Dynamic rainfall monitoring using microwave links

[1]  Hagit Messer,et al.  A New Approach to Precipitation Monitoring: A critical survey of existing technologies and challenges , 2015, IEEE Signal Processing Magazine.

[2]  Geert Leus,et al.  Spatial rainfall mapping from path-averaged rainfall measurements exploiting sparsity , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[3]  Christopher J. Rozell,et al.  Convergence of basis pursuit de-noising with dynamic filtering , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[4]  Yoav Liberman,et al.  Object tracking extensions for accurate recovery of rainfall maps using microwave sensor network , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[5]  Hagit Messer,et al.  Accurate reconstruction of rain field maps from Commercial Microwave Networks using sparse field modeling , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Christian P. Robert,et al.  Statistics for Spatio-Temporal Data , 2014 .

[7]  Wei Wang,et al.  Throat polyp detection based on compressed big data of voice with support vector machine algorithm , 2014, EURASIP J. Adv. Signal Process..

[8]  Christopher J. Rozell,et al.  Dynamic filtering of sparse signals using reweighted ℓ1 , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Hidde Leijnse,et al.  Country-wide rainfall maps from cellular communication networks , 2013, Proceedings of the National Academy of Sciences.

[10]  H. Leijnse,et al.  Seasonal semi-variance of Dutch rainfall at hourly to daily scales , 2012 .

[11]  M. Ghanem,et al.  Environmental monitoring via compressive sensing , 2012, SensorKDD '12.

[12]  Hagit Messer,et al.  Environmental sensor networks using existing wireless communication systems for rainfall and wind velocity measurements , 2012, IEEE Instrumentation & Measurement Magazine.

[13]  Georgios B. Giannakis,et al.  Tracking target signal strengths on a grid using sparsity , 2011, EURASIP J. Adv. Signal Process..

[14]  Justin K. Romberg,et al.  Sparsity penalties in dynamical system estimation , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[15]  Fabio Sigrist,et al.  A dynamic nonstationary spatio-temporal model for short term prediction of precipitation , 2011, 1102.4210.

[16]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[17]  Pini Gurfil,et al.  Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms , 2010, IEEE Transactions on Signal Processing.

[18]  H. Messer,et al.  Frontal Rainfall Observation by a Commercial Microwave Communication Network , 2009 .

[19]  Nitesh V. Chawla,et al.  Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data , 2009, KDD 2009.

[20]  Hagit Messer,et al.  Rain Rate Estimation Using Measurements From Commercial Telecommunications Links , 2009, IEEE Transactions on Signal Processing.

[21]  Hagit Messer,et al.  Estimation of rainfall fields using commercial microwave communication networks of variable density , 2008 .

[22]  Remko Uijlenhoet,et al.  Microwave link rainfall estimation: Effects of link length and frequency, temporal sampling, power resolution, and wet antenna attenuation , 2008 .

[23]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[24]  Michael Elad,et al.  Optimized Projections for Compressed Sensing , 2007, IEEE Transactions on Signal Processing.

[25]  Stephen P. Boyd,et al.  An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least Squares , 2007, IEEE Journal of Selected Topics in Signal Processing.

[26]  Remko Uijlenhoet,et al.  Path‐averaged rainfall estimation using microwave links: Uncertainty due to spatial rainfall variability , 2007 .

[27]  Soroosh Sorooshian,et al.  Spatial patterns in thunderstorm rainfall events and their coupling with watershed hydrological response , 2006 .

[28]  Hagit Messer,et al.  Environmental Monitoring by Wireless Communication Networks , 2006, Science.

[29]  Ke Xu,et al.  A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities , 2005 .

[30]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[31]  C. Mallows Some Comments on Cp , 2000, Technometrics.

[32]  Dino Giuli,et al.  Microwave tomographic inversion technique based on stochastic approach for rainfall fields monitoring , 1999, IEEE Trans. Geosci. Remote. Sens..

[33]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[34]  Alberto Toccafondi,et al.  Tomographic Reconstruction of Rainfall Fields through Microwave Attenuation Measurements , 1991 .

[35]  D. V. Rogers,et al.  The aR b relation in the calculation of rain attenuation , 1978 .

[36]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[37]  Carlton W. Ulbrich,et al.  Path- and Area-Integrated Rainfall Measurement by Microwave Attenuation in the 1–3 cm Band , 1977 .

[38]  Michael Elad,et al.  Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.

[39]  Jonathan R. Stroud,et al.  Dynamic models for spatiotemporal data , 2001 .

[40]  P. Brown,et al.  Blur‐generated non‐separable space–time models , 2000 .

[41]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[42]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[43]  A. R. Jameson A comparison of microwave techniques for measuring rainfall , 1991 .

[44]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[45]  Tohru Katayama,et al.  Robust fixed-lag smoother for linear systems including outliers in the system and observation noises , 1988 .

[46]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[47]  C. L. Mallows Some comments on C_p , 1973 .