Hybrid derivative-free extended Kalman filter for unknown lever arm estimation in tightly coupled DGPS/INS integration

  • Authors:

  • Yanrui Geng;Richard Deurloo;Luisa Bastos

  • Affiliations:

  • Institute of Systems and Robotics, Faculty of Engineering, University of Porto, Porto, Portugal 4200-465 and Astronomical Observatory, Faculty of Sciences, University of Porto, V.N. Gaia, Portugal ...;Astronomical Observatory, Faculty of Sciences, University of Porto, V.N. Gaia, Portugal 4430-146 and CIIMAR, University of Porto, Porto, Portugal 4050-123;Astronomical Observatory, Faculty of Sciences, University of Porto, V.N. Gaia, Portugal 4430-146 and CIIMAR, University of Porto, Porto, Portugal 4050-123

  • Venue:
  • GPS Solutions
  • Year:
  • 2011

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Abstract

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.