Trust-driven adaptive weighting for robust GNSS state Estimation under interference condition

Abstract

Reliable state estimation in Global Navigation Satellite Systems (GNSS) becomes challenging when measurement statistics are distorted by signal interference. Jamming, spoofing, multipath propagation, and environmental disturbances may significantly increase residual variance and introduce biases in pseudorange observations, which leads to degradation of navigation accuracy and loss of estimation reliability. Conventional robustness mechanisms typically rely on threshold-based fault detection, residual monitoring, or classical robust estimators. Although such approaches mitigate large outliers, they often employ binary measurement exclusion or purely statistical reweighting and therefore do not explicitly account for the reliability of individual measurement channels. This paper proposes a trust-driven adaptive weighting framework for GNSS state estimation under interference conditions. The method introduces a dynamic trust coefficient assigned to each measurement channel based on statistical quality assessment and risk-aware evaluation of its potential impact on the navigation solution. Residual variance is used to quantify measurement stability, while a channel risk factor incorporates geometric sensitivity and anomaly likelihood. The resulting trust coefficients regulate measurement influence through adaptive modification of the measurement covariance matrix within the Extended Kalman Filter.