Study on Innovation Adaptive EKF for In-Flight Alignment of Airborne POS

The Position and Orientation measurement System (POS) is a dedicated Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) integrated system for airborne remote sensing. In-flight alignment (IFA) is an effective way to improve the accuracy and speed of initial alignment for an airborne POS. During IFA, the GPS provides the position and velocity references for the SINS, so the alignment accuracy will be degraded by unstable GPS measurements. To improve the alignment accuracy under unstable GPS measurement, an adaptive filtering algorithm of an extended Kalman filter (EKF) combined with innovation-based adaptive estimation is proposed, which introduces the calculated innovation covariance into the computation of the filter gain matrix directly. Then, this innovation adaptive EKF algorithm is used for the IFA of the POS with a large initial heading error. Moreover, it is optimized by blocked matrix multiplication to reduce the computational burden and improve the real-time performance. To validate the proposed algorithm, the car-mounted IFA experiment is carried out for the prototype of the airborne POS (TX-D10) under a turning maneuver, taking Applanix's POS/AV510 as a reference and changing the GPS measurement artificially. The experiment results demonstrate that the proposed algorithm can reach a better alignment accuracy than the EKF under unknown GPS measurement noises.

[1]  O.A. Stepanov,et al.  Nonlinear filtering methods application in INS alignment , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Charles K. Toth.  Sensor integration in airborne mapping , 2002, IEEE Trans. Instrum. Meas..

[3]  Robert Sutton,et al.  Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system , 2004 .

[4]  Wook Hyun Kwon,et al.  Comments on "Optimal solution of the two-stage Kalman estimator , 2002, IEEE Trans. Autom. Control..

[5]  Kwangjin Kim,et al.  In-Flight Alignment Algorithm based on Non-Symmetric Unscented Transformation , 2006, 2006 SICE-ICASE International Joint Conference.

[6]  R. Mehra.  On the identification of variances and adaptive Kalman filtering , 1970 .

[7]  Bian Hong-wei,et al.  IAE-adaptive Kalman filter for INS/GPS integrated navigation system 1 1 This project was supported by the National Natural Science Foundation of China (40125013 & 40376011). , 2006 .

[8]  I. Bar-Itzhack,et al.  Observability analysis of piece-wise constant systems. II. Application to inertial navigation in-flight alignment (military applications) , 1992 .

[9]  B. M. Scherzinger.  Inertial navigator error models for large heading uncertainty , 1996, Proceedings of Position, Location and Navigation Symposium - PLANS '96.

[10]  Eun-Hwan Shin,et al.  An unscented Kalman filter for in-motion alignment of low-cost IMUs , 2004, PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556).

[11]  Jang Gyu Lee,et al.  A leveling algorithm for an underwater vehicle using extended Kalman filter , 1998, IEEE 1998 Position Location and Navigation Symposium (Cat. No.98CH36153).

[12]  Jiancheng Fang,et al.  Predictive Iterated Kalman Filter for INS/GPS Integration and Its Application to SAR Motion Compensation , 2010, IEEE Transactions on Instrumentation and Measurement.

[13]  Gunnar Prytz,et al.  Characterization of Accelerometers Using Nonlinear Kalman Filters and Position Feedback , 2007, IEEE Transactions on Instrumentation and Measurement.

[14]  Fumitoshi Matsuno,et al.  A Neuro-Fuzzy Assisted Extended Kalman Filter-Based Approach for Simultaneous Localization and Mapping (SLAM) Problems , 2007, IEEE Transactions on Fuzzy Systems.

[15]  M. Mostafa,et al.  DIRECT POSITIONING AND ORIENTATION SYSTEMS HOW DO THEY WORK? WHAT IS THE ATTAINABLE ACCURACY? , 2001 .

[16]  Yudan Yi,et al.  On improving the accuracy and reliability of GPS/INS-based direct sensor georeferencing , 2007 .

[17]  A. Shademan,et al.  Sensitivity analysis of EKF and iterated EKF pose estimation for position-based visual servoing , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[18]  Hugh F. Durrant-Whyte,et al.  Development of a nonlinear psi-angle model for large misalignment errors and its application in INS alignment and calibration , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[19]  Martin P. Mintchev,et al.  Observability Analysis for INS Alignment in Horizontal Drilling , 2007, IEEE Transactions on Instrumentation and Measurement.