INSTITUTO DE INVESTIGACIONES EN ELECTRONICA, CONTROL Y PROCESAMIENTO DE SEÑALES
Unidad Ejecutora - UE
A Bayesian Technique for Real and Integer Parameters Estimation in Linear Models and Its Application to GNSS High Precision Positioning
P. A. RONCAGLIOLO; C. H. MURAVCHIK; JAVIER GARCIA
IEEE TRANSACTIONS ON SIGNAL PROCESSING
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Lugar: Albuquerque; Año: 2016 vol. 64 p. 923 - 933
A novel Bayesian technique for the joint estimation of real and integer parameters in a linear measurement model is presented. The integer parameters take values on a finite set, and the real ones are assumed to be a Gaussian random vector. The posterior distribution of these parameters is sequentially determined as new measurements are incorporated. This is a mixed distributionwith a Gaussian continuous part and a discrete one. Estimators for the integer and real parameters are derived from this posterior distribution. A Maximum A Posteriori (MAP) estimator modified with the addition of a confidence threshold is used for the integer part and a Minimum Mean Squared Error (MMSE) is used for the real parameters. Two different cases are addressed: i) both real and integer parameters are time invariant and ii) the integer parameters are time invariant but the real ones are time varying. Our technique is applied to the GNSS carrier phase ambiguity resolution problem, that is key for high precision positioning applications. The good performance of the proposed technique is illustratedthrough simulations in different scenarios where different kind of measurements as well as different satellite visibility conditions are considered. Comparisons with state-of-the-art ambiguity solving algorithms confirm performance improvement. The new method is shown to be useful not only in the estimation stage but also for validating the estimates ensuring a predefined success ratethrough proper threshold selection.