Cooperative localization based on the azimuth angles among multiple UAVs

Y. Qu, Jizhi Wu, Youmin Zhang
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引用次数: 8

Abstract

In view of the potentials and benefits of using unmanned aerial vehicles (UAVs) in civil and surveillance applications, such as forest fire monitoring and fighting, earthquake and natural disasters monitoring and sensing etc., the cooperative flight of multiple UAVs has been paid more and more attention. Among the applications of multiple UAVs in cooperative flight, fault-tolerant cooperative localization against global positioning system (GPS) signal loss due to GPS receiver malfunction is one of key techniques for various practical applications. The current research on fault-tolerant cooperative localization of UAVs is mainly based on the distance between UAVs. However, cooperative localization based on the azimuth angles between UAVs has not been generally researched so far. This paper aims to solve the problem of fault-tolerant cooperative localization of UAVs by introducing the azimuth angles between UAVs. Firstly, the basic localization model is established and the formulas of localization in two and three dimensional coordinate systems are derived. Then the optimal reference planes are chosen with the principle of minimum horizontal dilution of positioning (HDOP). Kalman filter is applied to decrease the influence of observation errors on localization. Kalman filter and extended Kalman filter are designed against linear and non-linear systems, respectively. Finally, the simulation results indicate that the cooperative localization based on the azimuth angles achieves high localization accuracy.
基于方位角的多无人机协同定位
鉴于无人机在森林火灾监测与作战、地震和自然灾害监测与传感等民用和监视应用中的潜力和效益,多架无人机的协同飞行越来越受到重视。在多无人机协同飞行的应用中,针对GPS接收机故障导致的GPS信号丢失的容错协同定位是各种实际应用的关键技术之一。目前对无人机容错协同定位的研究主要基于无人机之间的距离。然而,基于无人机间方位角的协同定位技术目前还没有得到广泛的研究。本文通过引入无人机之间的方位角来解决无人机的容错协同定位问题。首先,建立了定位的基本模型,推导了二维和三维坐标系下的定位公式;然后根据最小水平定位稀释(HDOP)原则选择最佳参考平面。采用卡尔曼滤波减小观测误差对定位的影响。分别针对线性和非线性系统设计了卡尔曼滤波器和扩展卡尔曼滤波器。仿真结果表明,基于方位角的协同定位方法具有较高的定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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