Evaluation of a segmented navigation filter approach for vehicle self-localization in urban environment
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Advanced road safety automotive applications require reliable (available) and robust (e.g., GNSS outages) position, velocity and heading information. The velocity information generated by a GNSS receiver, in general, is affected by less errors then the further generated position and ranges. Determination of the measurement weighting for the range information is not in all cases appropriate. Unidentified multipath effects may reduce the quality but do not affect the signal to noise ratio.
This paper explores the concept of a segmented Kalman navigation for a vehicle navigation filter which fuses automotive onboard sensor data. A position filter providing only position information and a dynamic filter covering velocity and sensor error information are implemented in this approach. The dynamic filter is only aided by velocity information provided either by odometer or GNSS. The position filter is aided by the GNSS range information.
The evaluation covers the processing of simulated sensor data and the usage of real time automotive sensor data recorded in scenarios with reduced GNSS quality in urban area.
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