Kalman filter approach to real options with active learning

Real-time Tracking with Kalman Filter

automatically updated during the matching process, This paper describes a real-time tracking system which detects an object entering into the field of view of camera and executes the tracking of the detected object by controlling a servo device so that a target object always lies at the center of an image frame. In order to detect and track a moving object, we basically apply a model matching s...

Mergers and market valuation: real options approach

This paper investigates the connection between market valuation anda type of the merger (stock, cash) using real options setup. I solveexplicitly for the timing and terms of cash mergers in two deferent settingsto demonstrate that cash mergers generally occur at low marketvaluations, whereas stock mergers that may be observed at both low andhigh valuations; the result holds with some dierences ...

The Kalman Filter in Active Noise Control

Most Active Noise Control (ANC) systems use some form of the LMS [5][7] algorithm due to its reduced computational complexity. However, the problems associated with it are well-known, namely slow convergence and high sensitivity to the eigenvalue spread [3][7]. To overcome this problems the RLS algorithm is often used, but it is now widely known, that the RLS loses many of its good properties f...

A fast-array Kalman filter solution to active noise control

A Kalman filter solution to active control and its fast-array implementation are provided. The adaptive control problem is formulated as a state-estimation problem and no interchanging of the adaptive filter and the secondary-path is imposed. Moreover, no estimate of the disturbance signal is needed, and we exploit the structure in the state–space matrices to derive a fast-array implementation....

Kalman Filter in Real Time URBIS