农机BDS/INS组合导航算法研究

针对农机在环境干扰情况下BDS失调、INS单独导航误差大的不足,以BDS/INS两者间位置、速度差值为模型,提出蜂群算法优化BP神经网络辅助卡尔曼滤波的算法组合导航,精准定位农机位置速度信息。BDS有效时,BDS/INS组合导航结合卡尔曼滤波,校正INS的位置、速度偏差,BP神经网络进行训练模式;BDS失调时,BP神经网络转化为预测模式,代替BDS对INS进行校正反馈,输出精确位置信息。同时,改进人工蜂群的搜寻方式与选择概率,提出改进的蜂群算法优化BP神经网络,使速度误差小于0.6 m/s,位置误差小于30 m,改善对农机位置速度的定位精确度。

Research on BDS/INS integrated navigation algorithm for agricultural machinery

Aiming at the shortcomings of BDS maladjustment and large INS single navigation error of agricultural machinery under environmental interference,based on the model of position and speed difference between BDS/INS,a bee colony algorithm to optimize BP neural network-assisted Kalman filtering algorithm combined navigation is proposed.Accurately locate the position and speed information of agricultural machinery.When BDS is valid,BDS/INS integrated navigation combined with Kalman filtering corrects the position and speed deviation of INS,and the BP neural network performs the training mode;when the BDS is out of adjustment,the BP neural network is converted into the prediction mode,instead of BDS;replace BDS to correct INS feedback and output accurate position information.At the same time,the search method and selection probability of artificial bee colonies are improved,and an improved bee colony algorithm is proposed to optimize the BP neural network so that the speed error is less than 0.6 m/s and the position error is less than 30 m,which improves the positioning accuracy of agricultural machinery position and speed.