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Oregon Health & Science University

Simon Justin Julier

Jeffrey K Uhlmann

Rudolph Van Der Merwe

Eric Wan

A method of estimating the navigational state of a system entails acquiring observation data produced by noisy measurement sensors and providing a probabilistic inference system to combine the observation data with prediction values of the system state space model to estimate the navigational state of the system. The probabilistic inference system is implemented to include a realization of a Gaussian approximate random variable propagation technique performing deterministic sampling without analytic derivative calculations. This technique achieves for the navigational state of the system an estimation accuracy that is greater than that achievable with an extended Kalman filter-based probabilistic inference system.

Navigation system applications of sigma-point Kalman filters for nonlinear estimation and sensor fusion

A method and apparatus is described for fusing a plurality of signals corresponding to estimates of the state of an object, system, or process. The method and apparatus is specialized or programmed for (1) receiving estimates, each of which can be expressed in the form of a state vector and an error covariance matrix, at least one estimate of which can be decomposed into a sum of an independent error covariance matrix and a potentially correlated error covariance matrix, and (2) transmitting a resulting signal corresponding to an estimate, which can be expressed in the form of a state vector and an error matrix, in order to evoke a physical response from a system receiving the signal. The method and apparatus provides advantages over the prior art by improving the accuracy of fused estimates while guaranteeing consistency.

Method and apparatus for fusing signals with partially known independent error components

A system and a method for estimating the mean and covariance resulting from the application of a given nonlinear function or transformation when applied to a given mean and covariance derived from measurements of a physical system or process. The method comprises selecting a set of points having an arbitrary mean different from the given mean, but with covariance about the given mean equal to that of the given covariance; applying the given nonlinear function to each element of the selected set of points; computing an estimate of the mean of the nonlinearly transformed given mean and covariance; and computing the covariance of the nonlinearly transformed set of points about the estimated mean. The system and method provide an efficient procedure for accurately incorporating nonlinear models of quantities of interest in tracking systems, control mechanisms, and many other related signal processing systems.

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Falls Church, VA, United States of America

Simon Justin Julier