TY - GEN
T1 - Robust detection of random events with spatially correlated data in wireless sensor networks via distributed compressive sensing
AU - Wimalajeewa, Thakshila
AU - Varshney, Pramod K.
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/3/9
Y1 - 2018/3/9
N2 - In this paper, we exploit the theory of compressive sensing to perform detection of a random source in a dense sensor network. When the sensors are densely deployed, observations at adjacent sensors are highly correlated while those corresponding to distant sensors are less correlated. Thus, the covariance matrix of the concatenated observation vector of all the sensors at any given time can be sparse where the sparse structure depends on the network topology and the correlation model. Exploiting the sparsity structure of the covariance matrix, we develop a robust nonparametric detector to detect the presence of the random event using a compressed version of the data collected at the distributed nodes. We employ the multiple access channel (MAC) model with distributed random projections for sensors to transmit observations so that a compressed version of the observations is available at the fusion center. Detection is performed by constructing a decision statistic based on the covariance information of uncompressed data which is estimated using compressed data. The proposed approach does not require any knowledge of the noise parameter to set the threshold, and is also robust when the distributed random projection matrices become sparse.
AB - In this paper, we exploit the theory of compressive sensing to perform detection of a random source in a dense sensor network. When the sensors are densely deployed, observations at adjacent sensors are highly correlated while those corresponding to distant sensors are less correlated. Thus, the covariance matrix of the concatenated observation vector of all the sensors at any given time can be sparse where the sparse structure depends on the network topology and the correlation model. Exploiting the sparsity structure of the covariance matrix, we develop a robust nonparametric detector to detect the presence of the random event using a compressed version of the data collected at the distributed nodes. We employ the multiple access channel (MAC) model with distributed random projections for sensors to transmit observations so that a compressed version of the observations is available at the fusion center. Detection is performed by constructing a decision statistic based on the covariance information of uncompressed data which is estimated using compressed data. The proposed approach does not require any knowledge of the noise parameter to set the threshold, and is also robust when the distributed random projection matrices become sparse.
KW - Compressive sensing
KW - detection theory
KW - random events
KW - statistical dependence
KW - wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85051124181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051124181&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2017.8313148
DO - 10.1109/CAMSAP.2017.8313148
M3 - Conference contribution
AN - SCOPUS:85051124181
T3 - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
SP - 1
EP - 5
BT - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Y2 - 10 December 2017 through 13 December 2017
ER -