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Automotive Localization Integrity using Proprioceptive and Pseudo-Ranges Measurements

Integrity is an attribute of the quality of performance of a positioning technology that is getting crucial for many Intelligent Transportation Systems dealing with safety. Managing the confidence in estimated locations is difficult when operating in complex outdoors environments, particularly for Global Navigation Satellite Systems (GNSS). A common integrity process consists in removing outliers amongst the measurements using Fault Detection and Exclusion (FDE) algorithms and then to compute Protection Levels (PL) that quantify the integrity zone. This snapshot paradigm is applicable mainly to the aerospace domain where high pseudo-ranges redundancy exists, which is not the case for land applications. In this paper, a new approach to localization integrity for land mobile robot is proposed by combining vehicle and GNSS data, stored within a short-term memory, called data horizon. This formulation allows for the application of FDE algorithms on the combined data set. It also leads to a gain of redundancy that enables PLs to be reduced and to increase the availability of integrity algorithms. Results from evaluation tests are included to demonstrate the validity of the approach.