Wyniki wyszukiwania - BazTech

1

Single-epoch precise positioning using Modified Ambiguity Function Approach

Cellmer S.

Technical Sciences / University of Warmia and Mazury in Olsztyn

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2013

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nr 16(4)

265--280

EN

Single-epoch positioning is a great challenge in recent research related to GNSS data processing. The Modified Ambiguity Function Approach (MAFA) method can be applied to perform this task. This method does not contain a stage of ambiguity resolution. However the final results take into account their integer nature. The functional model of the adjustment problem contains the conditions ensuring the integer nature of the ambiguities. A prerequisite for obtaining the correct solution is a mechanism ensuring appropriate convergence of the computational process. One of such mechanisms is a cascade adjustment, applying the linear combinations of the L1 and L2 signals with the integer coefficients and various wavelengths. Another method of increasing the efficiency of the MAFA method is based on the application of the integer de-correlation matrix to transform observation equations into equivalent, but better conditioned, observation equations. The next technique of improving the MAFA method is search procedure. This technique together with the de-correlation procedure allows to reduce the number of stages of the cascade adjustment and to obtain correct solution even in the case when a priori position is a few meters away from the actual position. This paper presents some problems related to search procedure. The results of single-epoch positioning using improved MAFA method are presented.

2

Wykorzystanie funkcji nieoznaczoności pomiaru fazowego do wyznaczania precyzyjnej pozycji w czasie rzeczywistym

Cellmer S.

Zeszyty Naukowe Politechniki Rzeszowskiej. Budownictwo i Inżynieria Środowiska

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2012

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z. 59, nr 1/II

61-69

PL

W artykule przedstawiono algorytm opracowania obserwacji fazowych GNSS metodą MAFA. W tej metodzie “całkowitoliczbowość” nieoznaczoności pomiaru fazowego jest zapewniona poprzez wprowadzenie równań warunkowych do modelu funkcjonalnego zadania wyrównawczego. W zaproponowanym podejściu nie ma konieczności linearyzacji równań obserwacyjnych. Rozwiązanie poszukiwane jest poprzez minimalizację funkcji celu metodą sympleksu Neldera-Meada. Podjęta została próba wyznaczenia precyzyjnych pozycji na podstawie danych pochodzących z pojedynczych epok obserwacyjnych. Zaprezentowa no wyniki testów przeprowadzonych na rzeczywistych danych obserwacyjnych, pochodzących z pomiaru trzech wektorów o różnych długościach.

EN

In the paper an algorithm of carrier phase GNSS data processing using MAFA method is presented. In this method “integerness” of ambiguity is ensured through including of conditional equations into functional model of the adjustment problem. There is no necesssity to linearize of the observation equations in the proposed approach. The solution is searched through minimisation of the objective function using Nelder-Mead Symplex method. There was made an attempt of single epoch positioning. The tests based on real data were carried out. The results of these tests were presented.

3

Using the integer decorrelation procedure to increase of the efficiency of the MAFA method

Cellmer S.

Artificial Satellites : Journal of Planetary Geodesy

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2011

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Vol. 46, No. 3

103--110

EN

The Modified Ambiguity Function Approach (MAFA) is a method of GNSS carrier phase processing. In this method, the functional model of the adjustment problem contains the conditions ensuring the "integerness" of the ambiguities. These conditions are expressed in the form of differentiable function. A prerequisite for obtaining the correct solution is a mechanism ensuring not only the "integerness" of the ambiguity but also appropriate localization of the search space in the place where the ambiguities have correct values. One of such mechanisms is cascade adjustment, applying the linear combinations of the signals L1 and L2 with the integer coefficients and various wavelengths. This paper presents another, independent from the previous, approach to increase the efficiency of the MAFA method. It is based on the application of the integer decorrelation matrix to transform observation equations into equivalent, but better conditioned, observation equations. The transformation matrix is obtained in the well-known ambiguity variance-covariance matrix integer decorrelation process.