Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter

Indoor positioning based on wireless local area network (WLAN) signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, the positioning accuracy with this linear model is better than with the traditional models when the initial heading is not known, which is a common situation. The proposed method is computationally light and is also suitable for smoothing. Furthermore, we present modifications to WLAN positioning based on Gaussian coverage areas and show how a Kalman filter using the proposed model can be used for integrity monitoring and (re)initialization of a particle filter.

[1]  Philipp Müller,et al.  A field test of parametric WLAN-fingerprint-positioning methods , 2014, 17th International Conference on Information Fusion (FUSION).

[2]  Helena Leppäkoski,et al.  Pedestrian Navigation Based on Inertial Sensors, Indoor Map, and WLAN Signals , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[4]  Robert Piché,et al.  Gaussian Scale Mixture Models for Robust Linear Multivariate Regression with Missing Data , 2016, Commun. Stat. Simul. Comput..

[5]  A.R. Runnalls,et al.  A Kullback-Leibler Approach to Gaussian Mixture Reduction , 2007 .

[6]  A.H. Haddad,et al.  Applied optimal estimation , 1976, Proceedings of the IEEE.

[7]  François Marx,et al.  Map-aided indoor mobile positioning system using particle filter , 2005, IEEE Wireless Communications and Networking Conference, 2005.

[8]  Martin Klepal,et al.  A Backtracking Particle Filter for fusing building plans with PDR displacement estimates , 2008, 2008 5th Workshop on Positioning, Navigation and Communication.

[9]  Y. Ho,et al.  A Bayesian approach to problems in stochastic estimation and control , 1964 .

[10]  Simo Särkkä,et al.  Unscented Rauch-Tung-Striebel Smoother , 2008, IEEE Trans. Autom. Control..

[11]  François Marx,et al.  Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning , 2006, EURASIP J. Adv. Signal Process..

[12]  John Krumm,et al.  Accuracy characterization for metropolitan-scale Wi-Fi localization , 2005, MobiSys '05.

[13]  Ville Kaseva,et al.  Positioning with coverage area estimates generated from location fingerprints , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[14]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[15]  Stéphane Beauregard,et al.  Omnidirectional Pedestrian Navigation for First Responders , 2007, 2007 4th Workshop on Positioning, Navigation and Communication.

[16]  P. Robertson,et al.  Unscented Kalman filter and Magnetic Angular Rate Update (MARU) for an improved Pedestrian Dead-Reckoning , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[17]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[18]  R. Schroer,et al.  Position, Location, and Navigation Symposium (PLANS) , 2004 .

[19]  J.W. Kim,et al.  Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors , 2007, 2007 IEEE Sensors Applications Symposium.

[20]  Wei Chen,et al.  An effective Pedestrian Dead Reckoning algorithm using a unified heading error model , 2010, IEEE/ION Position, Location and Navigation Symposium.

[21]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[22]  Henri Nurminen,et al.  Particle filter and smoother for indoor localization , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[23]  Piotr Ptasinski,et al.  A method for dead reckoning parameter correction in pedestrian navigation system , 2003, IEEE Trans. Instrum. Meas..

[24]  Ivan Kadar,et al.  Signal Processing, Sensor Fusion, and Target Recognition , 1992 .

[25]  Gérard Lachapelle,et al.  Indoor Positioning System Using Accelerometry and High Accuracy Heading Sensors , 2003 .

[26]  C. Striebel,et al.  On the maximum likelihood estimates for linear dynamic systems , 1965 .

[27]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[28]  Henri Nurminen,et al.  A linear state model for PDR+WLAN positioning , 2013, 2013 Conference on Design and Architectures for Signal and Image Processing.