Target Tracking Problem Research Articles

Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

AbstractThis paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift‐induced drag is incorporated into a new manoeuvring model to describe the reentry target dynamics more explicitly. This model can account for the constraint between lift and drag, which is beneficial in ensuring the reliability of target state estimation. Noticing that the lift‐induced drag is an inherent characteristics of a reentry target that distinguishes the target's identity from others belonging to the same class, the integrated target tracking and identification problem is formulated within the framework of the multiple hypothesis testing about a set of manoeuvring models constructed by different prior knowledge. The proposed approach enables the authors to derive the optimal solution to the given problem in a mathematically rigorous manner. To cope with the real‐time implementation issue, a hypothesis merging strategy is also devised in view of maintaining the target identification performance. Simulation results demonstrate that the proposed scheme provides superior performance and reliability both in target tracking and identification compared to the existing method, despite imperfectness of prior knowledge.

Tracking a Maneuvering Object by Indirect Observations with Random Delays

A mathematical model for the target tracking problem is proposed. The model makes it possible to describe conditions when the time for an observer to receive the results of indirect observations of a moving object depends not only on the state of the observation environment but also on the state of the object itself. The source of such a model is the observation process, by stationary means, of an autonomous underwater vehicle, in which the time for obtaining up-to-date data depends on the unknown distance between the object and the observer. As part of the study of the problem, the equations of the optimal Bayesian filter are obtained. But this filter is not possible to implement. For practical purposes, it is proposed to use the conditionally minimax nonlinear filter, which has shown promising results in other complex tracking models. The conditions for the filter’s evaluation and its accuracy characteristics are given. A large-scale numerical experiment illustrating the filter’s operation and the observation system’s features with random delays are described.

Multirobot Fully Distributed Active Joint Localization and Target Tracking

In this article, we study the problem of multirobot active joint localization and target tracking (AJLATT), where a team of robots mounted with sensors of limited field of view actively estimate their own and the target’s states cooperatively. Each robot designs its motion strategy to gain better estimation performance while avoiding collisions by using only the information from itself and its one-hop communicating neighbors. By leveraging the framework of joint localization and target tracking (JLATT) presented in our previous work, we propose two fully distributed algorithms that help each robot design motion strategies to achieve better localization and target tracking performance. These two algorithms are designed from, respectively, the control and optimization perspectives. The control-based algorithm is designed by incorporating the estimated target’s and robots’ states and their uncertainties as well as collision avoidance in the control policy. The optimization-based algorithm minimizes an objective function involving both the target’s and robots’ estimation uncertainties and a potential function that helps each robot avoid collision and maintain communication connectivity when the robot is planning its motion. Monte Carlo simulations demonstrate our algorithms’ feasibility to solve the AJLATT problem, and performance comparison between these two algorithms is given.

An online method for robust target tracking using a wireless sensor network

This work addresses the target tracking problem using a wireless sensor network (WSN). A set of sensors is randomly deployed to continuously monitor the moving targets, with their spatial trajectories already known but with estimated temporal trajectories and thus, possible delays and advances. The network has to transfer the data collected by the sensors to a base station, with hop-communication between the sensors. Previous works focused on the computation of a robust activation schedule which is passively tracking the targets (i.e., with no reaction to the actual behavior of the targets) by just activating sensors according to the pre-computed schedule. It has a maximized stability radius which is the guarantee on the maximal delay or advance of the targets it can cover, however it loses some targets whenever a delay or advance is beyond the value of its stability radius. Consequently, it is observed in many instances that a target is lost despite being in the range of a fully functioning WSN. A major novelty of the present work is to propose an online method that actively reacts to the delays and advances of the targets to extend the coverage offered by the robust schedule. The proposed method both detects the delays and advances beyond the stability radius, and constantly modifies the schedule to cover the targets as long as possible, at a computational cost that remains acceptable in a real-life application. It lets the WSN monitor the targets despite delays and advances that may not be covered by the guarantee of the stability radius. The second major novelty of our work is the introduction of the dynamic stability radius; it is a guarantee on the greatest delay or advance that can be covered by an online scheduling method. It is, by definition, greater than or equal to the classical stability radius but it is dynamically recomputed such that this guarantee increases over time. Our online method is dynamically optimizing the dynamic stability radius, meaning that each online decision is made so that the WSN has increasing capacities to cover the targets over time. Numerical experiments show that the online method proposed in this work offers more robustness than the previous works, with a dynamic stability radius that improves significantly over the stability radius from previous works, and that increases over time. Experiments also highlight the possibility to use such a method in a real-time context because of its fast computation time.

Distributed State Estimation Over Wireless Sensor Networks With Energy Harvesting Sensors.

This article is concerned with the distributed state estimation problem over wireless sensor networks (WSNs), where each smart sensor is capable of harvesting energy from the external environment with a certain probability. The data transmission between neighboring nodes is dependent on the energy level of each sensor, and the internode communication is deemed as a failure when the current energy level is inadequate to guarantee the normal data transmission. Considering the intermittent information exchange over WSNs, a novel distributed state estimator is first constructed via introducing a set of indicator functions, and then the evolution of the probability distribution of energy level and its steady-state distribution is systematically discussed by resorting to the eigenvalue analysis approach and the mathematical induction. Furthermore, the optimal estimator gain is derived by minimizing the trace of the estimation error covariance under known communication sequences. In addition, the convergence of the minimized upper bound of the expected estimation error covariance is analyzed under any initial condition. Finally, an illustrative example regarding the target tracking problem is provided to verify the validity of the obtained theoretical results.

Event-triggered distributed cooperative extended Kalman filter based on formation estimation

The existing distributed filters focus on the consensus estimation of multi-sensor information, but ignore the cooperative characteristics of formation targets, resulting in information loss and limited estimation accuracy. To address the problem of formation target tracking, a novel event-triggered distributed cooperative extended Kalman filter based on formation estimation is proposed, which improves the estimation performance without additional information by mining formation characteristics. Different from previous works, the formation estimation based on the best-matched model is developed to establish the connection between different targets, by which the unknown formation evolution can be modeled in the sense of minimum innovation. Considering the communication topology and the formation detection, an event-triggered mechanism is constructed to detect formation status reliably and reduce the communication burden. Afterwards, a novel distributed filter is designed by employing the multi-agent consensus theory. The cooperative term can be considered as a pseudo measurement, which forces local estimations to reach a consensus. By virtue of the variance-constrained approach, the filter gain is derived by minimizing the upper bound of the estimation error covariance. The proposed filter can also realize optimal estimation in the absence of cooperation and the multi-sensor fusion function for a single target. Moreover, the exponential stochastic boundness of the proposed filter is analyzed based on the stochastic stability theory. Finally, numerical experiments are carried out in three different simulation scenarios, and simulation results show that estimation errors of the proposed filter are significantly decreased compared to existing filters. The advantages of the proposed filter become more apparent as the number of formation members increases or the information quality of formation members improves.

Group target tracking via jointly optimizing group partition and association

This paper considers the problem of indistinguishable group target tracking, where the individual targets within a group are closely spaced and resolvable, move in a coordinated manner, and the group splitting and merging may occur. In such a case, the performance of the commonly used multi-target tracking methods may be degraded. We focus on three aspects of the problem: deal with the group association, group splitting and merging, and derive the state estimates of the individual targets. First, in order to deal with the group partition and the group association globally, we propose a novel group target tracking method within the multiple hypothesis tracking (MHT) framework by extending the target partition and the measurement partition as parts of global hypotheses. An efficient algorithm is given to find the M-best group partition and association simultaneously. Second, we use the undirected graph to represent the interaction between groups and then obtain an identification method of the group splitting and merging. Third, in response to the state estimate of the group target, a group-individual Bayesian filter is designed to estimate the states of the virtual leader and the individual targets. Finally, numerical experiments are presented to illustrate the effectiveness of the proposed method.

Byzantine Resilient Joint Localization and Target Tracking of Multi-Vehicle Systems

This work investigates the problem of joint localization and target tracking (JLATT) of multi-vehicle systems (MVSs) in the presence of Byzantine attacks (BAs). For MVSs, there may exist malicious and misbehaving vehicles, called Byzantine vehicles, which act as deceivers and pass false information to their neighbors. For the first time, we formulate a resilient JLATT framework to synthesize the hybrid problems of distributed cooperative localization and flock tracking in the presence of BAs. More specifically, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covariance weighted mean-subsequence-filter</i> (CW-MSF) algorithm to guarantee that the MVS achieves flock tracking w.r.t a target even in the presence of a certain fraction of Byzantine vehicles. Note that this resilient JLATT scheme does not require global information and thus it is fully distributed. The practicality and validity of this novel JLATT framework are verified via an illustrative numerical simulation example.

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

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

복수 탐지정보의 공간분포 사전지식을 활용한 고해상도 차량용 레이더 표적추적 필터 설계

This paper deals with the problem of target tracking using high-resolution automotive radars. Due to the enhanced angular resolution, a high-resolution radar allows a single target to produce multiple detections originated from the main scatters with complicated spatial distribution Thus, the target tracking problem is formulated as a nonlinear state estimation associated with the multi-modal measurement noise distribution. As a practical resolution to this problem, the statistical properties of the multi-detection is modeled by approximating the reflected target signal strength as Gaussian mixture, then a Gaussian sum filter is designed using the prior knowledge about the model. To prevent the divergence of the number constituents of the posterior distribution, the weights of the pairing hypotheses between the multiple detections and the modes of their distribution are evaluated and the mode management technique is applied in implementing the tracking filter. Accordingly, the proposed filter is able to improve computational efficiency while maintaining the estimation performance. Through simulations for typical automotive target tracking scenarios, it is demonstrated that our approach provides reliable target tracking performance compared to the existing filters.

Validation of target tracking performance through signal feature extraction method based on 1D convolutional neural network

In order to perform active vibration and noise control, a variety of adaptive algorithms have been investigated and developed. Especially, neural network-based signal tracking algorithms, such as the radial basis function neural network (RBFNN), are being widely utilized. For signal tracking problems, a reference signal has a significantly important role, while it is difficult to determine the reference signal when it has relatively complex spectrum. Thus, this study focuses on the signal feature extraction for manipulating appropriate reference signals easily and validation of signal tracking performance with it. In order to carried out the signal feature extraction, 1D convolutional neural network (1D CNN) is implemented and trained based on the CWRU bearing dataset. In addition, multi normalized least mean square (NLMS), neural network-based signal tracking algorithm, and diagonal recurrent neural network (DRNN) is employed to confirm the signal tracking performance. The proposed method shows that the 1D CNN-based signal feature extraction method could find the signal feature properly and the reference signal obtained from this methodology is employed to a target tracking problem, which shows a great performance.

GVF-based guidance and super-twisting control of autonomous surface vehicle for target tracking in obstacle environments with experiments

This paper addresses the target tracking problem of an under-actuated autonomous surface vehicle (ASV) subject to internal uncertainties and exogenous forces in ocean environments with static and dynamic obstacles. A collision-free target tracking (CFTT) control method is proposed based on a guiding vector field (GVF) guidance and a super-twisting algorithm (STA) control. Specifically, a resultant GVF is developed to guide the ASV towards the target position and to avoid multiple obstacles. Based on the resultant GVF, a saturated guidance law is designed to calculate the surge velocity and angular velocity commands. Then, robust exact differentiator (RED)-based observers are developed to estimate the lumped disturbances within finite time. By using the estimated lumped disturbances, STA-based anti-disturbance kinetic control laws are designed to track the given velocity commands within finite time. The finite-time input-to-state stability of the closed-loop system is proven based on cascade theory. Emulational and experimental validations are conducted to substantiate the efficacy of the proposed CFTT control method.

Labeled Multi-Bernoulli Filter Based Multiple Resolvable Group Targets Tracking With Leader-Follower Model

This paper addresses the multiple resolvable group targets tracking (MRGTT) problem wherein the leader-follower (LF) model is adopted to evolve the group structure. Since there is an interaction between the destination and each leader, such that an attraction force of the destination information on the leader needs to be considered. To cope with the time-varying number of targets/groups and target-measurement association uncertainty, the so-called labeled multi-Bernoulli (LMB) filter is regarded as the filtering tool and combined with the LF model, named LF-LMB. Totally speaking, the implementation of the proposed LF-LMB filter consists of three steps. Firstly, the stochastic differential equation (SDE) of the state is used to, respectively, characterize the motion models of the leader and the follower. Then, the state transition equations (STEs) for the leader and the follower are derived. Lastly, the derived STEs are used to modify the prediction process of the standard LMB filter. Simulation experiments are carried out to demonstrate the superiority (e.g., optimal sub-pattern assignment (OSPA) and OSPA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{(2)}$</tex-math></inline-formula> errors are reduced by at least 15 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> comparing to the standard LMB filter) and robustness of the proposed approach.

A Novel AD-PDA-BACF Algorithm for Real-Time Moving Target Shadow Tracking Using ViSAR Imagery

Detection and tracking using video synthetic aperture radar (ViSAR) have attracted a great deal of attention in recent years due to its ability to produce high-resolution videos for regions of interest. In this work, we have chosen the background-aware correlation filter (BACF) as a key algorithm due to its superior performance in real-time tracking scenarios. Dim and small shadows with weak visual features, time-varying shadows, and complex environments pose serious challenges to the target tracking problem using ViSAR videos. These factors lead to a multipeak problem in the BACF and many other correlation filter-based algorithms. As a result, incorrect target detection, template model contamination, and tracker drift or failure during long-term tracking can occur. In this work, we propose a novel appearance-distance information-assisted probabilistic data association (AD-PDA) algorithm to tackle the multipeak problem. Based on the correlation outputs of the BACF, the AD-PDA algorithm selects multiple peak locations as validated measurements. By exploiting the appearance-distance probability distribution functions, the AD-PDA algorithm calculates the mixed appearance-distance association weights and estimates target states accurately. Furthermore, we propose an efficient AD-PDA-BACF algorithm that can track targets accurately by combining the AD-PDA and BACF algorithms. This study conducts experiments using two public ViSAR video datasets released by the Sandia National Laboratory. Our results demonstrate that the proposed algorithm outperforms several state-of-the-art algorithms in tracking dim and small targets in terms of tracking accuracy, success rate, and tracking speed.

Three-Dimensional Space-and-Time-Synchronized Target-Tracking Control Under Input Saturation

In this article, we present a space-and-time synchronized control method for target-tracking problems under input saturation in the three-dimensional (3-D) framework of polar coordinates. We first utilize the prescribed-distance controller to design a reference relative trajectory, aiming to formulate a synchronization of space and time. By doing this, the specified position can be approached at its predetermined instant and the relative trajectory in space is independent of time. Next, through correlating and decoupling the reference/actual kinematics between the self vehicle and the target, time and space are separated, controlled independently. To further address the existing closed-loop system errors, we propose a dynamic velocity assignment of the relative reference trajectory, which is an independent extra degree of freedom, to alleviate the negative effects caused by input saturation. Finally, numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed scheme.

Equilateral triangular formation of unmanned surface vehicles for target tracking with event-triggered collision avoidance

Multiple cooperative unmanned surface vehicles (USVs) offer enhanced capability than a single USV for complex missions. In this paper, the multi-objective control problem of target tracking by equilateral triangular formation of underactuated USVs with collision avoidance between USVs as well as between the USV and the target is addressed with the distance-based approach. A novel control algorithm with event-triggered collision avoidance is designed to accelerate formation shape convergence and reduce actuator activity to achieve a better trade-off between different control objectives. “Rotational forces” are introduced to the control law so that the formation turns to a prescribed orientation to guarantee avoiding collisions with the target in the tracking process with the least amount of control efforts. The gradient-property of the proposed control system and global convergence to the desired shape are proven. Finally, the effectiveness of the proposed formation tracking control with event-triggered collision avoidance is verified by simulation.

Multiradar Joint Tracking of Cluster Targets Based on Graph-LSTMs

The cluster target brings a serious challenge to the traditional multisensor multitarget tracking algorithm because of its large number of members and the cooperative interaction between members. Using multiradar joint tracking cluster target is an alternative method to solve the problem of cluster target tracking, but it inevitably brings the problem of radar-target assignment and tracking information fusion. Aiming at the problem of radar-target assignment and tracking information fusion, a joint tracking method based on graph-long short-term memory neural nets (Graph-LSTMs) is proposed. Firstly, we use multivariable stochastic differential equations (SDE) to model the cooperative interaction of cluster members and transform the derived state space model of cluster members into the same form as the constant velocity (CV) motion model, and the target state equation of cluster which can be used for Bayesian filtering iteration is established. Secondly, based on the detection relationship between radars and cluster members, we introduce the detection confirmation matrix and propose a radar-target assignment method to achieve multiple measurements of single member and detection coverage of all cluster members. Then, each radar uses δ-GLMB filter to estimate the motion state of the assigned targets. Finally, on the basis of spatial discretization, the labels of multiple estimates of cluster member states are obtained. We use the designed Graph-LSTMs to learn the cooperative relationship between target states to fuse the labels and obtain better tracking effect. The experimental results show that the proposed method effectively simulates the cluster motion and realizes the joint estimation of cluster target motion state by multiradar. Our method makes up for the defect that a single radar cannot stably track adjacent multiple targets and achieves better estimation fusion effect than the expectation-maximization (EM) algorithm and mean method.

Robust fixed-lag smoothing under model perturbations

A robust fixed-lag smoothing approach is proposed in the case there is a mismatch between the nominal model and the actual model. The resulting robust smoother is characterized by a dynamic game between two players: one player selects the least favorable model in a prescribed ambiguity set, while the other player selects the fixed-lag smoother minimizing the smoothing error with respect to least favorable model. We propose an efficient implementation of the proposed smoother. Moreover, we characterize the corresponding least favorable model over a finite time horizon. Finally, we test the robust fixed-lag smoother in two examples. The first one regards a target tracking problem, while the second one regards a parameter estimation problem.

Multi-Target State and Extent Estimation for High Resolution Automotive Sensor Detections

This paper discusses the perception and tracking of individual as well as group targets as applied to multi-lane public traffic. Target tracking problem is formulated as a two hierarchical layer problem-on the first layer, a multi-target tracking problem based on multiple detections is distinguished in the measurement space, and on the second (top) layer, group target tracking with birth and death as well as merging and splitting of group target tracks as they evolve in a dynamic scene is represented. This configuration enhances the multi-target tracking performance in situations including but not limited to target initialization(birth), target occlusion, missed detections, unresolved measurement, target maneuver, etc. In addition, group tracking exposes complex individual target interactions to help in situation assessment which is challenging to capture otherwise.

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety.

Collision avoidance plays a major part in the control of the wheeled mobile robot (WMR). Most existing collision-avoidance methods mainly focus on a single WMR and environmental obstacles. There are few products that cast light on the collision-avoidance between multiple WMRs (MWMRs). In this article, the problem of simultaneous collision-avoidance and target tracking is investigated for MWMRs working in the shared environment from the perspective of optimization. The collision-avoidance strategy is formulated as an inequality constraint, which has proven to be collision free between the MWMRs. The designed MWMRs control scheme integrates path following, collision-avoidance, and WMR velocity compliance, in which the path following task is chosen as the secondary task, and collision-avoidance is the primary task so that safety can be guaranteed in advance. A Lagrangian-based dynamic controller is constructed for the dominating behavior of the MWMRs. Combining theoretical analyses and experiments, the feasibility of the designed control scheme for the MWMRs is substantiated. Experimental results show that if obstacles do not threaten the safety of the WMR, the top priority in the control task is the target track task. All robots move along the desired trajectory. Once the collision criterion is satisfied, the collision-avoidance mechanism is activated and prominent in the controller. Under the proposed scheme, all robots achieve the target tracking on the premise of being collision free.