Mitigating the Near-Far Interference Problem in the GNSS Space Applications

Abstract

Nowadays missions at Geostationary (GEO) altitude mainly use traditional ranging for orbit determination and satellite control. The utilization of GNSS receivers on-board a GEO platform is becoming an attractive alternative for position and timing determination, mostly for ground operations minimization and associated cost benefits. To increase competitiveness, electric propulsion to perform or complement the transfer manoeuvres that allow the spacecraft to reach GEO orbit will be employed [15]. This imposes long duration manoeuvres (typically 3–6 months of quasi-continuous thrust) and will result in orbits that intersect several times the “GNSS Sphere”. The GNSS-based navigation performance in these kinds of orbits are strongly influenced by the potentially high GNSS signal power dynamics, by interference from different signals transmitted by the same system (so called intra-system interference) and interference from signals transmitted by other systems (known as inter-system interference). Therefore, the well-known “near-far problem” and the multiple access interference (MAI) may affect significantly the signal acquisition and tracking capabilities, especially when High-Sensitivity GNSS receivers (HS-GNSS) must be employed, constrained by specific missions’ targets (e.g. solution availability and continuity). In this paper, a suitable mitigation technique against the near-far problem and MAI for GNSS Space applications is presented and discussed, taking into account the constraints of nowadays state-of-art GNSS Spaceborne Receiver architecture in the context of GEO-Transfer Orbit (GTO) or Medium-Altitude Transfer Orbit (MTO). The technique is applicable also to Highly Elliptical Orbits (HEO) or all the orbits that intersect the GNSS Sphere. A discussion on the hardware/software implementation strategy is provided. A set of simulations that consider the specificity of the GNSS Space Service Volume (SSV), in terms of expected signal and power dynamics, are carried out and results providing statistics on the effectiveness of the method are shown. The simulation and the input datasets containing crosscorrelation (CC) events have been obtained through a GNSS Bit-True Signal Simulator in MATLAB that will be briefly described hereafter.