In this paper, the detector of a moving target in MIMO radar with widely separated antenna in the presence of the a-stable clutter is proposed. Due to the complexity of the unknown parameter estimation in the presence of the target, the Rao test is applied. The a-stable model is a general model that can be applied to different environments with appropriate selection of parameters. The problem with this model is inexistence of a closed form for the probability density function of the distribution. Fortunately, there are models that approximate the distribution by a closed form expression. In the present paper, mixing of Gaussian and Cauchy model is used for expressing clutter. Finally, the performance of the proposed detector is compared with the optimal Gaussian and MiniMax detector by a graph of the probability of detection versus signal-to-clutter ratio. The simulation results, show that the performance of the proposed detector is better than the optimal Gaussian and MiniMax detector.

Please click on PDF to view the abstract

In this paper we consider multivariate Lagrange mean-value interpolation problem, where interpolation parameters are integrals over spheres. We have concentric spheres. Indeed, we consider the problem in three variables when it is not correct.

In this paper we consider the interpolation by certain functions such as trigonometric and rational functions for finite dimensional linear space X. Then we extend this to infinite dimensional linear spaces.

Sometimes observations in statistical analysis are not independent and typically are correlated with their spatial locations. In spatial statistics there are some methods to interpolate this kind of data. In this paper some of interpolators for spatial data will be studied. Then they will be evaluated and compared by means of mean square errors criteria. Finally it will be shown that kriging acts better than other methods such as spline, inverse distance, for interpolation of spatial data.

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very high, especially in very large discrete utterance recognition problems. For real time implementation of very large discrete utterance recognition, we must use discrete density HMM (DDHMM). To increase the performance of DDHMM, one usual solution is fuzzy interpolation. In this study, we present a new method named Gaussian interpolation. We implemented and compared the performance of two types of interpolation methods for 1500 Persian speech command words. Results show that precision and flexibility of Gaussian interpolation is better thanthose of the fuzzy interpolation.