惯性测量装置
计算机科学
全球导航卫星系统应用
测距
卡尔曼滤波器
扩展卡尔曼滤波器
干扰(通信)
传感器融合
实时计算
全球定位系统
人工智能
电信
频道(广播)
作者
Yi Zheng,Yaqin Xie,Jiamin Li
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:11: 105499-105512
被引量:3
标识
DOI:10.1109/access.2023.3315870
摘要
When it comes to disaster rescue, quickly and accurately locating the trapped people will not miss the golden rescue time, thereby improving the success rate of rescue. The existing research has focused on the use of unmanned aerial vehicles (UAVs) to locate stationary user equipment (UE). However, the simultaneous movement of the UAVs and the UE brings about problems of UAV coordinate deviation and range interference, resulting in poor accuracy and stability of UE localization. To solve the mentioned issues above, a fusion localization method based on multi-UAV collaborative ranging and user-side inertial measurement unit (IMU) module was proposed for GNSS-denied scenarios. First, the initial coordinate deviations of the UAVs were corrected by multi-UAV collaboration; Then, a pre-processing scheme based on density peaks clustering (DPC) was proposed to eliminate the abnormal interference in the ranging data; Finally, the unscented Kalman filter (UKF) was used to realize the fusion localization based on multi-UAV ranging and user-side IMU module. The simulation results showed that the proposed scheme can maintain the accuracy and stability of UE localization in the presence of ranging interference, and the positioning error was reduced by 21.4% compared with the commonly used extended Kalman filter (EKF). Moreover, the proposed localization scheme had a better performance in low-altitude rather than high-altitude flight scenarios.
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