An approximated detection method for sequential detection in wireless sensor networks
ICCOM'07 Proceedings of the 11th Conference on 11th WSEAS International Conference on Communications - Volume 11
Sampling schemes for sequential detection with dependent observations
IEEE Transactions on Signal Processing
Detection of selfish nodes in networks using CoopMAC protocol with ARQ
IEEE Transactions on Wireless Communications
Group testing strategies for recovery of sparse signals in noise
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Sequential detection of misbehaving nodes in cooperative networks with HARQ
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
Tracking aircrafts by using impulse exclusive filter with RBF neural networks
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
| Hi-index | 754.84 |
It is supposed that there is a multichannel sensor system which performs sequential detection of a target. Sequential detection is done by implementing a generalized Wald's sequential probability ratio test, which is based on the maximum-likelihood ratio statistic and allows one to fix the false-alarm rate and the rate of missed detections at specified levels. We present the asymptotic performance of this sequential detection procedure and show that it is asymptotically optimal in the sense of minimizing the expected sample size when the probabilities of erroneous decisions are small. We do not assume that the observations are independent and identically distributed (i.i.d.). The first-order asymptotic optimality result holds for general statistical models that are not confined to the restrictive i.i.d. assumption. However, for i.i.d. and quasi-i.i.d. cases, where the log-likelihood ratios can be represented in the form of sums of random walks and slowly changing sequences, we obtain much stronger results. Specifically, using the nonlinear renewal theory we are able to obtain both tight expressions for the error probabilities and higher order approximations for the average sample size up to a vanishing term. The performance of the multichannel sequential detection algorithm is illustrated by an example of detection of a deterministic signal in correlated (colored) Gaussian noise. In this example, we provide both the results of theoretical analysis and the results of a Monte Carlo experiment. These results allow us to conclude that the use of the sequential detection algorithm substantially reduces the required resources of the system compared to the best nonsequential algorithm.