【摘要】 使用新型卡尔曼滤波对风速、风向测量结果进行数据校正。借助一组样本点发挥非线性函数作用，使风速、风向垂直分布状态的估计结果更准确，经过若干次迭代，逐步降低合成风速、风向的测量误差。实验结果表明，在不同距离范围内，该方法能保证风速、风向测量误差的均值不变且近似于0，误差的标准差降低60%以上。更多还原

【Abstract】 For the vertical distribution of wind speed and direction in front of wind turbines can not be accurately described by linear wind shear model, the measurement of wind filed reconstruction using four-beam lidar is prone to measurement errors. In order to solve this problem and take advantage of the simplicity and low cost of this method, A novel Kalman filtering was proposed to correct the measured data of wind speed and direction measurements. This method estimates the wind speed and direction accurately using a set of sample points to approximate the nonlinear function. After several iteration, this method reduce the measured error of wind speed and direction gradually. Experimental results show that, the novel method ensures the mean of measured error of wend speed and direction is close to zeros, and the standard deviation of the error is reduced by 60%.更多还原