A Novel Fault Detection and Exclusion Method for Applying Low-Cost INS/GNSS Integrated Navigation System in Urban Environments

Achieving higher accuracy in positioning at lower cost in urban environments is a fundamental requirement in modern intelligent transportation, autonomous driving, etc. The integration of low-cost inertial navigation system (INS) and global navigation satellite system (GNSS) is a promising choice to meet this objective, however, it still requires a suitable fault detection and correction method. Inspired by the structure of multi-channel parallelism in vector tracking, this paper introduces a multi-channel parallel processing (MCPP) method for identifying and eliminating GNSS faulty signals in the measurement domain in tightly integrated structure, aided by the principle of multiple model interaction. At each positioning epoch in each channel, the MCPP method individually compares the difference between each satellite’s measurement and its INS estimation version. These differences are normalized by a multiple model interaction process to derive the probability that each satellite is error free. These probabilities, linked to each satellite, can be used as weights for the optimal output or as indicators to identify false signals. The road test results in the Zhongguancun area of Beijing demonstrate that the method’s performance is comparable to that of state-of-the-art methods and avoids the unwanted outcome of selecting a fixed error elimination threshold that leads the system to diverge in a distinct environment or positioning mode. The results were valid in both tightly coupled (TC) and weighted least square (WLS) navigation modes, suggesting that the method can be utilized for single vehicle as well as vehicle-to-vehicle or vehicle-to-road cooperative positioning.