Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004. / Cover title. Includes bibliographical references (p. 129-134). Also available online from the MIT website (http://www.mit.edu/).

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

M.Ing. / Vehicle attitude is the most significant of the navigational parameters in terms of its influence on accumulated dead reckoning errors. To determine the attitude of the host vehicle body, with respect to the earth, it is necessary to keep track of the orientation of the body axes with respect to the local earth navigational frame (north, east and down). The aim of this research is to investigate the feasibility and the enhancement of low cost inertial sensors (such as gyroscopes) by the addition of magnetometer and pitch and roll angle sensors. The focus of this research is on the use of low cost inertial measurement systems to determine the attitude of a vehicle body. Strapdown system principles and the estimation theory are applied to achieve this goal. Both Euler angles and Quatemions as attitude representation are implemented and compared with one another. Work is concentrated around the mathematical models for low cost sensors and the attitude system dynamics. A sensor cluster is constructed using three gyroscopes, a magnetometer and two inclinometers. These inertial sensors were integrated using a Kalman filter. The mathematics, calculations and principles used are universal for all attitude systems. Practical data was recorded after which it was filtered to illustrate the working of the Kalman filter. The addition of a magnetometer and two inclinometers are indeed feasible for enhancing the attitude obtained from the inertial sensors. The benefit associated with the gyroscopes, when the magnetometer readings are disturbed by external magnetic anomalies, where small and of little significance. This thesis fully describes the theory and approach followed to implement the Kalman filter, making this a good example of a Kalman filter implementation, especially with the MATLAB software realisation presented in the appendix.

M.Ing. (Electrical & Electronic Engineering) / Please refer to full text to view abstract

This paper sheds light on the differences and similarities in natural gas trading at the National Balancing Point in the UK and the Henry Hub located in the US. For this, we analyze traders' expectations and implement a mechanical forecasting model that allows traders to predict future spot prices. Based on this, we compute the deviations between expected and realized spot prices and analyze possible reasons and dependencies with other market variables. Overall, the mechanical predictor performs well, but a small forecast error remains which can not be characterized by the explanatory variables included.

Using a Kalman filter that contains a forward-predictive model of a relevant system, to predict the states of that system by means of an analysis-by-synthesis implementation in order to evade significant time delays incurred by feedback mechanisms was previously applied to the coordinated movement of limbs by means of the cerebellum. In this dissertation, the same concept was applied to the auditory system in order to investigate if such a concept is a universal neurophysiological method for correctly estimating a state in a quick and reliable way. To test this assumption an auditory system model and Kalman estimator were designed, where the Kalman filter contained a stochastically equivalent forward-predictive model of the complete auditory system model. The Kalman filter was used to estimate the power found in a particular band of the frequency spectrum and its performance in the mean-squared error sense was compared to that of a simple postsynaptic current decoding filter under various types of neural channel noise. It was shown that the Kalman filter, containing a biologically plausible internal model could estimate the power better than a postsynaptic current decoding filter, proposed in the literature. When the just-noticeable difference in intensity discrimination, as reported in the literature, was compared to model-predictions, it was shown that a smaller mean-squared error results in the case of the designed auditory system model and Kalman estimator. This suggests that the application of the Kalman filter concept is important as it provides a bridge between measured data and the auditory system model. It was concluded that a Kalman filter model containing a biologically plausible internal model can explain some characteristics of the signal processing of the auditory system. The research suggests that the principle of an estimator that contains an internal model could be a universal neurophysiological method for the correct estimation of a desired state. / Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2006. / Electrical, Electronic and Computer Engineering / unrestricted

The first experimental demonstration of feedback suppression of rotating external kink modes near the ideal wall limit in a tokamak using Kalman filtering to discriminate the n = 1 kink mode from background noise is reported. In order to achieve the highest plasma pressure limits in tokamak fusion experiments, feedback stabilization of long-wavelength, external instabilities will be required, and feedback algorithms will need to distinguish the unstable mode from noise due to other magnetohydrodynamic activity. When noise is present in measurements of a system, a Kalman filter can be used to compare the measurements with an internal model, producing a realtime, optimal estimate for the system's state. For the work described here, the Kalman filter contains an internal model that captures the dynamics of a rotating, growing instability and produces an estimate for the instability's amplitude and spatial phase. On the High Beta Tokamak-Extended Pulse (HBT-EP) experiment, the Kalman filter algorithm is implemented using a set of digital, field-programmable gate array controllers with 10 microsecond latencies. The feedback system with the Kalman filter is able to suppress the external kink mode over a broad range of spatial phase angles between the sensed mode and applied control field, and performance is robust at noise levels that render feedback with a classical, proportional gain algorithm ineffective. Scans of filter parameters show good agreement between simulation and experiment, and feedback suppression and excitation of the kink mode are enhanced in experiments when a filter made using optimal parameters from the experimental scans is used.

The use of small satellites to measure the native electric field of the earth has historically presented many problems as a result of the generally modest pointing capabilities of small satellites. In spite of this, the cost of small satellites makes them ideal for just such scientic missions. This thesis details many of the constraints of electric field measuring missions as well as the requirements on any spacecraft designed to accomplish such. The data from a small sounding rocket mission is then analyzed and its usefulness discussed. Possible other methods for use are also discussed.

No description available.

Sensors are getting smaller, inexpensive and sophisticated, with an increased availability. Compared to 25 years ago, an object tracking system now can easily achieve twice the accuracy, a much larger coverage and fault tolerance, without any significant changes in the overall cost. This is possible by simply employing more than just one sensor and processing measurements from individual sensors sequentially (or even in a batch form). %This is the centralized scheme of multi-sensor target tracking wherein the sensors send their individual detections to a central facility, where tracking related tasks such as data association, filtering, and track management etc. are performed. This is also perhaps the simplest solution for a multi-sensor approach and also optimal in the sense of minimum mean square error (MMSE) among all other multi-sensor scenario. In sophisticated sensors, the number of detections can reach thousands in a single frame. The communication and computation load for gathering all such detections at the fusion center will hamper the system's performance while also being vulnerable to faults. A better solution is a distributed architecture wherein the individual sensors are equipped with processing capabilities such that they can detect measurements, extract clutter, form tracks and transmit them to the fusion center. The fusion center now fuses tracks instead of measurements, due to which this scheme is commonly termed track-level fusion. In addition to sub-optimality, the track-level fusion suffers from a very coarse problem, which occurs due to correlations between the tracks to be fused. Often, in realistic scenarios, the cross-correlations are unknown, without any means to calculate them. Thus, fusion cannot be performed using traditional methods unless extra information is transmitted from the fusion center. This thesis proposes a novel and generalized method of fusing any two probability density functions (pdf) such that a positive cross-correlation exists between them. In modern tracking systems, the tracks are essentially pdfs and not necessarily Gaussian. We propose harmonic mean density based fusion and prove that it obeys all the necessary requirements of being a viable fusion mechanism. We show that fusion in this case is a classical example of agreement between the fused and participating densities based on average $\chi^2$ divergence. Compared to other such fusion techniques in the literature, the HMD performs exceptionally well. Transmitting covariance matrices in distributed architecture is not always possible in cases for e.g. tactical and automotive systems. Fusion of tracks without the knowledge of uncertainty is another problem discussed in the thesis. We propose a novel technique for local covariance reconstruction at the fusion center with the knowledge of estimates and a vector of times when update has occurred at local sensor node. It has been shown on a realistic scenario that the reconstructed covariance converges to the actual covariance, in the sense of Frobenius norm, making fusion without covariance, possible. / Thesis / Doctor of Philosophy (PhD)