Object localization based on audio and video information is important for the analysis of dynamic scenes such as video conferences or traffic situations. In this paper, we view the the dynamic audiovideo object localization problem as a joint recursive estimation problem. It is solved using a decentralized Kalman filter fusing both audio and video position estimates. To better take into account...

A statistical parameter estimation theory for quantum pure state models is presented. We rst investigate a general framework of the pure state estimation theory and derive quantum counterparts of the Fisher metric. Then we formulate a one parameter estimation theory, based on the symmetric logarithmic derivatives, and clarify the di erences between pure state models and strictly positive models.

Objective of this paper is to highlight the rapid assessment (in a few minutes) of fractionally differenced standard long memory time series in terms of parameter estimation and optimal lag order assessment. Initially, theoretical aspects of standard fractionally differenced processes with long memory and related state space modelling will be discussed. An efficient mechanism based on theory to...

A new method for eliminating parameter drift in parameter estimation problems is proposed. Existing methods for eliminating parameter drift work either on a limited time horizon, restricts the parameter estimates to a range that has to be determined a priori, or introduces bias in the parameter estimates which will degrade steady state performance. The idea of the new method is to apply leakage...

The performance of Vector Controlled Induction Motor drive depends on the accuracy of rotor resistance which will vary with temperature and frequency. The MRAS approach using reactive power and flux as a state variable for rotor resistance estimation makes MRAS computationally simpler and easy to design. In this paper, Rotor Flux based MRAS (RF-MRAS) and Reactive Power based MRAS (RP-MRAS) for ...