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The Gaussian particle filter has emerged as a useful tool for nonlinear state estimation problems. The sample size used in the estimation is one of the key factors to the efficiency and accuracy of the filter. However, the fixed sample size which is usually determined empirically may be highly inappropriate since it ignores the varying errors of the processes. This paper presents a sample size adaptive Gaussian particle filter that uses statistical methods and unscented transform technique to estimate the needed sample size in the time update step and the observation update step respectively at each iteration. Simulation results show that the proposed method performs much better than the standard GPF in the nonlinear problems with great accuracy. [ABSTRACT FROM PUBLISHER]

The aim of situation analysis is to assess the relevant objects in the surroundings and interpret their relations and their impact in order for a decision maker to achieve situation awareness and be able to make suitable decisions. However, the information regarding the relevant objects is typically uncertain, which will induce uncertainty in the result from the situation analysis. If the kinematic states of the objects are estimated with a tracking filter, the estimates can be considered as random variables. Furthermore, the situation analysis algorithm is a function of these estimates entailing that the result from the situation analysis is random variable. This paper studies the fighter aircraft domain and a situation analysis algorithm that calculates the combat survivability, i.e., the probability that the aircraft can a fly a route inside hostile territory without getting hit by enemy fire. The survivability of different routes can be compared in order to decide where to fly. However, the uncertainties regarding the threats' positions imply that the survivability is uncertain and can be described as a random variable with a distribution. The unscented transform (UT) is here used for calculating the mean and standard deviation (std) of the survivability in a few scenarios with threats located on the ground. Simulations show that the position uncertainties affect both the mean and std of the survivability and that UT gives similar estimates as a Monte Carlo (MC) approach. UT therefore seems to be a promising approach for calculating the uncertainty in the survivability, which is more computational efficient than MC. [ABSTRACT FROM PUBLISHER]

Acoustic travel-time tomography of the atmosphere is a nonlinear inverse problem which attemps to reconstruct temperature and wind velocity fields in the atmospheric surface layer (ASL) using the dependence of sound speed on temperature and wind velocity fields along the propagation path. A new statistical-based algorithm is introduced in this paper based on fixed-point unscented Kalman filter (UKF) which is capable of reconstructing and tracking temperature and wind velocity fields within a specified investigation area. [ABSTRACT FROM PUBLISHER]

The paper deals with an analysis of the scaling factor of the unscented transform as a method to provide approximate means and covariance matrices of random variables in nonlinear systems. It is a basis of the unscented Kalman filter, which provides a state estimate of nonlinear stochastic dynamic systems. The scaling factor represents a design parameter significantly affecting quality of the approximation. The analysis provides an important insight into the parameter role and its impact on the quality. It also justifies recently published techniques for adaptive setting of the scaling parameter within the unscented Kalman filter. Usage of such an adaptive setting of the scaling parameter is illustrated in a bearings-only tracking example. [ABSTRACT FROM PUBLISHER]

Automatic speech recognition systems often rely on statistical noise suppression methods to increase their recognition performance in non-stationary noisy environments. However, even with a good approximation of the noise power spectrum, the estimated clean signal contains residual noise along with artifacts introduced by speech estimation inaccuracies. In this paper, we show that this can be compensated by propagating a measure of the uncertainty of estimation through the feature extraction process and combining it with missing feature techniques directly in the feature domain. [ABSTRACT FROM AUTHOR]