基于UWB的LSM-Taylor级联车辆定位算法

LSM-Taylor Cascade Vehicle Location Algorithm Based on UWB

为了满足智能车在室内的高精度定位要求,针对室内的伪三维定位场景,提出了一种基于超宽带(Ultra Wideband,UWB)的LSM-Taylor级联车辆定位算法。该算法以到达时间差(Time Difference of Arrival,TDOA)为定位方式,以多基站最小二乘法(Least Square Method,LSM)定位算法的计算结果为初始值,通过Taylor级数迭代估计车辆的精确位置。该算法主要解决多径效应和非视距产生的测量误差对定位精度的影响,从而提高定位精度。在仿真结果中,相比LSM定位算法,LSM-Taylor级联定位算法的定位结果分布更加紧密,定位精度更高。实际测试结果表明,该定位算法的均方根误差(Root Mean Squared Error,RMSE)在10 cm以下,能满足智能驾驶中的室内定位要求,验证了该方法的有效性。

In order to meet the requirements of high-precision indoor positioning for intelligent vehicles,a LSMTaylor cascade vehicle positioning algorithm based on ultra wideband(UWB)is proposed for indoor pseudo-3 D positioning scene.TDOA(time difference of arrival)is taken as the positioning method,the calculation result of the multi-base station LSM(least square method)positioning algorithm is taken as the initial value,and the exact position of the vehicle is estimated through Taylor series iteration.The influences of multipath effects and NLOS(non-line-of-sight)error on the positioning accuracy are removed,and the positioning accuracy is improved.Compared with LSM positioning algorithm,LSM-Taylor cascade positioning algorithm has tighter positioning distribution and higher positioning accuracy.The actual test results show that the root mean square error(RMSE)of the algorithm is less than 10 cm,which can meet the indoor positioning requirements in intelligent driving,and verify the effectiveness of the method.