基于卡尔曼滤波的无人机离地高度估计算法

Height estimation algorithm of UAV based on kalman filter

针对无人机对离地高度有高精度需求,而超声波或毫米波波雷达等距离传感器测量噪声较大的问题,本文提出了基于卡尔曼滤波对两路距离传感器数据融合的最优估计算法。首先,建立了系统状态模型,给出了算法详细执行步骤,分析了算法在DSP6713高速数字信号处理器上的实现特性。然后,进行了算法仿真,通过对比距离传感器测量误差和卡尔曼滤波估计结果,表明本文所设计的算法能有效减小距离测量误差,得到较高精度的离地高度结果,具有一定的工程应用价值。

According to the UAV height accuracy requirement of ground,and millimeter wave radar or ultrasonic distance sensor measurement noise problem,this paper proposes a Kalman filter distance of two optimal sensor data fusion estimation algorithm based on. Firstly,the system state model is established,and the detailed implementation steps of the algorithm are given,and the implementation characteristics of the algorithm on DSP6713 high speed digital signal processor are analyzed. Then,the algorithm simulation,by comparing the distance measurement error and Kalman filtering estimation results show that the design of the algorithm can effectively reduce the error of distance measurement,get high precise height from the results,it has certain engineering application value.