基于雷达组合多模型联合火箭弹径向速度预测

Radial Velocity Prediction of Combined Rocket Based on Radar Combination Multiple Model

为解决连续波雷达测试火箭弹主动段径向速度有时出现缺失的问题,选择把共同参试的弹道测量雷达测试的坐标作为特征向量,径向速度作为目标向量,将两台雷达数据进行融合,利用样本1分别建立速度与射程、速度与横偏的一元线性回归模型以及速度与射程、横偏的支持向量回归机模型,然后把样本2作为测试数据,将3个模型的预测值作为特征向量,对应的实测值作为目标向量,建立遗传算法优化LSSVM模型,最后再把样本1和2合并作为训练数据,样本3作为测试数据,将两个线性回归模型和支持向量回归机模型预测值带入到遗传算法优化LSSVM模型中,就得到了遗传算法优化LSSVM预测出的样本3的径向速度,最后再把4个模型对样本3的预测值进行组合,就得到了多模型联合预测值。实验结果表明,多模型联合预测值精度最高,误差为0.065%,小于1‰,达到了连续波雷达测试火箭弹径向速度的误差要求。

In order to solve the problem that the radial velocity of the active phase of the rocket is sometimes missing in the continuous wave radar test,the coordinates of the trajectory measurement radar test jointly participated in the test are selected as the feature vector,and the radial velocity is selected as the target vector.The data of the two radars are fused,and the univariate linear regression model of velocity and range,velocity and transverse deviation,as well as the support vector regression model of velocity and range,transverse deviation are established by using sample 1,Then take sample 2 as the test data,take the predicted values of the three models as the feature vectors,and the corresponding measured values as the target vectors,and establish the genetic algorithm optimized LSSVM model.Finally,combine samples 1 and 2 as the training data,and sample 3 as the test data,and bring the predicted values of the two linear regression models and support vector regression machine models into the genetic algorithm optimized LSSVM model,The radial velocity of sample 3 predicted by LSSVM optimized by genetic algorithm is obtained.Finally,the predicted values of sample 3 are combined by the four models to obtain the joint predicted values of multiple models.The experimental results show that the accuracy of the joint prediction value of multiple models is the highest,with an error of 0.065%,less than 1‰,which meets the error requirements of continuous wave radar for measuring the radial velocity of rockets.