Grid Design for Efficient and Accurate Point Mass Filter-Based Terrain Referenced Navigation

This paper proposes an optimal grid design method for point mass filter-based terrain referenced navigation (PTRN) by considering the process and measurement uncertainties or noise to efficiently obtain accurate results. The estimation performance of the point mass filter (PMF) is generally known to improve when very large support and a high-resolution grid are implemented. However, when this condition is applied to the PTRN, the algorithm cannot be executed in real-time due to the high computational load. In addition, even though the grid condition is improved, the filter accuracy is limited by the given process and measurement noises. Therefore, it is possible to perform efficient and accurate PTRN by finding the minimum number of grid points that can achieve the maximum performance. In this paper, a grid design method is carried out by considering each noise, and the selection logic between the two design results are proposed. By applying the proposed grid design method, it is possible to obtain almost the same accuracy as the results that are obtained when a very high resolution is applied, with a much lower computational load, and it is expected for the highly accurate PTRN to be available in real-time.

[1]  Antonio M. Pascoal,et al.  Robust particle filter formulations with application to terrain‐aided navigation , 2017 .

[2]  Chan Gook Park,et al.  Improvement of terrain referenced navigation using a Point Mass Filter with grid adaptation , 2015, International Journal of Control, Automation and Systems.

[3]  Kjetil Bergh Ånonsen,et al.  An analysis of real-time terrain aided navigation results from a HUGIN AUV , 2010, OCEANS 2010 MTS/IEEE SEATTLE.

[4]  Kamesh Subbarao,et al.  Nonlinear adaptive filtering in terrain-referenced navigation , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[5]  S. Bian,et al.  Research on regional model of continuous Fourier series of marine magnetic anomaly field using for the geomagnetic navigation , 2010, 2010 2nd International Conference on Industrial and Information Systems.

[6]  Baoqi Huang,et al.  A Novel Terrain-Aided Navigation Algorithm Combined With the TERCOM Algorithm and Particle Filter , 2015, IEEE Sensors Journal.

[7]  Jianhu Zhao,et al.  A Study of Underwater Terrain Navigation based on the Robust Matching Method , 2014, Journal of Navigation.

[8]  Gert F. Trommer,et al.  Tightly coupled GPS/INS integration for missile applications , 2004 .

[9]  Guochang Xu,et al.  Derivation of gravity anomalies from airborne gravimeter and IMU recordings—Validation with regional analytic models using ground and satellite gravity data , 2009 .

[10]  F. Gustafsson,et al.  Marginalized Particle Filter for Accurate and Reliable Terrain-Aided Navigation , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Tomonari Furukawa,et al.  Computational modeling for parallel grid-based recursive Bayesian estimation: parallel computation using graphics processing unit , 2013 .

[12]  Ling Zhou,et al.  Terrain aided navigation for long-range AUVs using a new bathymetric contour matching method , 2015, 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[13]  Bo Wang,et al.  An Improved TERCOM-Based Algorithm for Gravity-Aided Navigation , 2016, IEEE Sensors Journal.

[14]  Karim Dahia,et al.  A mixture regularized rao-blackwellized particle filter for terrain positioning , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[15]  L. B. Hostetler,et al.  Nonlinear Kalman filtering techniques for terrain-aided navigation , 1983 .

[16]  David A. Seal,et al.  The Shuttle Radar Topography Mission , 2007 .

[17]  Hyochoong Bang,et al.  Terrain slope estimation methods using the least squares approach for terrain referenced navigation , 2013 .

[18]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[19]  Niclas Bergman,et al.  Recursive Bayesian Estimation : Navigation and Tracking Applications , 1999 .

[20]  Torsten Söderström,et al.  Anticipative grid design in point-mass approach to nonlinear state estimation , 2002, IEEE Trans. Autom. Control..

[21]  J. Hollowell,et al.  Heli/SITAN: a terrain referenced navigation algorithm for helicopters , 1990, IEEE Symposium on Position Location and Navigation. A Decade of Excellence in the Navigation Sciences.

[22]  Torsten Söderström,et al.  Advanced point-mass method for nonlinear state estimation , 2006, Autom..

[23]  Ling Zhou,et al.  Terrain aided navigation for autonomous underwater vehicles with coarse maps , 2016 .

[24]  Hugh F. Durrant-Whyte,et al.  Parallel grid-based recursive Bayesian estimation using GPU for real-time autonomous navigation , 2010, 2010 IEEE International Conference on Robotics and Automation.

[25]  Oddvar Hallingstad,et al.  Terrain Aided AUV Navigation A Comparison of the Point Mass Filter and Terrain Contour Matching Algorithms , 2005 .

[26]  John Weston,et al.  Strapdown Inertial Navigation Technology , 1997 .