Multi-UAV cooperative target tracking with bounded noise for connectivity preservation

ZHOU Rui; FENG Yu; DI Bin; ZHAO Jiang; HU Yan

doi:10.1631/FITEE.1900617

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Multi-UAV cooperative target tracking with bounded noise for connectivity preservation

Regular Papers | Updated：2022-05-19

- Multi-UAV cooperative target tracking with bounded noise for connectivity preservation
- 有界噪声下保通信连接的多无人机分布式协同目标跟踪
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 10, Pages: 1494-1503(2020)
- Affiliations：
  
  School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
  National Innovation Institute of Defense Technology, Academy of Military Sciences PLA China, Beijing 100171, China
  CETC Key Laboratory of Aerospace Information Applications, Shijiazhuang 050081, China
- Author bio：
  
  Jiang ZHAO, E-mail: jzhao@buaa.edu.cn
- Funds：
- DOI：10.1631/FITEE.1900617
  CLC： TN953;TP391.41
- Received：12 November 2019，
  
  Revised：2020-;7-28，
  
  Published：2020-10
- Accepted：
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ZHOU Rui, FENG Yu, DI Bin, et al. Multi-UAV cooperative target tracking with bounded noise for connectivity preservation[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(10): 1494-1503.
DOI：

ZHOU Rui, FENG Yu, DI Bin, et al. Multi-UAV cooperative target tracking with bounded noise for connectivity preservation[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(10): 1494-1503. DOI： 10.1631/FITEE.1900617.

摘要

本文研究通信距离受限的多无人机协同目标跟踪问题。该问题集成了无人机运动控制、目标状态估计和网络拓扑控制。首先，介绍用于描述网络连通性的通信拓扑和基本符号，以及分布式卡尔曼一致性滤波器。其次，分析基于滤波器的估计误差收敛性和有界性，采用势函数方法实现通信连接保持和防撞控制。在考虑稳定跟踪的基础上，设计基于势函数的分布式无人机运动控制器。由于无人机仅能获得目标状态的估计值而非真实值，且其运动也会影响状态估计精度，因此目标状态估计与无人机运动控制是耦合的。最后，详细分析耦合系统在有界噪声下的稳定性和收敛性，并进行仿真验证。

Abstract

We investigate cooperative target tracking of multiple unmanned aerial vehicles (UAVs) with a limited communication range. This is an integration of UAV motion control

target state estimation

and network topology control. We first present the communication topology and basic notations for network connectivity

and introduce the distributed Kalman consensus filter. Then

convergence and boundedness of the estimation errors using the filter are analyzed

and potential functions are proposed for communication link maintenance and collision avoidance. By taking stable target tracking into account

a distributed potential function based UAV motion controller is discussed. Since only the estimation of the target state rather than the state itself is available for UAV motion control and UAV motion can also affect the accuracy of state estimation

it is clear that the UAV motion control and target state estimation are coupled. Finally

the stability and convergence properties of the coupled system under bounded noise are analyzed in detail and demonstrated by simulations.

关键词

Keywords

references

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