Assessing receiver noise using GPS short baseline time series

Abstract

In this contribution, we focus on both the functional and stochastic models of GPS short baseline time series. Biases in the observations can be interpreted as due to an incomplete functional model. Multipath, as a major part of errors, is believed to induce periodic effects on the carrier-phase observations over short time spans (a few minutes). Here, we employ a harmonic estimation method to include a set of harmonic functions in the functional model. Such sinusoidal functions are introduced to compensate for periodic systematic effects in GPS short baselines time series. This guarantees the property of unbiasedness of the least-squares estimators. On the other hand, the covariance matrix of observables is, in practice, generally based on the supposition of uncorrelated observables. A realistic description of the measurement noise characteristics, through the observation covariance matrix, is required to yield minimum variance (best) estimators. We will use least-squares variance component estimation to assess time-correlated noise of GPS receivers. Receiver noise characteristics are traditionally assessed through special zero baseline measurements. With the technique introduced in this paper we demonstrate that we can reach the same conclusions using (ordinary) short baseline measurements.