A Centralized Control Algorithm for Target Tracking with UAVs

This paper addresses the mobile targets covering problem by using unmanned aerial vehicles (UAVs). It is assumed that each UAV has a limited initial energy and the energy consumption is related to the UAV's altitude. Indeed, the higher the altitude, the larger the monitored area and the higher the energy consumption. When an UAV runs out of battery, it is replaced by a new one. The aim is to locate UAVs in order to cover the piece of plane in which the target moves by using a minimum number of UAVs. Each target has to be monitored for each instant time.The problem under consideration is mathematically represented by defining mixed integer non-linear optimization models. Heuristic procedures are defined and they are based on restricted mixed integer programming (MIP) formulation of the problem. A computational study is carried out to assess the behaviour of the proposed models and MIP-based heuristics. A comparison in terms of efficiency and effectiveness among models and heuristics is carried out.

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“…Real vision-based UAV navigation and guidance systems are presented in [3,24]. Detection problems are also studied in [23,28].…”

Section: Related Workmentioning

confidence: 99%

Modelling the mobile target covering problem using flying drones

et al. 2015

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“…To this end, we minimize the weighted trace of the one-step-ahead prediction error covariance Pk+1 [34]. In practical scenarios, the UAV dynamics limit the rate at which its heading may change.…”

Section: A Error Covariance Minimization (Ecm)mentioning

confidence: 99%

Adaptive Mobile Sensor Positioning for Multi-Static Target Tracking

Zhan

Casbeer

Swindlehurst

2010

IEEE Trans. Aerosp. Electron. Syst.

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Unmanned Air Vehicles (UAVs) are playing an increasingly prominent role in both military and civilian applications. In this paper, we focus on the use of multiple UAV agents in a target tracking application where performance is improved by exploiting each agent's maneuverability. Local time-delay and Doppler measurements made at each UAV are used as inputs to an Extended Kalman Filter (EKF) which tracks the target's position and velocity. Two simple metrics are defined to quantify the accuracy of the tracking algorithm, and heading feedback to the UAVs is used to minimize the metric and improve tracking performance. A simplified version of one of the algorithms that reduces computational complexity is also presented. Simulations demonstrate the significant improvement that results when the UAV sensors are allowed to be optimally positioned during tracking.

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“…Recently, unmanned aerial vehicles (UAVs) have attracted considerable attention in many military as well as civilian applications [1], [2]. Besides other advantages, one major advantage of using UAVs is that they can be quickly deployed into the battlefield, or various communication environments as relays [3], and therefore improve the performance of wireless communications systems [4], [5].…”

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confidence: 99%

Wireless Relay Communications using ann Unmanned Aerial Vehicle

Zhan

Swindlehurst

2006

2006 IEEE 7th Workshop on Signal Processing Advances in Wireless Communications

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Abstract-Herein, we investigate the optimal deployment of an unmanned aerial vehicle (UAV) in a wireless relay communication system. The optimal UAV position is found by maximizing the average data rate, while at the same time keeping the symbol error rate (SER) below a certain threshold. We derive a closedform expression for the average data rate in a fixed wireless link using adaptive modulation. By using the alternate definite integral form for the Gaussian Q-function, the symbol error rate (SER) of the system in the link level is evaluated. An upper bound on the SER is also derived using the improved exponential bounds for the Q-function. It is shown that the derived SER expression matches the simulation results very well and the derived upper bound is tight for a wide range of SNRs. Simulation results also show that the system data rate matches the derived closed-form expression.

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A Centralized Control Algorithm for Target Tracking with UAVs

Cited by 31 publications

References 6 publications

Modelling the mobile target covering problem using flying drones

Modelling the mobile target covering problem using flying drones

Adaptive Mobile Sensor Positioning for Multi-Static Target Tracking

Wireless Relay Communications using ann Unmanned Aerial Vehicle

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