Prior Models for Indoor Super-Resolution Time of Arrival Estimation

We propose a scheme for super-resolution estimation of the time of arrival of a radio signal which makes use of a prior model of the radio propagation conditions. The method is based on examination of the leading edge of the channel impulse response, and comparison with a database of leading edge shapes generated by Monte Carlo simulation. We also propose an efficient implementation, which makes the method practical for mobile devices with limited computational power. We verify the performance of the method using actual measured data, taken in a number of different situations, and show improved performance over other super-resolution methods proposed in the literature.

[1]  Kaveh Pahlavan,et al.  Super-resolution TOA estimation with diversity for indoor geolocation , 2004, IEEE Transactions on Wireless Communications.

[2]  G.B. Giannakis,et al.  Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks , 2005, IEEE Signal Processing Magazine.

[3]  Fang Zhao,et al.  Comparison of Super-Resolution Algorithms for TOA Estimation in Indoor IEEE 802.11 Wireless LANs , 2006, 2006 International Conference on Wireless Communications, Networking and Mobile Computing.

[4]  Bahman Abolhassani,et al.  TOA Extraction in Multipath Fading Channels for Location Estimation , 2006, 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications.

[5]  Yingning Peng,et al.  Super-Resolution Time Delay Estimation in Multipath Environments , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[6]  Ramy Farha,et al.  Multipath delay estimations using matrix pencil , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[7]  Harri Saarnisaari,et al.  TLS-ESPRIT in a time delay estimation , 1997, 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion.

[8]  Jack M. Holtzman,et al.  Wireless information networks , 2010, 2010 International Conference on Wireless Information Networks and Systems (WINSYS).

[9]  Zhi Ning Chen,et al.  Ultra Wideband Wireless Communication: Arslan/Ultra Wideband Wireless Communication , 2006 .

[10]  Mark Hedley,et al.  Super-Resolution Time of Arrival for Indoor Localization , 2008, 2008 IEEE International Conference on Communications.

[11]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

[12]  Kaveh Pahlavan,et al.  Analysis of Time of Arrival Estimation Using Wideband Measurements of Indoor Radio Propagations , 2007, IEEE Transactions on Instrumentation and Measurement.

[13]  Kaveh Pahlavan,et al.  Wireless Information Networks (Wiley Series in Telecommunications and Signal Processing) , 2005 .

[14]  P. Ho,et al.  A platform for radio location research in Ad Hoc and sensor networks , 2007, 2007 International Symposium on Communications and Information Technologies.

[15]  Andreas F. Molisch,et al.  Localization via Ultra- Wideband Radios , 2005 .

[16]  Kaveh Pahlavan,et al.  Wireless Information Networks: Pahlavan/Wireless Information Networks, Second Edition , 2005 .

[17]  Petre Stoica,et al.  Spectral Analysis of Signals , 2009 .

[18]  Weihua Zhuang,et al.  Ultra-wideband wireless communications , 2005, Wirel. Commun. Mob. Comput..