Differential carrier phase observations from GPS (Global Positioning System) integrated with high-rate sensor measurements, such as those from an inertial navigation system (INS) or an inertial measurement unit (IMU), in a tightly coupled approach can guarantee continuous and precise geo-location information by bridging short outages in GPS and providing a solution even when less than four satellites are visible. However, to be efficient, the integration requires precise knowledge of the lever arm, i.e. the position vector of the GPS antenna relative to the IMU. A previously determined lever arm by direct measurement is not always available in real applications; therefore, an efficient automatic estimation method can be very useful. We propose a new hybrid derivative-free extended Kalman filter for the estimation of the unknown lever arm in tightly coupled GPS/INS integration. The new approach takes advantage of both the linear time propagation of the Kalman filter and the nonlinear measurement propagation of the derivative-free extended Kalman filter. Compared to the unscented Kalman filter, which in recent years is typically used as a superior alternative to the extended Kalman filter for nonlinear estimation, the virtue of the new Kalman filter is equal estimation accuracy at a significantly reduced computational burden. The performance of the new lever arm estimation method is assessed with simulated and real data. Simulations show that the proposed technique can estimate the unknown lever arm correctly provided that maneuvers with attitude changes are performed during initialization. Field test results confirm the effectiveness of the new method.
Wim De Wilde (M.Sc. in EE) works as a system architect, with focus on the RF and digital signal processing section. Dr. Gert Cuypers (M.Sc. and Ph.D in EE) works on the receiver RF front-end and antennas. Dr. Jean-Marie Sleewaegen (M.Sc. and Ph.D in EE) is responsible for the GNSS signal processing, system architecture and technology development. Dr. Richard Deurloo (M.Sc. in Aerospace Engineering and Ph.D. in Surveying Engineering) works on high-precision GNSS and GNSS/INS integration algorithms.
Strapdown airborne gravimetry relies on the combination of an inertial measuring unit (IMU) and a global navigation satellite system (GNSS) to measure the Earth's gravity field. Early results with navigation-grade IMUs showed similar accuracies to those obtained with scalar gravimetric systems in the down component. This paper investigates the accuracy of three IMUs used for strapdown airborne gravimetry under the same flight conditions. The three systems considered were navigation-grade IMUs, iXSea AIRINS and iMAR iNAV-FMS, and a tactical-grade Litton LN-200. The data were collected in 2010 over the Island of Madeira, Portugal, in the scope of GEOid over MADeira campaign. The coordinates and orientation of the aircraft were computed using an extended Kalman filter based on the inertial navigation approach. GNSS position and velocity observations were used to update the filter, and the gravity disturbance was considered to be a stochastic process and was part of the state vector. A new crossover point-based serial tuning was introduced to deal with the uncertainty of choosing the filter's a priori information. The results show that with the iXSea accuracies of 2.1 and 1.6 mGal can be obtained for 1.7 and 5.0 km of spatial resolution (half-wavelength), respectively. iMAR's results were significantly affected by a nonlinear drift, which led to lower accuracies of 4.1-5.5 mGal. Remarkably, Litton showed very consistent results and achieved an accuracy of about 4.5 mGal at 5 km of spatial resolution (half-wavelength).
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