智能汽车两阶段UWB定位算法

Two-stage UWB positioning algorithm of intelligent vehicle

为了提高智能汽车行驶的可靠性,以超宽带(UWB)为研究对象,研究了智能汽车两阶段UWB定位算法;分析了智能汽车UWB定位算法的基本原理与误差来源;建立了测距值筛选与加权位置解算两阶段UWB定位算法,在测距值筛选阶段,采用高斯筛选剔除小概率、大干扰事件,在加权位置解算过程中,根据多测距点的位置坐标加权计算得到最终的位置坐标,以有效减小非视距、多径效应所带来的误差,通过使用抗多径天线以有效减小多径效应所带来的误差,并分别建立了静态补偿和运动补偿策略,以有效减小设备晶振偏差等硬件问题造成的误差;在MATLAB/Simulink仿真平台中搭建一定测距方差约束下的UWB随机测距值仿真环境,对算法进行了仿真测试并与三边定位算法、三边质心定位算法进行仿真比较,分析基站数量对定位精度的影响;搭建实物UWB测试系统,对UWB设备定位精度进行了评估与误差补偿,并对两阶段UWB定位算法进行了实车测试。仿真结果表明:东向和北向的定位误差均值最小分别可达0.3823、0.4470 m;补偿后的UWB定位轨迹更接近RT3002所示的轨迹,东向和北向轨迹误差的平均值分别为0.0492、0.0178 m,均方根误差分别为0.0698、0.0264 m。可见,提出的智能汽车两阶段UWB定位算法能够满足智能汽车的定位需求,具有高精度、低成本、稳定性好等优点。

To improve the driving reliability of intelligent vehicles, taking the ultra-wide band(UWB) as the research object, the two-stage UWB positioning algorithm for intelligent vehicles was studied. The basic principles and error sources of intelligent vehicle’s UWB positioning algorithm were analyzed. A two-stage UWB positioning algorithm was established to filter the ranging values and calculate the weighted positions. In the filtering stage of ranging values, small probabilities and large interference events were eliminated through the Gaussian filtering. In the calculation stage of weighted positions, the final position coordinates were obtained by weighting the position coordinates of multiple ranging points to effectively reduce the errors caused by non-line-of-sight and multipath effects. The errors of multipath effects were effectively reduced by using the anti-multipath antennas, and the static and motion compensation strategies were established to effectively reduce the errors caused by hardware problems, such as the crystal deviation of the device. A simulation environment for UWB random ranging values under certain range variance constraints was built by using the MATLAB/Simulink simulation platform. The algorithm was simulated and compared with the trilateral positioning algorithm and the trilateral centroid positioning algorithm, and the impact of the number of base stations on positioning precision was analyzed. A physical UWB test system was built, the positioning precision of UWB equipment was evaluated, and the error compensation was performed. The two-stage UWB positioning algorithm was tested on a real vehicle. Simulation result shows that the mean values of positioning errors in the east and north directions can be as small as 0.382 3 and 0.447 0 m, respectively. The compensated UWB positioning trajectory is closer to the trajectory shown by RT3002. The average values of the east and north trajectory errors are 0.049 2 and 0.017 8 m, and the root mean square errors are 0.069 8 and 0.026 4 m, respectively. Thus, the proposed two-stage UWB positioning algorithm can meet the positioning requirements of intelligent vehicles, and has the advantages of high precision, low cost, and good stability. 2 tabs, 20 figs, 32 refs.