Tracking moving optima using Kalman-based predictions

The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison

Three-axis attitude determination via Kalman filtering of magnetometer data

A three-axis Magnetometer/Kalman Filter attitude determination system for a spacecraft in low-altitude Earth orbit is developed, analyzed, and simulation tested. The motivation for developing this system is to achieve light weight and low cost for an attitude determination system. The extended Kalman filter estimates the attitude, attitude rates, and constant disturbance torques. Accuracy near that of the International Geomagnetic Reference Field model is achieved. Covariance computation and simulation testing demonstrate the filter's accuracy. One test case, a gravity-gradient stabilized spacecraft with a pitch momentum wheel and a magnetically-anchored damper, is a real satellite on which this attitude determination system will be used. The application to a nadir pointing satellite and the estimation of disturbance torques represent the significant extensions contributed by this paper. Beyond its usefulness purely for attitude determination, this system could be used as part of a low-cost three-axis attitude stabilization system

Local ensemble transform Kalman filter, a fast non-stationary control law for adaptive optics on ELTs: theoretical aspects and first simulation results

We propose a new algorithm for an adaptive optics system control law, based on the Linear Quadratic Gaussian approach and a Kalman Filter adaptation with localizations. It allows to handle non-stationary behaviors, to obtain performance close to the optimality defined with the residual phase variance minimization criterion, and to reduce the computational burden with an intrinsically parallel implementation on the Extremely Large Telescopes (ELTs).Comment: This paper was published in Optics Express and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://www.opticsinfobase.org/oe/ . Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under la

Efficient detection and signal parameter estimation with applications to high dynamic GPS receivers

A novel technique for simultaneously detecting data and estimating the parameters of a received carrier signal phase modulated by unknown data and experiencing very high Doppler, Doppler rate, etc. is discussed. Such a situation arises, for example, in the case of Global Positioning Systems (DPS) where the signal parameters are directly related to the position, velocity and acceleration of the GPS receiver. The proposed scheme is based upon first estimating the received signal local (data dependent) parameters over two consecutive bit periods, followed by the detection of a possible jump in these parameters. The presence of a detected jump signifies a data transition which is then removed from the received signal. This effectively demodulated signal is then processed to provide the estimates of global (data independent) parameters of the signal related to the position, velocity, etc. of the receiver. One of the key features of the proposed algorithm is the introduction of two different schemes which can provide an improvement of up to 3 dB over the conventional implementation of Kalman filter as applied to phase and frequency estimation, under low to medium signal-to-noise ratio conditions