Showing 3 results for G. Mirjalily
G. Mirjalily, M. R. Aref, M. M. Nayebi and M. Kahrizi,
Volume 19, Issue 1 (7-2000)
Abstract
In a detection
network, the final decision is made by fusing the decisions from local detectors. The objective of that decision is to minimize the final error probability. To implement and optimal fusion rule, the performance of each detector, i.e. its probability of false alarm and its probability of missed detection as well as the a priori probabilities of the hypotheses, must be known. However, these statistics are usually unknown or may vary with time. In this paper, we develop a recursive algorithm that adapts the fusion center. This approach is based on the time-averaging of local decisions and on using the analytic solutions that guarantee the asymptotic convergence. Also a simple method is proposed that enables the algorithm to track changes faster. Simulation results are presented to demonstrate the efficiency and convergence properties of the algorithm.
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. Ghaffari, M.r. Taban, M.m. Nayebi, and G. Mirjalily,
Volume 26, Issue 2 (1-2008)
Abstract
In this paper, two suboptimum detectors are proposed for coherent radar signal detection in K-distributed clutter. Assuming certain values for several initial moments of clutter amplitude, the characteristic function of the clutter amplitude is approximated by a limited series. Using the Pade approximation, it is then converted to a rational fraction. Thus, the pdf of the clutter amplitude is obtained as a sum of simple exponential functions. Using such a pdf, we develop the suboptimum detectors PGLR and PAALR, which are simplified forms of the GLR and AALR. Computer simulations show that the suggested detectors have appropriate performance compared to OLD, GLR and AALR detectors.
