Performance analysis of different SIC-based methods for multipath cancelation

In this work the performance of the Sucessive Interference Cancelation (SIC) architecture combined with Matching Pursuit (SIC/MP) and Least Squares (SIC/LS) algorithms is statistically analyzed when they are used to mitigate multipath effect in an Acoustic Local Positioning System (ALPS). The experiments are carried out with sets of two hundred real signals acquired in positions where there exists a strong multipath interference, showing that the proposed methods have a better performance than the classical correlation + thresholding-based detection method. Especially, in the SIC/MP method case, its corresponding Error Cummulative Distribution Function (ECDF) shows that practically 100% of the Time of Flight (TOF) measurements affected by multipath interference are recovered, obtaining positioning errors below 10 cm for 60% of the measurements.

[1]  Juan R. Gonzalez,et al.  High-Precision Robust Broadband Ultrasonic Location and Orientation Estimation , 2009, IEEE Journal of Selected Topics in Signal Processing.

[2]  Abdelmoumen Norrdine,et al.  Adaptive Signal Processing for a Magnetic Indoor Positioning System , 2011 .

[3]  Sunwoo Kim,et al.  Geometric derivation of expectation-maximization and generalized successive interference cancellation algorithms with applications to CDMA channel estimation , 2003, IEEE Trans. Signal Process..

[4]  Bhaskar D. Rao,et al.  Performance limits of matching pursuit algorithms , 2008, 2008 IEEE International Symposium on Information Theory.

[5]  Andy Hopper,et al.  Implementing a Sentient Computing System , 2001, Computer.

[6]  Santiago Mazuelas,et al.  Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks , 2009, IEEE Journal of Selected Topics in Signal Processing.

[7]  Álvaro Hernández,et al.  Advanced sensorial system for an acoustic LPS , 2007, Microprocess. Microsystems.

[8]  Mike Hazas,et al.  A high performance privacy-oriented location system , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[9]  David E. Culler,et al.  A practical evaluation of radio signal strength for ranging-based localization , 2007, MOCO.

[10]  Mike Hazas,et al.  A Novel Broadband Ultrasonic Location System , 2002, UbiComp.

[11]  Henk L. Muller,et al.  Low Cost Indoor Positioning System , 2001, UbiComp.

[12]  Volker Willert,et al.  Accuracy Evaluation for Automated Optical Indoor Positioning Using a Camera Phone , 2012 .

[13]  Sebastian Tilch,et al.  Current investigations at the ETH Zurich in optical indoor positioning , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[14]  B. Rao,et al.  Forward sequential algorithms for best basis selection , 1999 .

[15]  Ig-Jae Kim,et al.  Indoor location sensing using geo-magnetism , 2011, MobiSys '11.

[16]  J. Werb,et al.  Designing a positioning system for finding things and people indoors , 1998 .

[17]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[18]  Sunwoo Kim,et al.  A matching-pursuit/GSIC-based algorithm for DS-CDMA sparse-channel estimation , 2004, IEEE Signal Processing Letters.

[19]  Álvaro Hernández,et al.  Efficient hardware implementation for detecting CSS-based Loosely Synchronous codes in a Local Positioning System , 2009, 2009 IEEE Conference on Emerging Technologies & Factory Automation.

[20]  K. Arshak,et al.  A Model for Estimating the Real-Time Positions of a moving Object in Wireless Telemetry Applications using RF Sensors. , 2007, 2007 IEEE Sensors Applications Symposium.

[21]  Santiago Mazuelas,et al.  Hybrid RSS-RTT Localization Scheme for Indoor Wireless Networks , 2010, EURASIP J. Adv. Signal Process..