A Multi-LEO-SOP/INS Integrated Positioning Method and Performance Analysis

Abstract

Positioning using low-Earth orbit satellite signals of opportunity (LEO-SOP) is an important backup technology in Global Navigation Satellite System (GNSS)-denied environments. However, limited usable observations and uneven directional constraints from a single-constellation hinder continuous high-accuracy dynamic positioning. This study develops a multi-LEO-SOP/INS integrated positioning framework using real Iridium, ORBCOMM, and Globalstar signals. To enable cross-constellation comparison of Doppler-based position observability, a constellation-specific normalization factor is introduced into multiepoch local observability (MELO), yielding the multi-constellation MELO (MC-MELO) metric for evaluating directional constraints across constellations and satellite arcs. Based on MC-MELO, a dynamically weighted robust sequential Kalman filter (MC-MELO-RSKF) is developed to mitigate the adverse effects of inaccurate preset noise covariances, limited state correction under weak observability, and anomalous observations in practical multisystem integration. In this framework, MC-MELO adjusts the measurement covariance according to the local geometric constraints, while an arc-statistics-based dynamic-threshold IGG-III model suppresses outliers. Vehicular experiments show that three-constellation integration outperforms single- and dual-constellation solutions, while the proposed method further improves positioning accuracy and robustness.