线性最小方差估计用于SAR干涉图大气延迟改正
Linear Minimum Mean Square Error Estimation for Wet Delay Correction in SAR Interferogram
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摘要: 将线性最小方差估计(linear minimum mean square error,LMMSE)引入SAR干涉图大气延迟改正,并用模拟实验比较了地势平坦地区LMMSE与以往常用于SAR干涉图大气延迟改正的距离权倒数法(inverse distance weighted averaging,IDWA)和普通克里格算法(ordinary Kriging,KRG)的插值效果。同时,还比较了地势起伏较大地区,考虑了高差因素的LMMSE和KRG算法、未考虑高差因素的LMMSE、KRG算法以及IDWA的插值效果。研究结果表明,对于地势平坦地区,在各种精度情况下,当已知点呈随机分布时,点数越少,LMMSE的插值效果的优势越明显;对于地势起伏较大地区,考虑了高差因素的LMMSE的插值效果最佳。Abstract: Linear minimum mean square error(LMMSE) estimation is introduced for atmospheric delay correction in SAR interferogram.In plain area,the performance of LMMSE is compared with inverse distance weighted averaging(IDWA) and ordinary Kriging(KRG),commonly used for wet atmospheric correction in SAR interferogram,using simulated data.According to the simulation experiment result,LMMSE is better than IDWA and KRG,particularly for sparse randomly distributed known points even the accuracy of known points is bad.And in mountainous area,the performance of LMMSE,KRG considering height difference is compared with LMMSE,KRG not considering height difference and IDWA.According to the simulation experiment result,the LMMSE interpolator considering height difference has the best performance in interpolating atmospheric delay observations in mountainous area.
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