摘要
为了提高小型无人机无源目标定位的精度,设计了一种新的目标定位算法。首先确定目标定位过程中的坐标转换关系并推导出视轴角的计算模型;然后,利用光电侦察平台锁定跟踪目标的特性,提出了对同一目标点多次测量的目标定位框架,建立了系统状态方程和测量方程,考虑到测量方程的非线性,将无迹卡尔曼滤波应用于目标位置估计;最后,针对加性高斯白噪声的非线性目标定位系统,推导出理论上的定位误差的克拉美-罗下限。仿真结果表明,该算法具有较高的目标定位精度,滤波器估计误差均方差已逼近非线性系统的克拉美-罗下限。现场试验结果表明,在离地面约1000 m的空中,无人机对地面目标定位精度可达8.1 m。该算法易于部署,可操作性强,具有较大的实用价值。
In order to improve the passive target localization accuracy for small unmanned aerial vehicle（ UAV）,a novel target localization algorithm is proposed. Firstly,the coordinate transformation relation in target localization process is established and the calculation model of camera line-of-sight angle is derived. Then according to the characteristic of locking tracking target of photoelectric detection platform,the target localization framework for measuring the same target repeatedly is proposed; the system state equation and measurement equation are established. Due to the nonlinearity of the measurement equation,unscented Kalman filter（ UKF） is applied in the target position estimation. Finally,aiming at the nonlinear target localization system with overlapped zeromean Gaussian white noise,the Cramer-Rao low bound（ CRLB） of theoretical localization error is derived. The simulation results show that this algorithm has high target localization accuracy,and the root mean square error（ RMSE） of the filter estimation error has been approaching to the CRLB of the nonlinear system. Field experiment results show that the localization accuracy for a ground target can reach 8. 1 m when the UAV flies at about 1000 m above ground. The algorithm is easy to deploy,has strong operability and great practical value.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2015年第5期1115-1122,共8页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(61401200)项目资助
关键词
小型无人机
无源目标定位
无迹卡尔曼滤波
克拉美-罗下限
small unmanned aerial vehicle（UAV） passive target localization unscented Kalman filter（UKF） Cramer-Rao low bound（CRLB）
