| 摘要: |
| 无人机的移动定位是应对无人机机动性和应用环境复杂性的关键技术。为解决无人机中的全球定位系统（GPS）信号失效问题，提出了一种通过机载无线射频的接收信号强度解决定位问题的技术,分别采用扩展卡尔曼滤波方法估计距离和最小二乘方法估计路径损耗因子两种处理方法。理论分析和实验测试结果证实所提算法对有色噪声干扰下的接收信号有较好的增强效果，基于80%的置信水平，新算法相对于白噪声模型将估算误差从9.5 m减少到了4 m，还进一步提供了融合惯性导航的算法。 |
| 关键词: 无人机 移动定位 扩展卡尔曼滤波 距离估算 |
| DOI： |
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| 基金项目:国家自然科学基金资助项目(71201026)；东莞市高等院校、科研机构科技项目(2014106101034)；东莞职业技术学院科研基金项目（2015b06） |
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| UAV positioning based on extended Kalman filter algorithm |
| YANG Runfeng,LUO Chunbo,ZHANG Zhicong,LI Mingzhao |
| () |
| Abstract: |
| Mobile positioning is essential for Unmanned Aerial Vehicles(UAVs) to cope with UAV mobility and the complexity of deployed environments.To solve the problem that UAVs' Global Positioning System(GPS) signal fails,radio frequency(RF) signal strength from the on-board communication module is adopted. For the processing of received signals,the extended Kalman filter(EKF) method is used to estimate distance and the Least Square(LS) algorithm is used to estimate the path loss factor. Theoretical analysis and experiment results demonstrate that the proposed algorithms offer better performance for enhancing received signals under coloured noise and improving distance estimation accuracy. Given a confidence level of 80%,the new algorithm has improved the estimation accuracy from 9.5 m to 4 m. The further enhancement with data fusion from the gyro information is also provided. |
| Key words: unmanned aerial vehicle(UAV) mobile positioning extended Kalman filter distance estimation |
《电讯技术》编辑部 