{"title":"Range-Only UWB 6-D Pose Estimator for Micro UAVs: Performance Analysis","authors":"Junning Lyu;Tao Song;Shaoming He","doi":"10.1109/TAES.2024.3520950","DOIUrl":null,"url":null,"abstract":"In recent years, ultra-wideband (UWB) technology has been widely used in indoor unmanned aerial vehicle (UAV) localization due to its high-precision ranging capabilities. Many localization schemes use multiple UWB devices onboard the UAV to correlate distance and orientation, demonstrating better resistance to magnetic field interference compared to relying solely on inertial measurement unit for orientation estimation. However, the precision achieved varies significantly among different approaches, and there lacks a unified metric to evaluate the performance of the estimation algorithms. In this work, we first derive an unconstrained Cramér–Rao Lower Bound (CRLB) for UAV pose estimation using range-only measurements. To assess the multiple effect of specified distance measurement errors on 6-D pose estimation accuracy, we leverage the CRLB to extend the conventional dilution of precision (DOP) concept and propose two physically meaningful orientation-coupled dilution of precision (ODOP) metrics. By utilizing the proposed ODOP metrics, we deduce their mathematical bounds and conduct a detailed analysis of the influence of tag, anchor, and pose distribution on the estimation performance. We evaluate the localization accuracy under various conditions to validate the technical findings.","PeriodicalId":13157,"journal":{"name":"IEEE Transactions on Aerospace and Electronic Systems","volume":"61 2","pages":"5284-5301"},"PeriodicalIF":5.7000,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Aerospace and Electronic Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10811832/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, ultra-wideband (UWB) technology has been widely used in indoor unmanned aerial vehicle (UAV) localization due to its high-precision ranging capabilities. Many localization schemes use multiple UWB devices onboard the UAV to correlate distance and orientation, demonstrating better resistance to magnetic field interference compared to relying solely on inertial measurement unit for orientation estimation. However, the precision achieved varies significantly among different approaches, and there lacks a unified metric to evaluate the performance of the estimation algorithms. In this work, we first derive an unconstrained Cramér–Rao Lower Bound (CRLB) for UAV pose estimation using range-only measurements. To assess the multiple effect of specified distance measurement errors on 6-D pose estimation accuracy, we leverage the CRLB to extend the conventional dilution of precision (DOP) concept and propose two physically meaningful orientation-coupled dilution of precision (ODOP) metrics. By utilizing the proposed ODOP metrics, we deduce their mathematical bounds and conduct a detailed analysis of the influence of tag, anchor, and pose distribution on the estimation performance. We evaluate the localization accuracy under various conditions to validate the technical findings.
近年来,超宽带(UWB)技术因其高精度的测距能力在室内无人机定位中得到了广泛的应用。许多定位方案使用UAV机载的多个UWB设备来关联距离和方向,与仅仅依靠惯性测量单元进行方向估计相比,显示出更好的抗磁场干扰能力。然而,不同方法所获得的精度差异很大,并且缺乏统一的度量来评估估计算法的性能。在这项工作中,我们首先推导了无人机姿态估计的无约束cram r - rao下界(CRLB)。为了评估特定距离测量误差对6-D姿态估计精度的多重影响,我们利用CRLB扩展了传统的精度稀释度(DOP)概念,并提出了两个物理上有意义的方向耦合精度稀释度(ODOP)指标。通过利用提出的ODOP度量,我们推导了它们的数学界限,并详细分析了标签、锚点和姿态分布对估计性能的影响。我们评估了不同条件下的定位精度,以验证技术发现。
期刊介绍:
IEEE Transactions on Aerospace and Electronic Systems focuses on the organization, design, development, integration, and operation of complex systems for space, air, ocean, or ground environment. These systems include, but are not limited to, navigation, avionics, spacecraft, aerospace power, radar, sonar, telemetry, defense, transportation, automated testing, and command and control.