Showing 2 results for and A. Sheikhi
G. Mirjalily, H. Hossieni, and A. Sheikhi,
Volume 25, Issue 2 (1-2007)
Abstract
The theory of distributed detection is receiving a lot of attention. A common assumption used in previous studies is the conditional independence of the observations. In this paper, the optimization of local decision rules for distributed detection networks with correlated observations is considered. We focus on presenting the detection theory for parallel distributed detection networks with fixed fusion rules to develop a numeric algorithm based on Neyman-Pearson criterion. Simulation results are
presented to demonstrate the efficiency and convergence properties of the algorithm.
M. Farzan Sabahi, M. Modarres Hashemi, and A. Sheikhi,
Volume 27, Issue 1 (7-2008)
Abstract
In this paper, radar detection based on Monte Carlo sampling is studied. Two detectors based on Importance Sampling are presented. In these detectors, called Particle Detector, the approximated likelihood ratio is calculated by Monte Carlo sampling. In the first detector, the unknown parameters are first estimated and are substituted in the likelihood ratio (like
the GLRT method). In the second detector, the averaged likelihood ratio is calculated by integrating out the unknown parameters (like the AALR method). Thanks to the numerical nature of these methods, they can be applied to many detection problems which do not have analytical solutions. Simulation results show that both the proposed detectors and the GLRT have approximately the same performance in problems to which the GLRT can be applied. On the other hand, the proposed detectors can be used in many cases for which either no ML estimate of unknown parameters exists or their prior distribution is known.
