Coordinated Target Tracking Research Articles

Maneuvering Resource Allocation for Coordinated Target Tracking in Airborne Radar Network

In this paper, a performance driven maneuvering resource allocation (MRA) scheme is developed for target tracking in airborne radar network (ARN). To exploit the degree of freedom of ARN mobility for target tracking, we formulate a MRA scheme for maximizing the target tracking performance, under the practical constraints on the airborne radar's speed and attitude variation rate, as well as threat zone and collision avoidance. We adopt the Bayesian Cramér-Rao lower bound as a metric function to gauge the target tracking performance, and build the MRA scheme as a non-convex optimization problem. Instead of using the heuristic based methods to solve the resulting non-convex optimization problem, we design an efficient three-step solution technique, which incorporates inactive constraints elimination and active constraints linearization procedure. In such a case, the resulting relaxed problem can be solved with guaranteed convergence through the proximal alternating direction method of multipliers. Simulation results demonstrate that the proposed MRA scheme can greatly increase the target tracking accuracy, and is computationally more efficient than the heuristic algorithms.

Coordinated target tracking in sensor networks by maximizing mutual information

Abstract This paper addresses the problem of coordinated target tracking in sensor networks. For a typical target tracking scene with nonlinear bearing-only measurements, we first investigate the mutual information between multiple sensors and the target state. To improve the performance of target tracking, we analyzed the relative positions between sensor agents and the target to be tracked and derived the optimal positions for sensors in the network by mutual information maximization. Simulation results are presented and discussed to demonstrate that the performance of estimated target states could be improved by the proposed method.

Coordinated target tracking of two UAVs based on game theory approach and Lyapunov guidance vector fields

Considering the problems of the lack of tracking the intelligent target, which is “smart” enough to escape from the detection by maximizing the estimation error in the current tracking methods by multi-UAVs, a cooperative control method for tracking intelligent target by two UAVs is proposed based on Lyapunov guidance vector field. The mathematical model of an intelligent target is established. According to the multi-UAV distributed intelligent target state fusion estimation method, the target state information is obtained to control two UAVs to circle the target through the Lyapunov guidance vector method. The simulation results demonstrated that the method enables two UAVs to track the intelligent target stably and improve the intelligent target’s positioning accuracy effectively

Coordinated target tracking by multiple unmanned surface vehicles with communication delays based on a distributed event-triggered extended state observer

This paper is concerned with a coordinated target tracking problem for a network of unmanned surface vehicles (USVs) subject to communication delays and constrained actuation resources. The target to be tracked maneuvers with arbitrary velocity. Its position can be measured by a small fraction of USVs whereas its velocity is not known to follower USVs. A resource-aware event-triggered control design method is presented for coordinated target tracking. Specifically, a distributed event-triggered extended state observer (ESO) is designed to estimate the position and velocity of the target over a cooperative estimation network. Furthermore, the above result is extended to cooperative estimation subject to communication delays, and a time-delayed distributed event-triggered ESO is designed. In the control law design, an event-triggered anti-disturbance position tracking control law is proposed for each follower USV based on a reduced-order ESO. By the proposed resource-aware event-triggered control laws, cooperative target tracking can be achieved regardless of the model uncertainty and external disturbances. Moreover, both the execution rate of the actuators and communication burden of the network are reduced. The stability of the closed-loop system is proven through input-to-state stability and cascade stability analysis. Simulation results demonstrate the effectiveness of the proposed method for the coordinated target tracking of USVs.

Coordinated Target Tracking Strategy for Multiple Unmanned Underwater Vehicles With Time Delays

Focusing on coordination and consensus, this paper, addresses a control problem for a group of unmanned underwater vehicles (UUVs) by tracking a maneuvering target with varying velocity and time delays. To realize coordinated target tracking, consensus control of multiple UUVs requires neighboring UUV state information, detected target state information, and estimated target acceleration information. At least one UUV is assumed to be capable of obtaining information about the target, and the communication topology graph of the vehicle is assumed to be undirected connected. We consider the convergence analysis of this multi-UUV system for two conditions: information interaction between the target and an UUV with time delays and information interaction between the target and an UUV without time delays. Sufficient conditions for the uniform ultimate boundedness of the tracking errors and estimation errors are obtained by utilizing a Lyapnov-Krasovskii functional. Two simulations are presented to demonstrate the effectiveness of the proposed method.

Coordinated Target Tracking via a Hybrid Optimization Approach.

Recent advances in computer science and electronics have greatly expanded the capabilities of unmanned aerial vehicles (UAV) in both defense and civil applications, such as moving ground object tracking. Due to the uncertainties of the application environments and objects’ motion, it is difficult to maintain the tracked object always within the sensor coverage area by using a single UAV. Hence, it is necessary to deploy a group of UAVs to improve the robustness of the tracking. This paper investigates the problem of tracking ground moving objects with a group of UAVs using gimbaled sensors under flight dynamic and collision-free constraints. The optimal cooperative tracking path planning problem is solved using an evolutionary optimization technique based on the framework of chemical reaction optimization (CRO). The efficiency of the proposed method was demonstrated through a series of comparative simulations. The results show that the cooperative tracking paths determined by the newly developed method allows for longer sensor coverage time under flight dynamic restrictions and safety conditions.

Coordinated Target Tracking by Distributed Unscented Information Filter in Sensor Networks with Measurement Constraints

Tracking a target in a cluttered environment is a representative application of sensor networks. In this paper, we develop a distributed approach to estimate the motion states of a target using noisy measurements. Our method consists of two parts. In first phase, using the unscented sigma-point transformation techniques and information filter framework, a class of algorithms denoted as unscented information filters was developed to estimate the states of a target to be tracked. These techniques exhibit robustness and accuracy of sigma-point filters for nonlinear dynamic inference while being as easily fused as the information filters. In the second phase, we proposed a novel consensus protocol which allows each sensor node to find a consistent estimate of the value of the target. Under this protocol, the final estimate of the value of the target at each time step is iteratively updated only by fusing the neighbors’ measurements when one sensor node is out of the measurement scope of the target. Performance of the distributed unscented information filter is demonstrated and discussed on a target tracking task.

Cooperative Estimation for Coordinated Target Tracking in a Cluttered Environment

The purpose of the work in this paper is to gain insight into several fundamental questions that arise whenever a distributed multiple-vehicle control system is endowed with communication capabilities. These fundamental questions include: what data should each vehicle share?, how frequently should communication take place?, and what benefit does communication provide? These questions are evaluated with respect to a target tracking task in which multiple pursuit vehicles estimate the state of a target vehicle. This task has three main components: communication, estimation, and control. Communication takes place on a broadcast network, estimation is achieved with an Unscented Kalman Filters, and the controller is behavior-based. Simulation results show that communication always improves distributed estimate results. Which information to transmit depends on available bandwidth, and more frequent communication generally yields better estimates. Simulation results also show how coordinated control can be beneficial to target tracking in a cluttered environment.