1 Markuze Yu.I.
2 Le Anh Cuong
3 Nguyen Thi Thu
4 Dinh Hai Nam

Moscow State University of Geodesy and Cartography (MIIGAiK)


Hanoi University of Mining and Geology

Rough errors are a consequence of the observer’s miscalculations, device malfunctions, their displacements at the time of measurement, incorrect measurement techniques chosen, rapid and sharp deterioration of external conditions and other causes. That is why, one of the problems in the theory of mathematical processing geodetic measurements is culling measurements containing gross errors. In the last two decades received recursion equalization has been widespread. It enables evaluating the unknown while new measurements are added to the network and measurements with gross errors are deleted [3, 4]. In the article, a complementary recurrent equalization is developed. It is original, convenient and recommended for wide application in production.
1.   Markuze Yu.I. Algoritmy dlja uravnivanija geodezicheskih setej na EVM [Algorithms for equalizing geodetic networks on computers]. M.: Nedra, 1989, 248 p.
2.   Markuze Ju. I. Obobshchennyj rekurrentnyj algoritm uravnivaniya svobodnyh i nesvobodnyh geodezicheskih setej s lokalizatsiej grubyh oshibok. Izvestia vuzov. Geodesy and Aerophotosurveying, 2000, no. 1, pp. 3–16.
3.   Markuze Ju. I. Osnovy uravnitel'nyh vychislenij: Ucheb. posobie dlya vuzov. Moskva: Nedra, 1990, 240 p.
4.   Markuze Yu.I., Golubev V.V. Teoriya matematicheskoj obrabotki geodezicheskih izmerenij: Ucheb. Posobie dlya vuzov. Pod obshch. red. Yu. I. Markuze. M.: Akademicheskij Proekt Al'ma Mater, 2010, 247 p.
5.   Markuze Yu.I., Le Anh Cuong, Tran Tien Rang (2016) The research of an initial matrix of the return scales of unknown at a recurrent way. Geodezia i Kartografia, (11), pp. 7–10. (In Russian). DOI: 10.22389/0016-7126-2016-917-11-7-10.
Markuze Yu.I., 
Le Anh Cuong, 
Nguyen Thi Thu, 
Dinh Hai Nam, 
(2018) Monitoring coarse measurement errors and initial data. Geodesy and cartography = Geodezia i Kartografia, 79(7), pp. 11-16. (In Russian). DOI: 10.22389/0016-7126-2018-937-7-11-16