Mixed integer programming for the resolution of GPS carrier phase ambiguities

TL;DR

This paper presents advanced integer programming techniques for resolving GPS carrier phase ambiguities, improving accuracy and efficiency in high-precision geodetic positioning by incorporating reduction and reformulation methods.

Contribution

It introduces a one-step nonexact approach using Gaussian decompositions and reformulates the problem into a 0-1 linear integer program for exact solutions.

Findings

Improved ambiguity resolution accuracy over simple rounding methods

Efficient reformulation enabling robust integer programming solutions

Theoretical results on decorrelation via unimodular transformation

Abstract

This arXiv upload is to clarify that the now well-known sorted QR MIMO decoder was first presented in the 1995 IUGG General Assembly. We clearly go much further in the sense that we directly incorporated reduction into this one step, non-exact suboptimal integer solution. Except for these first few lines up to this point, this paper is an unaltered version of the paper presented at the IUGG1995 Assembly in Boulder. Ambiguity resolution of GPS carrier phase observables is crucial in high precision geodetic positioning and navigation applications. It consists of two aspects: estimating the integer ambiguities in the mixed integer observation model and examining whether they are sufficiently accurate to be fixed as known nonrandom integers. We shall discuss the first point in this paper from the point of view of integer programming. A one-step nonexact approach is proposed by employing minimum diagonal pivoting Gaussian decompositions, which may be thought of as an improvement of the simple rounding-off method, since the weights and correlations of the floating-estimated ambiguities are fully taken into account. The second approach is to reformulate the mixed integer least squares problem into the standard 0-1 linear integer programming model, which can then be solved by using, for instance, the practically robust and efficient simplex algorithm for linear integer programming. It is exact, if proper bounds for the ambiguities are given. Theoretical results on decorrelation by unimodular transformation are given in the form of a theorem.