Paper Info
Title
A least square approach for bidimensional source separation using higher order statistics criteria
Abstract
The anomaly detection based on processing of distributed temperature sensors data is a new research problem. The acquired data is highly influenced by the response of the ground in which the sensors are buried. It therefore becomes essential to remove the influence of this response. This response, being the most coherent factor in the acquired signal, appears as the most energetic source vector. However, its classical estimation by SVD runs the risk of taking into account energetic phenomena like precipitations. We propose to characterize such phenomena using higher order statistics thus giving a criteria of selecting only the data not influenced by such phenomena. An overlapping window approach then allows estimation of characteristic ground response source. Moreover, the corresponding ground response subspace is constructed by least squares based unmixing approach on the characteristic source. This avoids also the physically unjustifiable orthogonality condition of temporal variations of the estimated sources imposed by SVD.
Year
Venue
Keywords
2008
Lausanne
distributed sensors,estimation theory,fibre optic sensors,geophysical techniques,higher order statistics,least squares approximations,precipitation,singular value decomposition,source separation,temperature sensors,svd,anomaly detection,bidimensional source separation,distributed temperature sensor data,ground response source,ground response subspace,higher order statistics criteria,least squares based unmixing approach,source vector
Field
DocType
ISSN
Least squares,Anomaly detection,Singular value decomposition,Mathematical optimization,Subspace topology,Higher-order statistics,Algorithm,Orthogonality,Source separation,Mathematics
Conference
2219-5491
Citations 
PageRank 
References 
2
0.59
2
Authors
4
Name
Order
Citations
PageRank
Amir Ali Khan120.59
Valeriu Vrabie2188.89
J.I. Mars316114.94
Alexandre Girard454.39