Observation Space Representation Refinement Algorithm for Real-Time GNSS in Unilateral Obstruction Scenarios

The Position information is essential for large-scale Internet of Things (IoT) devices and services. Multipath and non-line of sight (NLOS) effects introduce additional delays in pseudorange measurements in urban areas. It is one of the main unmodeled errors in Global Navigation Satellite Systems (GNSS). To mitigate interference, various techniques have been developed, including antenna design and sensor fusion. However, traditional estimation approaches often produce biased estimates under the additional path delays. To improve estimation accuracy and robustness, we present an Observation Space Representation Refinement (OSRR) algorithm. The initial position is estimated by least squares without the additional path error. Then, the multipath projection method is used to get possible compensation in pseudorange measurements. Subsequently, the Moving Horizontal Estimation (MHE) is leveraged to get the position with corrected observation space. Field experiments demonstrate that the proposed OSRR algorithm significantly reduces the impact of interference on positioning accuracy. There is no empirical constraint to easily adapt to real-time static and kinematic GNSS pseudorange positioning with unilateral obstruction scenarios.