基于卡尔曼滤波的UGV-UAV协同作战鲁棒目标定位与跟踪

S. K. Tripathi, Rahul M Sapre
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引用次数: 7

摘要

UGV-UAV协同作战中对目标的鲁棒跟踪和定位是最重要的。在实际应用中广泛采用的目标定位和跟踪技术要么是利用无人机传感器进行广域监视,要么是向地面无人驾驶车辆提供关键信息。现有技术的局限性是由于无法获得目标位置而导致目标定位和跟踪不准确。研究了利用无人机上的传感器对目标位置进行间接测量的情况下与无人机的协同目标跟踪问题。本文论证了在有噪声和不可预测的目标观测场景下,采用卡尔曼滤波可以获得较好的跟踪效果。目标跟踪的精度取决于UAV和UGV的GPS传感器数据的测量。通过多次实验,跟踪精度可达约。已经达到了2000万。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust target localization and tracking using Kalman filtering for UGV-UAV coordinated operation
The need of robust target tracking and localization in UGV-UAV coordinated operation is the most important. Common target localization and tracking techniques that are widely adopted in practice are either utilizing wide area surveillance using unmanned aerial vehicle (UAV) sensors and providing crucial information to on-ground unmanned ground vehicle (UGV). The limitations of existing techniques are inaccurate target localization and tracking due to inaccessible target position. This paper considers the problem of cooperative target tracking with UAV subject to indirect measurement of target position using sensors on UAV. In this paper, it is demonstrated that better tracking results are obtained by using Kalman filtering, under noisy and unpredictable target observation scenario. The accuracy of target tracking depends on measurement of GPS sensor data from UAV & UGV. By performing several experiments tracking accuracy up to approx. 20m has been achieved.
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