Title :
Combining Gaussian mixture estimates from uncertain sources
Author_Institution :
Lockheed Martin Syst. Integration, Owego, NY, USA
Abstract :
This paper examines the exact solution for combining multiple Gaussian mixture (GM) estimates of a state vector, methods for order-reduction of the resulting GM, estimating the likelihood of each source GM, and for classifying sources as good (in agreement) or bad (biased or false/spoofed). Together, these allow combining estimates from GM sources in a way that guarantees some false-rejection probability of good sources in the merged best-estimate and allows generating a set of ellipsoids to approximate the most-probable volume for displaying the result to a user.
Keywords :
Gaussian processes; estimation theory; Gaussian mixture estimates; false rejection probability; order reduction; state vector; Ellipsoids; Filling; Gain measurement; Gaussian approximation; Gaussian distribution; Kalman filters; Random variables; State estimation; State-space methods; Visualization; Gaussian mixture; State space; spoofing; visualization; water filling;
Conference_Titel :
Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on
Conference_Location :
Cardiff
Print_ISBN :
978-1-4244-2709-3
Electronic_ISBN :
978-1-4244-2711-6
DOI :
10.1109/SSP.2009.5278510