In radar, sonar, and ultrasonic detection system, interference due to clutter can severely deteriorate the quality of the received signal to the point of concealing the target. This paper presents theoretical analysis of order statistic processors for improved target detection. The sort function is used to provide in s i i t into the optimal rank for detection of various targets and clutter env...

A new method of sparse spike train deconvolution is presented. It is based on the coupling of the Hunt filter with a thresholding (to obtain a sparse spike train signal). We show that a good model for the probability density function of the Hunt filter output is a Gaussian mixture, from which we derive the threshold that minimizes the probability of errors. Based on an interpretation of the met...

We study a model of non-Bayesian updating, based on the Hypothesis Testing model of Ortoleva (2012), for ambiguity averse agents. Agents ranks acts following the MaxMin Expected Utility model of Gilboa and Schmeidler (1989) and when they receive new information they update their set of priors as follows: If the information is such that all priors in the original set of priors assign to it a pro...

The implicit particle filter is a sequential Monte Carlo method for data assimilation. The idea is to focus the particles onto the high probability regions of the target probability density function (pdf) so that the number of particles required for a good approximation of this pdf remains manageable, even if the dimension of the state space is large. We explain how this idea is implemented, di...

A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximat...

The probability density functions fitting to the discrete probability functions has always been needed, and very important. This paper is fitting the continuous curves which are probability density functions to the binomial probability functions, negative binomial geometrics, poisson and hypergeometric. The main key in these fittings is the use of the derivative concept and common differential ...

Based on recent studies by Guy Jumarie [1] which defines probability density of fractional order and fractional moments by using fractional calculus (fractional derivatives and fractional integration), this study expands the concept of probability density of fractional order by defining the fractional probability measure, which leads to a fractional probability theory parallel to the classical ...

We address the problem of maintaining resource availability in a networked multirobot team performing distributed tracking an unknown number targets bounded environment. Robots are equipped with sensing and computational resources, enabling them to cooperatively track set using probability hypothesis density (PHD) filter. use trace robot's sensor measurement noise covariance matrix quantify its...

In this paper, we apply a Gaussian Mixture Cardinalized PHD tracker to several real and simulated datasets from the MSTWG (Multistatic Tracking Working Group) library from NURC, TNO and ARL:UT. We also report our analysis on the SEABAR’07 sea experiment.