Sensor configuration and activation for field detection in large sensor arrays
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Detection of Gauss-Markov random fields with nearest-neighbor dependency
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Error exponents for target-class detection in a sensor network
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
| Hi-index | 754.96 |
The performance of Neyman-Pearson detection of correlated random signals using noisy observations is considered. Using the large deviations principle, the performance is analyzed via the error exponent for the miss probability with a fixed false-alarm probability. Using the state-space structure of the signal and observation model, a closed-form expression for the error exponent is derived using the innovations approach, and the connection between the asymptotic behavior of the optimal detector and that of the Kalman filter is established. The properties of the error exponent are investigated for the scalar case. It is shown that the error exponent has distinct characteristics with respect to correlation strength: for signal-to-noise ratio (SNR) ≥1, the error exponent is monotonically decreasing as the correlation becomes strong whereas for SNR