论文标题
来自流星相特征的雷达横向散射速度的精确测量
Precision measurements of radar transverse scattering speeds from meteor phase characteristics
论文作者
论文摘要
我们描述了一种改进的技术,用于在镜头点($ t_ {0} $)之前使用流星雷达回声测量的反向散射相($ t_ {0} $)来计算流星速度及其不确定性。我们的方法建立在Cervera等人(1997)的早期工作的基础上,它在菲涅尔距离中扫描了可能的速度 - 带有动态滑动窗口的时域,并从最佳的速度分布中得出了最佳的估计。我们测试方法的性能,通过滑动斜线技术(PSSST)称为pre-t_ {0} $速度,在中间大气中的Alomar Radar System(Maarsy)和加拿大的Metor Orbit Radar(CMOR)观察到的横向分散的流星回声(CMOR),并将结果与最终的速度变换估计。与使用其他技术相比,我们的新技术被证明可以产生良好的结果。我们表明,我们的速度精度为$ \ pm $ 5 $ \%$,速度少于40 km/s,我们发现所有CMOR多站回声中的90美元以上$ \%$ \%$均具有PSSST解决方案。对于CMOR数据,PSSST可与选择临界相值和较差的相位拆开相关。选择高达$ \ $ pm $ 6脉冲的流星速度少于50 km/s的错误产生的误差小于$ \ pm $ \ pm $ 5 $ \%$ \%$ \%$ $ \%$。此外,PSSST速度内核密度估计值(KDE)的宽度被用作自然衡量不确定性的量度,可以捕获噪声和$ T_0 $选择不确定性。
We describe an improved technique for using the backscattered phase from meteor radar echo measurements just prior to the specular point ($t_{0}$) to calculate meteor speeds and their uncertainty. Our method, which builds on earlier work of Cervera et al (1997), scans possible speeds in the Fresnel distance - time domain with a dynamic, sliding window and derives a best-speed estimate from the resultant speed distribution. We test the performance of our method, called pre-$t_{0}$ speeds by sliding-slopes technique (PSSST), on transverse scattered meteor echoes observed by the Middle Atmosphere Alomar Radar System (MAARSY) and the Canadian Meteor Orbit Radar (CMOR), and compare the results to time-of-flight and Fresnel transform speed estimates. Our novel technique is shown to produce good results when compared to both model and speed measurements using other techniques. We show that our speed precision is $\pm$5$\%$ at speeds less than 40 km/s and we find that more than 90$\%$ of all CMOR multi-station echoes have PSSST solutions. For CMOR data, PSSST is robust against the selection of critical phase value and poor phase unwrapping. Pick errors of up to $\pm$6 pulses for meteor speeds less than about 50 km/s produce errors of less than $\pm$5$\%$ of the meteoroid speed. In addition, the width of the PSSST speed Kernel density estimate (KDE) is used as a natural measure of uncertainty that captures both noise and $t_0$ pick uncertainties.