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2014-06-05

Kouritzin, Michael

The classical particle filter, introduced in 1993, approximates the normalized filter directly. It has two defiencies, over resampling and the inability to distinguish models, the former of which was overcome but the later is fundamental. Conversely, the weighted particle filter, motivated by the...

1994

Heunis, A. J., Kouritzin, Michael

In this note we consider the almost sure convergence (as ϵ→0) of solution Xϵ(·), defined over the interval 0 ≤ τ ≤ 1, of the random ordinary differential equation View the MathML source Here {F(x, t, ω), t ≥ 0} is a strong mixing process for each x and (x, t) → F(x, t, ω) is subject to regularity...

1994

Heunis, A.J., Kouritzin, Michael

Consider the following random ordinary differential equation: X˙ϵ(τ)=F(Xϵ(τ),τ/ϵ,ω)subject toXϵ(0)=x0, where {F(x,t,ω),t≥0} are stochastic processes indexed by x in Rd, and the dependence on x is sufficiently regular to ensure that the equation has a unique solution Xϵ(τ,ω) over the interval...

2004

Zhao, Xingqiu, Long, Hongwei, McCrosky, Jesse, Kim, Surrey, Kouritzin, Michael

In this paper, we discuss multi-target tracking for a submarine model based on incomplete observations. The submarine model is a weakly interacting stochastic dynamic system with several submarines in the underlying region. Observations are obtained at discrete times from a number of sonobuoys...

2004

Kouritzin, Michael, Kim, Surrey, Sun, Wei, Kim, Hyukjoon

In this note, we consider the problem of detecting network portscans through the use of anomaly detection. First, we introduce some static tests for analyzing traffic rates. Then, we make use of two dynamic chi-square tests to detect anomalous packets. Further, we model network traffic as a...

2001

Chan, Hubert, Kouritzin, Michael

Filtering is a method of estimating the conditional probability distribution of a signal based upon a noisy, partial, corrupted sequence of observations of the signal. Particle filters are a method of filtering in which the conditional distribution of the signal state is approximated by the...

2008

Kouritzin, Michael, Wu, Biao

Target Obscuration, including foliage or building obscuration of ground targets and landscape or horizon obscuration of airborne targets, plagues many real world filtering problems. In particular, ground moving target identification Doppler radar, mounted on a surveillance aircraft or unattended...

2005

Kouritzin, Michael, Kim, H., Hu, Y., Ballantyne, D.

This paper addresses the problem of detecting and tracking an unknown number of submarines in a body of water using a known number of moving sonobuoys. Indeed, we suppose there are N submarines collectively maneuvering as a weakly interacting stochastic dynamical system, where N is a random...

2008

Kouritzin, Michael, Long, H.

In this paper, we give a direct derivation of the Duncan–Mortensen–Zakai filtering equation, without assuming right continuity of the signal, nor its filtration, and without the usual finite energy condition. As a consequence, the Fujisaki–Kallianpur–Kunita equation is also derived. Our results...

2000

Ballantyne, David, Chan, Hubert, Kouritzin, Michael

Particle approximations are used to track a maneuvering signal given only a noisy, corrupted sequence of observations, as are encountered in target tracking and surveillance. The signal exhibits nonlinearities that preclude the optimal use of a Kalman filter. It obeys a stochastic differential...