Integer aperture estimation in the presence of biases

Abstract

Ambiguity validation is a quality control step for ambiguity resolution. It is realized by integer aperture (IA) estimator and critical value determination approach. During the past decades, most of research about IA estimator were mainly implemented by numerical simulation. The influence of biases to IA estimation has not been studied. Actually, ambiguity resolution is subject to various kinds of biases in practice, which influences the performance of IA estimation. In this contribution, properties of IA estimators are investigated when they are biased. The probability evaluation formulae for IA estimators with bias-affected are derived and verified. The proper ways to evaluate biased-IA estimators are recommended by numerical experiments. In addition to this, the influences of atmospheric biases to IA estimation are analyzed in different experiments. Those results show that under the same influence of biases, all IA estimators have no better positioning precision than integer estimator, and different IA estimators may lead to different positioning precision. A better choice of IA estimator may lead to less loss of positioning precision. However, if biases can be properly separated, positioning precision of integer and IA estimators can be greatly improved. Hence, the adaptation of biases is more important than the choice of IA estimators in positioning and other applications.