Thesis (M.S in Computer Science) Naval Postgraduate School, March 1996. / Thesis advisor(s): Robert B. McGhee, Anthony Healey. "March 1996." Includes bibliographical references ( p. 121-123).

Thesis (M.S. in Mechanical Engineering) Naval Postgraduate School, March 1997. / Thesis advisor(s): Healey, Anthony J. "March 1997." Includes bibliographical references (p. 151). Also available online.

Dissertation (PhD)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

Thesis (MSc)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

Bernard C. Levy. / Bibliography: leaves 16-17. / "March 1984" / "ECS-83-12921" "AFOSR-82-0135A"

Ahmed H. Tewfik, Bernard C. Levy, Alan S. Willsky. / Bibliography: leaf 21. / "March 1985." / "...supported in part by the National Science Foundation under Grant ECS-83-12921" "...supported... in part by the Army Research Office under Grant No. DAAG-29-84-K-0005."

Mobile devices with wireless networking capabilities are used in a wide range of environments. Geolocation information increases the value of the data generated by a device and is vital in the development of a wide range of applications from autonomous vehicles to the Internet of things. Systems that generate signals specifically for geolocation have become widely adopted but, due to fundamental constraints, lack coverage and accuracy in complex urban and indoor environments. In addition to this, the reliance on a single signal source is not desirable in many applications that value the integrity of the geolocation estimate. A direction of research aiming to improve geolocation in indoor and urban environments measures signals of opportunity in order to generate a more robust estimate. While this approach improves signal availability, the unpredictable nature of these variable and uncontrolled signals leads to poor geolocation estimates, which are typically not suitable for use in many applications. This project aims to improve on the accuracy, resilience and integrity of a geolocation estimate obtained from signal of opportunity measurements in indoor and urban environments while reducing hardware requirements. This has been achieved by efficiently coupling signals of opportunity within the radio environment with other system signals, such as those from an inertial measurement unit. Research has been carried out to optimise the coupling of these data sources resulting in techniques to allow the identification and removal of key error drivers from both the radio environment and other system sensors. This thesis proposes a specifically designed extended Kalman filter to improve on the signal coupling. The filter aims to optimise the accuracy of radio environment measurements while also providing the ability to identify signal error sources in urban and indoor environments, leading to both greater accuracy and resilience of the geo-location estimate. Further, the proposed extended Kalman filter may use the radio environment as a source of geolocation data. The ability of the filter to recognise and mitigate leading radio environment error sources such as multipath and interference allowed the design of filters to obtain detailed and accurate signal strength and time of arrival information. The thesis also presents a thorough set of simulation and modelling experiments to investigate and optimise the efficiency of the proposed solutions in a range of environments. Validation testing confirmed that in the urban and indoor environments, the average error of geo-location estimates has been reduced from 10 m to 3 m without improvement to the hardware surrounding infrastructure. The improvements presented in this thesis allow networked devices to improve the value of their data by incorporating the context that comes from increased geolocation accuracy and resilience. In turn, this allows the development of a wide range of new location based applications for mobile devises in indoor and urban environments.

In this thesis, we have investigated several computational methods on data assimilation for air quality prediction, especially on the characteristic of sparse matrix and the underlying information of gradient in the concentration of pollutant species. In the first part, we have studied the ensemble Kalman filter (EnKF) for chemical species simulation in air quality forecast data assimilation. The main contribution of this paper is to study the sparse data observations and make use of the matrix structure of the Kalman filter updated equations to design an algorithm to compute the analysis of chemical species in the air quality forecast system efficiently. The proposed method can also handle the combined observations from multiple species together. We have applied the proposed method and tested its performance for real air quality data assimilation. Numerical examples have demonstrated the efficiency of the proposed computational method for Kalman filter update, and the effectiveness of the proposed method for NO2, NO, CO, SO2, O3, PM2.5 and PM10 in air quality data assimilation. Within the third part, we have set up an automatic workflow to connect the management system of the chemical transport model - CMAQ with our proposed data assimilation methods. The setup has successfully integrated the data assimilation into the management system and shown that the accuracy of the prediction has risen to a new level. This technique has transformed the system into a real-time and high-precision system. When the new observations are available, the predictions can then be estimated almost instantaneously. Then the agencies are able to make the decisions and respond to the situations immediately. In this way, citizens are able to protect themselves effectively. Meanwhile, it allows the mathematical algorithm to be industrialized implying that the improvements on data assimilation have directly positive effects on the developments of the environment, the human health and the society. Therefore, this has become an inspiring indication to encourage us to study, achieve and even devote more research into this promising method.

With the advent of the Global Position System (GPS), we now have the ability to determine absolute position anywhere on the globe. Although GPS systems work well in open environments with no overhead obstructions, they are subject to large unavoidable errors when the reception from some of the satellites is blocked. This occurs frequently in urban environments, such as downtown New York City. GPS systems require at least four satellites visible to maintain a good position 'fix' . Tall buildings and tunnels often block several, if not all, of the satellites. Additionally, due to Selective Availability (SA), where small amounts of error are intentionally introduced, GPS errors can typically range up to 100 ft or more. This thesis proposes several methods for improving the position estimation capabilities of a system by incorporating other sensor and data technologies, including Kalman filtered inertial navigation systems, rule-based and fuzzy-based sensor fusion techniques, and a unique map-matching algorithm. / Master of Science

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