低信号下利用回归模型提高雷达测速精度方法

Method of Improving Radar Velocity Measurement Accuracy by Using a Regression Model under Low Signal Conditions

靶场试验中,初速测量雷达是测试多种弹丸炮口初速的重要工具。但在某型火炮试验中,两台雷达同时参试,某台雷达受到弹丸自身或其他因素的影响,获取的弹丸径向速度并不完整,导致该雷达计算出的初速数据与径向速度相对完整雷达的数据误差超出了正常的误差要求。为此,选择在等时间间隔加载数据点的基础上,利用回归模型预测出缺失的弹丸径向速度,等补齐所有数据以后再拟合递推出弹丸的初速数据。将采用该方法计算出的回归初速数据与信号完整雷达的实测初速数据进行对比分析,发现两者的误差明显小于两台初速雷达原始实测值的误差,表明该方法能够提高雷达在低信号下的测速精度。

In range tests,the muzzle velocity measurement radar is an important tool to measure muzzle velocities of various projectiles.However,in the test of a certain type of gun,there were two radars participating in the test at the same time,and one of them was affected by the projectile itself or other factors,so the obtained projectile radial velocity data was not complete,which leads to the error between the muzzle velocity data calculated by this radar and the other radar which has relatively complete radial velocity data.The error exceeds the normal error requirements.Therefore,on the basis of loading the data points with equal time intervals,the missing radial velocities of the projectile were predicted by a regression model,and the muzzle velocity data of the projectile can be obtained by fitting after all data were restored.Comparing the regression velocity data calculated by this method with the measured muzzle velocity data of the radar with complete signals,it is found that the error between the two methods is obviously less than that of the originally measured values of the two muzzle velocity radars,which indicates that the method can improve the accuracy of radar velocity measurement under low signal conditions.