This paper proposes an efficient multi-sensor system to complement GNSS (Global Navigation Satellite System) for improved positioning in urban area. The proposed system augments GNSS by low-cost MEMS IMU (Micro Electro Mechanical Systems Inertial Measurement Unit), OBD (On-Board Diagnostics)-II, and digital altimeter modules. For improved availability of time synchronization in urban area, an adaptive synchronization method is proposed to combine the external PPS (Pulse Per Second) signal and the internal onboard clock. For improved positioning accuracy and availability, a 17-state Kalman filter is formulated for efficient multi-sensor fusion, including OBD-II and digital altimeter modules. A strategy to apply different types of measurement updates is also proposed for improved performance in urban area. Four experiment results with field-collected measurements evaluates the performance of the proposed GNSS/IMU/OBD-II/altimeter system in various aspects, including accuracy, precision, continuity, and availability.
For improved positioning in urban canyons, this study proposes an efficient dual‐filter method integrating multi‐constellation global navigation satellite system (GNSS), an inertial navigation system (INS), a barometric altimeter and an on‐board diagnostics (OBD) module. The proposed method consists of a position‐domain (PD) Hatch filter and velocity Kalman filter. The Hatch filter is operated as the main positioning filter and the Kalman filter is operated as a sub‐filter to aid the main filter for the occasional GNSS outages. The Hatch filter combines barometric altimeter measurements with GNSS pseudorange and carrier phase measurements. The Kalman filter integrates the inertial measurement unit (IMU) measurements with GNSS Doppler shift and OBD speed measurements. A semi‐simulation method is applied for the accurate performance evaluation of the proposed method compared with the several conventional methods under deficient satellite visibility, multipath errors and cycle slips caused by urban canyons. By the test results, it is demonstrated that the proposed method can bound the positioning errors effectively by utilising low‐cost all‐weather sensors. The RMSEs of the proposed Kalman–Hatch dual‐filter algorithm were shown as 0.05, 0.19 and 0.11 m, respectively, and the maximum errors were shown as 0.16, 0.26 and 0.40 m, respectively, in the east, north and upward directions.
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