Source parameter estimation of atmospheric pollution using regularized least squares

This paper presents a regularized least squares method to estimate the location and release rate of atmospheric pollution. We assume that measured pollution concentration at different ground locations and meteorological conditions such as wind speed are available so that one can solve the advection-diffusion equation for a non-steady point source. However, even finding linear parameters related to the release rate is an ill-posed problem and one has to impose certain regularization technique to avoid potential overfit. We propose to use lscrp-regularization (0 les p les 1) and discuss its advantage over the popularly used lscrp-regularization. The accuracy of source parameter estimation is examined for the cases where the number of sources and the corresponding locations are unknown.

[1]  Arthur B. Maccabe,et al.  Radiation detection with distributed sensor networks , 2004, Computer.

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

[3]  David K. Y. Yau,et al.  A sensor-cyber network testbed for plume detection, identification, and tracking , 2007, IPSN.

[4]  H. Ishida,et al.  Plume-Tracking Robots: A New Application of Chemical Sensors , 2001, The Biological Bulletin.

[5]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[6]  Robert McKibbin,et al.  Source Release-Rate Estimation of Atmospheric Pollution from a Non-Steady Point Source at a Known Location , 2004 .

[7]  Tong Zhao,et al.  Detecting and estimating biochemical dispersion of a moving source in a semi-infinite medium , 2006, IEEE Transactions on Signal Processing.

[8]  G. Nofsinger,et al.  Distributed chemical plume process detection: MILCOM 2005 #1644 , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[9]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[10]  Jay A. Farrell,et al.  Plume mapping via hidden Markov methods , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[11]  T. Moriizumi,et al.  Controlling a gas/odor plume-tracking robot based on transient responses of gas sensors , 2002, Proceedings of IEEE Sensors.

[12]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[13]  John W. Fisher,et al.  Detection and Localization of Material Releases With Sparse Sensor Configurations , 2007, IEEE Trans. Signal Process..

[14]  Tong Zhao,et al.  Distributed Sequential Bayesian Estimation of a Diffusive Source in Wireless Sensor Networks , 2007, IEEE Transactions on Signal Processing.

[15]  Arye Nehorai,et al.  A Sequential Detector for Biochemical Release in Realistic Environments , 2007, IEEE Transactions on Signal Processing.

[16]  Rick Chartrand,et al.  Exact Reconstruction of Sparse Signals via Nonconvex Minimization , 2007, IEEE Signal Processing Letters.

[17]  D. Torney,et al.  Radioactive source detection by sensor networks , 2005, IEEE Transactions on Nuclear Science.

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