RF Source Localization using Unmanned Aerial Vehicle with Particle Filter
RF Source Localization using Unmanned Aerial Vehicle with Particle Filter
复制标题
使用带粒子滤波器的无人机进行射频源定位
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
G. Inalhan
中科院分区：
文献类型：
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作者：
M. Hasanzade;Ömer Herekoğlu;R. Yeniceri;E. Koyuncu;G. Inalhan
本文提出了一种解决无人机大范围环境下射频发射源定位问题的方法。由于噪声的特性，利用接收信号强度指标(RSSI)进行目标定位是最具挑战性的问题之一。为了评估噪声对RSSI的影响，我们进行了RSSI测量测试。这为适当的模型增加了价值，有助于实现更逼真的仿真系统。在定位过程中，本文使用了粒子滤波来代替扩展卡尔曼滤波等多无人机定位工具。准备了仿真环境和半实物系统，以真实的模型和自动驾驶系统展示概念验证。仿真结果表明，定位的平均搜索时间为84.06秒，平均距离误差为13.96米。
In this paper, we propose a solution for the localization problem of a radio frequency (RF) emitting source over a large scale environment with unmanned aerial vehicle (UAV). Target localization using received signal strength indicator(RSSI) is one of the most challenging problem because of noise characteristics. To evaluate the noise effect on RSSI, we perform a RSSI measurement test. This adds value for proper model and helps to implement a more realistic simulation system. For the localization process, the particle filter is utilized in this paper instead of tools such as Extended Kalman Filter with multi UAVs. Simulation environment and software-in-the-loop system are prepared to exhibit the conceptual proof with realistic models and autopilot system. Simulation results show that, mean search time for localization is 84.06 seconds and mean distance error is 13.96 meters.