculated by the target lost duration time and speed, the UAV flies around the lost point with desired distance to search the lost target by the method of shrinking annulus. The switch tactics between the target tracking stage and the target searching stage is provided. Simulations demonstrate that the strategy for sustainable target tracking is feasible in uncertain shelters environment.

Limited resource allocation and scheduling are important problems in distributed wireless sensor networks (WSNs). Saving energy and real-time performance deserve research for low-cost sensor nodes in the target tracking application. In this paper, we propose a new dynamic-group idea and apply it to our scheduling scheme. An Extended Kalman Filter (EKF) is employed to predict tracking accuracy, and an energy model is adopted to predict the energy consumption. Simulation results show that, compared with the information-driven sensor quering (IDSQ) and the adaptive sensor scheduling strategy, the proposed scheme can achieve significant improvement on real-time property and tracking accuracy for target tracking in WSN. At the same time, it approaches to the adaptive sensor scheduling strategy in energy consumption.

This paper proposes a light-weight sensing node selection scheme, which incorporates both information utility measure and energy consumption measure for distributive target tracking in wireless sensor networks. By adopting this scheme, the target tracking algorithm could maintain lower energy consumption while still achieve desirable tracking accuracy. Simulation results are provided in this paper.

Improving tracking quality and extending network lifespan are two main objects for target tracking, which are usually contradictory due to limited energy of sensor nodes in wireless sensor networks. This article incorporates this contradiction into a problem of multi-objective optimization in tracking networks where multiple sensor nodes are scheduled for collaborative target tracking by adopting the unscented Kalman filter algorithm. We propose an effective scheme to extend the lifespan of wireless sensor networks while guaranteeing preset tracking quality. More specifically, with regard to practical circumstances, we perform analysis on the target detection probability, as well as residual energy of sensor nodes, when selecting a suitable set of candidate sensor nodes. Then, we put forward a novel energy-balanced sensor nodes scheduling algorithm, Greedy Balance Replace Heuristic Algorithm, to select a near-optimal task sensor set from the candidate sensor node set to balance tracking quality and network lifetime. In addition, we also design an efficient multi-sensor node collaborative method to track a single target and to timely report its state to the remote end. From simulation results, it is demonstrated that the proposed node scheduling scheme can not only maintain the preset tracking accuracy but also extend network lifespan with a low computation complexity.

In this paper, a modern packaging model is constructed by combining three-dimensional digital image processing technology, which has achieved the effect of improving the quality of modern packaging design. In addition, this paper proposes a new target recognition and tracking scheme, which uses the method of servo motor to control the synchronous conveyor belt to monitor the moving objects in real time and proposes a decision analysis algorithm to avoid repeated identification of multiple moving targets. At the same time, the software structure and execution flow of the visual control system are designed, the level of each functional module of the software is divided, and the key technology of the software is studied. The experimental research shows that the 3D digital image processing technology is effective in improving modern packaging design methods and can improve the interactivity and user experience of modern packaging design.

Mobile Wireless Sensor Networks (MWSN) are becoming increasingly common these days and are hugely used in different applications like remotely monitoring health of patients, wild life tracking, localization and target tracking. In all these applications, mobile sensor nodes associate and communicate with different fixed coordinators. Reliable data transmission, maintaining an acceptable level of resiliency, providing seamless handover, effective target tracking and low energy consumption are few of the key requirements of effective communication in MWSNs. Out of these, the work done in this paper focuses on target tracking and the tangible need to have a seamless as well as fast handover in MWSNs. A few studies have been conducted to evaluate the performance of the IEEE 802.15.4 Media Access Control (MAC) layer in MWSNs. Moreover, the paper proposes three different target tracking schemes as part of the handover activity of sensor nodes to demonstrate the feasibility of experiencing seamless handover procedure. The schemes are properly validated through simulation studies.

Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target’s position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms.

In this paper, same target is being sensed by multiple sensors and the main objective is to classify the information into set of data produced for the same target. Once tracks are initialized and confirmed, the number of targets can be estimated; the future predicted position and target velocity can be computed for each track. Fusion is necessary to integrate the data from different sensors and to extract the relevant information of the targets. Support Vector Machines (SVMs) are generally binary classifiers and the multi class problems are solved by combining more than one SVM. This paper proposes a novel scheme for multiple target tracking using SVM classifier. The proposed scheme achieves classification by finding the optimal classification hyperplane with maximal margin. Also Kalman Filter (KF) and 1 Backscan Multiple Hypothesis Tracking (1 BMHT) are used for filtering and association respectively.

Time and fuel balance is an important topic in the dynamic tracking problem of unmanned systems. In this paper, we propose a quasi time-fuel optimal control strategy (QTFOC) to solve the dynamic tracking problem of a double integrator system, which is capable of multi-target switching tracking tasks, such as the multi-target strike of weapons and the rapid multi-target grabbing of robots on industrial assembly lines. Compared with the previous optimal control algorithms, the proposed controller retains the time-fuel optimal characteristic when switching among multiple dynamic targets, and overcomes the high-frequency oscillation problem by incorporating local linear control region and two nonlinear buffer areas. That is, when switching among multiple dynamic targets, the proposed control strategy enables the corresponding system to achieve desired transient performance and satisfactory steady-state performance simultaneously. In addition, the asymmetry of friction load is further explored, which affects the dynamic performance of the actual system. Extensive experiments based on the visual tracking turntable demonstrate the superiority and feasibility of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the problem of multi-target switching tracking, such as the multi-target strike of weapons with vision sensors and the rapid multi-target grabbing of robots on industrial assembly lines. In recent years, various algorithms have been developed for trajectory planning and tracking. However, it is still challenging for unmanned servo system to capture dynamic targets quickly due to the constrains of motion performance, energy, and computational power. In order to reduce the computational burden and obtain ideal response under various physical constraints, a quasi time-fuel optimal control strategy (QTFOC) with analytical solutions is proposed in this paper. First, the static target is extended to the dynamic target by further investigating the traditional time-fuel optimal control theory for double integrator system. Second, to deal with the oscillation problem caused by system disturbances, we incorporate buffer areas and local linear control region into the control algorithm, which makes the system more robust. Third, the system performance is further improved by analyzing the frictional load asymmetry. This algorithm requires small computational resources and can be implemented directly on microcontrollers such as STM32. The experimental results demonstrate the superiority of our proposed method in handling the multi-target switching tracking problem, which can also adjust the response speed weight, making the unmanned system perform better under different operating conditions. Furthermore, the proposed QTFOC can be extended to other unmanned systems, such as trajectory planning and motion control for unmanned vehicles and bionic robots.

In this paper, a joint revisit and dwell time management (JRDTM) strategy for single target tracking based on the predicted Bayesian Cramer-Rao lower bound (BCRLB) in phased array radar system is addressed. We achieve the time resources management by formulating and solving an optimization problem, which is to minimize the resource amount used for tracking with the tracking accuracy of the target meeting a predesigned threshold. As the BCRLB provides a lower bound on the estimated mean square error (MSE) of target state, the predicted BCRLB model with time resources is derived and employed as the metric for the tracking performance. We put forward a converted algorithm to settle the optimization problem subsequently. Simulation results demonstrate that the proposed joint management strategy can help to achieve the tracking performance with less resource consumed.

Tracking moving targets in complex scenes using an active video camera is a challenging task. Tracking accuracy and efficiency are two key yet generally incompatible aspects of a Target Tracking System (TTS). A compromise scheme will be studied in this paper. A fast mean-shift-based Target Tracking scheme is designed and realized, which is robust to partial occlusion and changes in object appearance. The physical simulation shows that the image signal processing speed is >50 frame/s.

In this study, we designed an autonomous mobile robot based on the rules of the Federation of International Robotsoccer Association (FIRA) RoboSot category, integrating the techniques of computer vision, real-time image processing, dynamic target tracking, wireless communication, self-localization, motion control, path planning, and control strategy to achieve the contest goal. The self-localization scheme of the mobile robot is based on the algorithms featured in the images from its omni-directional vision system. In previous works, we used the image colors of the field goals as reference points, combining either dual-circle or trilateration positioning of the reference points to achieve selflocalization of the autonomous mobile robot. However, because the image of the game field is easily affected by ambient light, positioning systems exclusively based on color model algorithms cause errors. To reduce environmental effects and achieve the self-localization of the robot, the proposed algorithm is applied in assessing the corners of field lines by using an omni-directional vision system. Particularly in the mid-size league of the RobotCup soccer competition, selflocalization algorithms based on extracting white lines from the soccer field have become increasingly popular. Moreover, white lines are less influenced by light than are the color model of the goals. Therefore, we propose an algorithm that transforms the omni-directional image into an unwrapped transformed image, enhancing the extraction features. The process is described as follows: First, radical scan-lines were used to process omni-directional images, reducing the computational load and improving system efficiency. The lines were radically arranged around the center of the omni-directional camera image, resulting in a shorter computational time compared with the traditional Cartesian coordinate system. However, the omni-directional image is a distorted image, which makes it difficult to recognize the position of the robot. Therefore, image transformation was required to implement self-localization. Second, we used an approach to transform the omni-directional images into panoramic images. Hence, the distortion of the white line can be fixed through the transformation. The interest points that form the corners of the landmark were then located using the features from accelerated segment test (FAST) algorithm. In this algorithm, a circle of sixteen pixels surrounding the corner candidate is considered and is a high-speed feature detector in real-time frame rate applications. Finally, the dual-circle, trilateration, and cross-ratio projection algorithms were implemented in choosing the corners obtained from the FAST algorithm and localizing the position of the robot. The results demonstrate that the proposed algorithm is accurate, exhibiting a 2-cm position error in the soccer field measuring 600 cm2 x 400 cm2.

In this paper, a joint detection and tracking (JDT) scheme is presented for single target tracking in clutter using a moving radar platform. The core of the JDT scheme is to adaptively modify the detection threshold according to the feedback information from the tracker to the detector, while guaranteeing the averaged false alarm rate in the validation gate is a constant. The feedback information we utilized is the target's predicted location distribution, which can be achieved through jointly estimating the state of platform and target. Simulation results suggest that the JDT scheme can provide improved detection and tracking performance significantly, when compared with the standard probabilistic data association method.