本文選題：加權總體最小二乘WLTS + 總體最小二乘平差EIV模型��； 參考：《測繪科學》2017年04期

【摘要】：針對加權總體最小二乘平差模型中系數(shù)矩陣具有結構性的問題,該文設計了一種顧及系數(shù)矩陣結構性的加權總體最小二乘迭代解法:首先,利用非線性最小二乘平差方法將總體最小二乘模型線性化;然后,采用結構矩陣的方法顧及系數(shù)矩陣的重復元素和常數(shù)項,通過間接平差的原理推導了顧及系數(shù)矩陣結構性的加權總體最小二乘迭代公式,可適用于加權總體最小二乘的參數(shù)估計;最后,通過算例分析并與其他算法進行比較,驗證了該算法的有效性和可行性。
[Abstract]:In order to solve the structural problem of coefficient matrix in weighted population least square adjustment model, a weighted global least squares iterative solution considering the structure of coefficient matrix is designed in this paper.The nonlinear least square adjustment method is used to linearize the total least squares model, and the structural matrix is used to take into account the repeated elements and constant terms of the coefficient matrix.Based on the principle of indirect adjustment, the iterative formula of weighted population least squares considering the structure of coefficient matrix is derived, which can be used to estimate the parameters of weighted population least squares.The validity and feasibility of the algorithm are verified.
【作者單位】： 湖南軟件職業(yè)學院;湘潭大學能源工程學院;
【基金】：湖南省教育廳科研項目(15C0741,13C903)
【分類號】：P207

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1 曾文憲;系數(shù)矩陣誤差對EIV模型平差結果的影響研究[D];武漢大學;2013年

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本文編號：1740508