A bias-weighted model for GLONASS ambiguity resolution: from short to medium baselines

Abstract

Abstract Integer ambiguity resolution (IAR) is crucial for fully exploiting the ultra-precise carrier phase measurements in global navigation satellite system positioning. To achieve IAR, phase biases that violate the integer property of ambiguities are typically either estimated together with other model parameters or eliminated through differencing techniques. Observing that these biases can be bounded, exploring the impact of constraining their extreme bounds on IAR performance is a logical next step yet one that has received limited attention in existing research. This study proposes a bias-weighted model that constrains the random variations in GLO..