Sensor configuration and activation for field detection in large sensor arrays

The problems of sensor configuration and activation for the detection of correlated random fields using large sensor arrays are considered. Using results that characterize the large-array performance of sensor networks in this application, the detection capabilities of different sensor configurations are analyzed and compared. The dependence of the optimal choice of configuration on parameters such as sensor signal-to-noise ratio (SNR), field correlation, etc., is examined, yielding insights into the most effective choices for sensor selection and activation in various operating regimes.

[1]  H. Vincent Poor,et al.  Neyman-pearson detection of gauss-Markov signals in noise: closed-form error exponentand properties , 2005, IEEE Transactions on Information Theory.

[2]  Randall K. Bahr Asymptotic analysis of error probabilities for the nonzero-mean Gaussian hypothesis testing problem , 1990, IEEE Trans. Inf. Theory.

[3]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[4]  Carl W. Helstrom,et al.  Elements of signal detection and estimation , 1994 .

[5]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[6]  H. Vincent Poor,et al.  An introduction to signal detection and estimation (2nd ed.) , 1994 .

[7]  H. Vincent Poor,et al.  Neyman-Pearson Detection of Gauss-Markov Signals in Noise: Closed-Form Error Exponent and Properties , 2005, ISIT.

[8]  James A. Bucklew,et al.  Optimal sampling schemes for the Gaussian hypothesis testing problem , 1990, IEEE Trans. Acoust. Speech Signal Process..

[9]  I. M. Chakravarti,et al.  Asymptotic Theory of Statistical Tests and Estimation: In Honor of Wassily Hoeffding , 1980 .

[10]  Lang Tong,et al.  Sensor configuration and activation for field detection in large sensor arrays , 2005 .

[11]  Robert M. Gray,et al.  On the asymptotic eigenvalue distribution of Toeplitz matrices , 1972, IEEE Trans. Inf. Theory.

[12]  I. Vajda Theory of statistical inference and information , 1989 .

[13]  M. Micheli Random Sampling of a Continuous-time Stochastic Dynamical System: Analysis, State Estimation, and Applications , 2001 .

[14]  Harald Luschgy,et al.  Asymptotic Behavior of Neyman-Pearson Tests for Autoregressive Processes , 1994 .

[15]  Fred C. Schweppe,et al.  Evaluation of likelihood functions for Gaussian signals , 1965, IEEE Trans. Inf. Theory.

[16]  U. Grenander,et al.  Toeplitz Forms And Their Applications , 1958 .

[17]  Venugopal V. Veeravalli,et al.  Design of sensor networks for detection applications via large-deviation theory , 2004, Information Theory Workshop.

[18]  Mauro Piccioni,et al.  Optimal importance sampling for some quadratic forms of ARMA processes , 1995, IEEE Trans. Inf. Theory.

[19]  B. Bercu,et al.  Large deviations for quadratic forms of stationary Gaussian processes , 1997 .

[20]  Gerald R. Benitz,et al.  Large deviation rate calculations for nonlinear detectors in Gaussian noise , 1990, IEEE Trans. Inf. Theory.

[21]  W. Bryc,et al.  On the large deviation principle for a quadratic functional of the autoregressive process , 1993 .

[22]  Richard A. Davis,et al.  Time Series: Theory and Methods (2nd ed.). , 1992 .

[23]  David R. Cox,et al.  The Theory of Stochastic Processes , 1967, The Mathematical Gazette.

[24]  Jean-Francois Chamberland-Tremblay Design of Sensor Networks for Detection Applications via Large -Deviation Theory , 2004 .

[25]  H. Vincent Poor,et al.  Detection of Stochastic Processes , 1998, IEEE Trans. Inf. Theory.