温室作业机具室内定位方法研究

针对农机具在温室大棚内的定位、作业轨迹跟踪及作业面积核算需求,提出一种基于多源数据融合理论的温室机具室内定位优化算法。首先根据惯性导航测量技术预估被测目标定位初值,再利用无线RSSI测距技术使用加权质心算法获得定位测量结果,利用卡尔曼滤波算法进行定位信息最优化计算,以消除基于单一测量技术存在数据漂移、测试信号受遮挡、电磁干扰造成的误差,获得准确的定位信息,进而实现作业轨迹的实时跟踪以及作业面积的有效核算。在Matlab仿真分析中,首先建立定位算法评价指标以便于定位效果评估,通过仿真计算得出:基于多源数据融合的优化定位算法的定位精度及稳定性均优于单一无线RSSI室内定位算法。温室大棚田间试验的实际测试结果表明,室内定位精度不大于0.125 m,定位误差小于0.4%,能够较好地满足温室内作业机具的定位及作业轨迹实时获取与监测的需求。

Greenhouse Agricultural Machinery Indoor Positioning Method

The agricultural machinery positioning, dynamic tracking and working area calculation in greenhouse were focused on, and an optimum indoor positioning algorithm was proposed based on multi-source data fusion theory. The above method firstly relied on inertia navigation measurement technology to estimate initial value of target positioning; then wireless RSSI ranging technology and weighted centroid algorithm were adopted to obtain positioning measurements; and optimum calculation was conducted with Kalman filtering algorithm to eliminate errors in the end, which were common in present measurement using single measurement technology and caused by data drift, signal obstruction, electromagnetic interference and other factors, in order to acquire accurate positioning information, realize real-time dynamic tracking and calculate working area. In Matlab simulation analysis, the positioning algorithm evaluation indexes were established to assess positioning effect and it was found that the proposed algorithm had better positioning accuracy and stability than single wireless RSSI indoor positioning algorithm. In the field test, the results showed that indoor positioning accuracy was not more than 0.125m and error was less than 0.4%, which could effectively meet demands of agricultural machinery positioning and real-time dynamic tracking in greenhouse.