Compressed sampling of bandpass signals with finite rate of innovation: Application to UWB channel estimation and indoor accurate localization

Compressed sampling of bandpass signals with finite rate of innovation: Application to UWB channel estimation and indoor accurate localization

Tina Yaacoub Ana Maria Pistea Roua Youssef Emanuel Radoi Gilles Burel 

Université de Brest, CNRS, UMR 6285 Lab-STICC, 6, avenue Le Gorgeu, 29238 Brest, France

Académie technique militaire, 39-49, boulevard George Cosbuc, Bucarest, Roumanie

Page: 
415-440
|
DOI: 
https://doi.org/10.3166/ts.2017.00003
Received: 
30 September 2015
| |
Accepted: 
20 March 2017
| | Citation
Abstract: 

The finite rate of innovation is a recently proposed concept for reducing the sampling frequency of signals depending on a finite number of parameters. In this paper, low complexity sampling schemes are designed for bandpass signals, which are typical for ultra-wideband (UWB) transmissions. Firstly, two compressed sensing schemes, single-channel (Sum of Sincs [SoS] filter) and multichannel (multichannel modulating waveforms [MCMW]) respectively, are extended to such signals, taking into account the circuit implementation constraints. These schemes allow sampling at greatly reduced frequencies that do not depend on the bandwidth of the signals, but only on the number of UWB channel paths. Then, the effectiveness of the proposed approach is demonstrated through two applications: estimation of the UWB channel for a low complexity coherent Rake receiver, and indoor accurate localization in a dependency aid context.

Keywords: 

ultra-wideband, finite rate of innovation, channel estimation, time of arrival, coherent Rake receiver, indoor localization.

1. Introduction
2. Concept de taux d’innovation fini et modèle du système
3. Méthodes d’échantillonnage compressé des signaux à taux d’innovation fini
4. Applications
5. Conclusion
  References

Akbar R., Radoi E., Azou S. (2010). Conception d’une forme d’onde IR-UWB optimisée et analyse de ses performances dans le canal IEEE 802.15.3a. In: Conf. MajecSTIC 2010, Bordeaux, France. p. 1-8, 13-15.

Basaran M., Erkucuk S., Cirpan H. (2014). Compressive sensing for ultra-wideband channel estimation: on the sparsity assumption of ultra-wideband channels. Int. J. Commun. Sys., vol. 27, no 11, p. 3383-3398.

Cai T., Wang L. (2011). Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Trans. Inf. Theory, vol. 57, no 7, p. 4680-4688.

Candes E., Romberg J., Tao T. (2006). Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory, vol. 52, no 2, p. 489- 509.

Cassioli D., Win M., Vatalaro  F.,  Molisch  A.  (2002).  Performance  of  low-complexity  rake reception in a realistic UWB channel. In: IEEE Int. Conf. Commun. (ICC), p. 763- 767.

Donoho D.L. (2006). Compressed sensing. IEEE Trans. Inf. Theory, vol. 52, no 4, p. 1289-1306. Eldar Y.C., Kutyniok G. (2012). Compressed sensing: theory and applications. Cambridge University Press, Cambridge.

Espes D., Daher A., Autret Y., Radoi E., Le Parc P. (2013). Ultra-wideband positioning for assistance robots for elderly. In: 10th IASTED Int. Conf. Signal Process., Pattern Recognition and Applications (SPPRA 2013), Innsbruck, Austria.

Foerster R. (2001). The effects of multipath interference on the performance of UWB systems in an indoor wireless channel. In: Veh. Technol. Conf. (VTC). IEEE VTS 53rd, Rhodes, IEEE, p. 1176-1180.

Gedalyahu K., Tur R., Eldar Y.C. (2011). Multichannel sampling of pulse streams at the rate of innovation. IEEE Trans. Signal Process., vol. 59, no 4, p. 1491-1504.

Kusuma J., Coyal V. (2006). Multichannel sampling of parametric signals with a successive approximation property. In: IEEE Int. Conf. Image Process., Atlanta, GA, p. 1265-1268.

Kusuma J., Maravic I., Vetterli M. (2003). Sampling with finite rate of innovation: channel and timing estimation for UWB and GPS. In: IEEE Int. Conf. Commun. (ICC), vol. 5. p. 3540- 3544.

Maravic I., Vetterli M. (2005). Sampling and reconstruction of signals with finite rate of innovation in the presence of noise. IEEE Trans. Signal Process., vol. 53, no 8, p. 2788-2805.

Molisch A., Balakrishnan K., Cassioli D., Chong C.-C., Emami S., Fort A., et al. (2004). IEEE 802.15.4a channel model – final report. IEEE P802, vol. 15, no 4.

Molisch A., Cassioli D., Chong C.-C., Emami S., Fort A., Kannan B., et al. (2006). A comprehensive standardized model for ultrawideband propagation channels. IEEE Trans. Antennas Propag., vol. 54, no 11, p. 3151-3166.

Olkkonen H., Olkkonen J. (2008). Measurement and reconstruction of impulse train by parallel exponential filters. IEEE Signal Process. Lett., vol. 15, p. 241-244.

Schmidt R.O. (1981). A signal subspace approach to multiple emitter location and spectral estimation. Thèse de doctorat non publiée, Stanford University, Stanford, USA.

Tan V., Goyal V. (2008). Estimating signals with finite rate of innovation from noisy samples: a stochastic algorithm. IEEE Trans. Signal Process., vol. 56, no 10, p. 5135-5146.

Tur R., Eldar Y., Friedman Z. (2011). Innovation rate sampling of pulse streams with application to ultrasound imaging. IEEE Trans. Signal Process., vol. 59, no 4, p. 1827-1842.

Vetterli M., Marziliano P., Blu T. (2002). Sampling signals with finite rate of innovation. IEEE Trans. Signal Process., vol. 50, no 6, p. 1417-1428.

Waweru N., Konditi D., Langat P. (2014). Performance analysis of MUSIC, root-MUSIC and ESPRIT DOA estimation algorithm. Int. J. Electr. Comput. Energ. Electron. Commun. Eng., vol. 8, no 1, p. 209-216.

Wax M., Kailath T. (1985). Detection of signals by information theoretic criteria. IEEE Trans. ASSP, vol. 33, no 2, p. 387-392.

Weber T., Ahmad B., Ihle M. (2013). Sub-Nyquist sampling for TDR sensors: finite rate of innovation with dithering. In: Proc. of the International Workshop on Compressed Sensing Applied to Radar, Bonn.

Win M., Scholtz R. (1998). On the energy capture of ultrawide bandwidth signals in dense multipath environments. IEEE Commun. Lett., vol. 2, no 9, p. 245-247.

Win M., Scholtz R. (2002). Characterization of ultrawide bandwidth wireless indoor communications channel: a communication theoretic view. IEEE J. Sel. Areas Commun., vol. 20, no 9, p. 1613-1627.

Yang L., Giannakis G. (2004). Ultra-wideband communications: an idea whose time has come.

IEEE Signal Process. Mag., vol. 21, no 6, p. 26-54.