Robust GNSS Positioning Using Unbiased Finite Impulse Response Filter

In a typical GNSS receiver, pseudorange and rate measurements are generated through the code carrier tracking loops, respectively. These then employed to calculate user’s position velocity (PV) solutions, which is typically achieved using Kalman filter (KF) or least squares (LS) algorithm. However, LS method only uses current observation without error analysis. The positioning result greatly affected by errors in observed data. KF, an iterative approach that combines predictions of PV information, more accurate estimates can be obtained because information time-correlated. Meanwhile, its optimal estimate requires both model noise statistics exactly known. Otherwise, achieving optimality cannot guaranteed. To address this issue, paper proposes implements novel solution based on unbiased finite impulse response (UFIR) filter. Two different field tests were conducted. results UFIR compared with those from KF methods, horizon mean improved 44% 29%, respectively, highlights efficacy. offers two primary benefits: it robust uncertainty, leverages historical data within framework provide state.