Discrete-Time $H_{\infty}$ Filtering for Mobile Robot Localization Using Wireless Sensor Network

This paper proposes a localization technique for mobile robots using a wireless sensor network (WSN), based on chirp spread spectrum ranging and an inertial measurement unit (IMU). A discrete-time H∞ filter with input forcing function is newly derived for mobile robot localization. The position of the robot is estimated by the filter using an integration of position information collected by the WSN and absolute acceleration data obtained by the IMU. From the dynamics of the robot, the solution existence of the proposed filter is shown, and a low-complexity computational method to obtain a solution from the filter is proposed by a generalized eigenvector approach. Through simulation and experiments, we evaluate the performance of the proposed H∞ filter and compare it with the standard Kalman filter.

[1]  Yan Yu,et al.  Toward Robust Indoor Localization Based on Bayesian Filter Using Chirp-Spread-Spectrum Ranging , 2012, IEEE Transactions on Industrial Electronics.

[2]  G. Stewart,et al.  An Algorithm for Generalized Matrix Eigenvalue Problems. , 1973 .

[3]  F. Lewis,et al.  Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second Edition , 2007 .

[4]  Ali H. Sayed,et al.  Network-based wireless location , 2005 .

[5]  Hyo-Sung Ahn,et al.  Simple Pedestrian Localization Algorithms Based on Distributed Wireless Sensor Networks , 2009, IEEE Transactions on Industrial Electronics.

[6]  Rodney M. Goodman,et al.  Distributed odor source localization , 2002 .

[7]  Francisco Barceló,et al.  A Ranging Method with IEEE 802.11 Data Frames for Indoor Localization , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[8]  Hwan Hur,et al.  A Circuit Design for Ranging Measurement Using Chirp Spread Spectrum Waveform , 2010, IEEE Sensors Journal.

[9]  S. Krishnan,et al.  A UWB based Localization System for Indoor Robot Navigation , 2007, 2007 IEEE International Conference on Ultra-Wideband.

[10]  Fotini-Niovi Pavlidou,et al.  An overview of the IEEE 802.15.4a Standard , 2010, IEEE Communications Magazine.

[11]  Christof Röhrig,et al.  Localization of Sensor Nodes in a Wireless Sensor Network Using the nanoLOC TRX Transceiver , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[12]  Mohamed Darouach,et al.  Kalman filtering with unknown inputs via optimal state estimation of singular systems , 1995 .

[13]  G.Ph. Alexiou,et al.  Target Localization Utilizing the Success Rate in Infrared Pattern Recognition , 2006, IEEE Sensors Journal.

[14]  A.H. Sayed,et al.  Network-based wireless location: challenges faced in developing techniques for accurate wireless location information , 2005, IEEE Signal Processing Magazine.

[15]  Jonathan P. How,et al.  Signal architecture for a Distributed Magnetic Local Positioning System , 2002, Proceedings of IEEE Sensors.

[16]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[17]  M. Bolic,et al.  Particle filtering for indoor RFID tag tracking , 2011, 2011 IEEE Statistical Signal Processing Workshop (SSP).

[18]  João Pedro Hespanha,et al.  Linear Systems Theory , 2009 .

[19]  U. Shaked,et al.  H,-OPTIMAL ESTIMATION: A TUTORIAL , 1992 .

[20]  Hao Zhang,et al.  Signal Processing of On-line Partial Discharge Measurements On HV Power Cables , 2004 .

[21]  Abdullah Kadri,et al.  Detection and performance of weak M-ary chirp signals in class-A impulsive noise , 2011, 2011 IEEE Wireless Communications and Networking Conference.

[22]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[23]  Mohamed Darouach,et al.  Unbiased minimum variance estimation for systems with unknown exogenous inputs , 1997, Autom..

[24]  F. Azizi,et al.  Mobile Robot Position Determination Using Data Integration of Odometry and Gyroscope , 2006, 2006 World Automation Congress.

[25]  Heejae Jung,et al.  Using RFID for Accurate Positioning , 2004 .

[26]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[27]  A. Laub,et al.  On the numerical solution of the discrete-time algebraic Riccati equation , 1980 .

[28]  Hugh F. Durrant-Whyte,et al.  Mobile robot localization by tracking geometric beacons , 1991, IEEE Trans. Robotics Autom..

[29]  Masayuki Murata,et al.  Indoor Localization System using RSSI Measurement of Wireless Sensor Network based on ZigBee Standard , 2006, Wireless and Optical Communications.