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Optimal estimation of a subset of integers with application to GNSS

Brack A.

Artificial Satellites : Journal of Planetary Geodesy

2016

Vol. 51, No. 4

123--134

The problem of integer or mixed integer/real valued parameter estimation in linear models is considered. It is a well-known result that for zero-mean additive Gaussian measurement noise the integer least-squares estimator is optimal in the sense of maximizing the probability of correctly estimating the full vector of integer parameters. In applications such as global navigation satellite system ambiguity resolution, it can be beneficial to resolve only a subset of all integer parameters. We derive the estimator that leads to the highest possible success rate for a given integer subset and compare its performance to suboptimal integer mappings via numerical studies. Implementation aspects of the optimal estimator as well as subset selection criteria are discussed.

Least-squares collocation with integer parameters

Teunissen P. J. G.

Artificial Satellites : Journal of Planetary Geodesy

2006

Vol. 41, No. 2

59--66

The prediction of spatially and/or temporal varying variates based on observations of these variates at some locations in space and/or instances in time, is an important topic in the various spatial and Earth sciences disciplines. This topic has been extensively studied, albeit under different names. In Geodesy it is generally referred to as least-squares collocation. The underlying model used is often of the trend-signal-noise type. This model is quite general and it encompasses many of the conceivable measurements. However, the methods of prediction based on these models have only been developed for the case the trend parameters are real-valued. In the present contribution we generalize the theory of least-squares collocation by permitting some or all of the trend parameters to be integer valued. We derive the solution of integer-based least-squares collocation and show how it compares to the solution of standard least-squares collocation.