Universiẗat, Diss., 2002--Bremen.

Universiẗat, Diss., 2001--Bremen.

Universiẗat, Diss., 2000--Tübingen.

Techn. Hochsch., Diss., 2001--Aachen.

Universiẗat, Diss., 2002--Münster (Westfalen).

Universiẗat, Diss., 2002--Münster (Westfalen).

Universiẗat, Diss., 2004--Kassel. / Erscheinungsjahr an der Haupttitelstelle: 2003.

No description available.

Networked, multi-sensor array systems have proven to be advantageous in the sensor world. A large amount of research has been conducted with these systems, with a main interest in data fusion. Intelligently processing the large amounts of data collected by these systems is required in order to fully utilize the benefits of a multi-sensor array system. A robust but flexible simulation environment would provide a platform for accurately comparing current and future data fusion theories. This thesis proposes a simulator model for testing fusion theories for these acoustic multi-sensor networks. An iterative, lossless data fusion algorithm was presented as the model for simulation development. The arrangement and orientation of objects in the simulation environment, as well as most other system parameters are defined by the user before the simulation runs. The sensor data, including noise, is generated at the appropriate time delay and propagation loss before being processed by a delay and sum beamformer and a matched filter. The resulting range-Doppler maps are modified to probability density functions, and translated to a single point of reference. The data is then combined into a single world model. An iterative process is used to filter out false targets and amplify true target detections. Data is fused from each multi-sensor array and from each simulation run. Target amplitudes are gained if they are present in all combined world models, and are otherwise reduced. This thesis presents the results of the fusion algorithm used, including multiple iterations, to prove the algorithms effectiveness. / Master of Science

Unlike the conventional "lock-and-key" sensor design in which one sensor is finely tuned to respond to one analyte, the sensor array approach employs multiple sensors in which one sensor responds to many analytes. Consequently, signal processing algorithms must be used to identify the analyte present from the array's response. The analyte identification process becomes significantly more complicated when a mixture of analytes is presented to the sensor array. Conventional methods that are employed in gas mixture identification are plagued by several design issues like: complexity, scalability, and flexibility. This thesis derives and develops a novel method, fingerprint-based ICA (FICA), to extract and identify individual analytes from a sensor array's response to a gas mixture of the analytes. FICA is a simple, flexible, and scalable signal processing system that employs independent components analysis (ICA) to extract and identify individual analytes present in a gas mixture; separation and identification of gas mixtures using ICA has not been investigated previously. FICA takes a fundamentally different approach that reflects the underlying property of gas mixtures: gas mixtures are composed of individual analyte responses. Conventional signal processing methods that identify gas mixtures have been developed and implemented in this work; this helps us understand the drawbacks in the conventional approach. FICA's performance is compared to the performance of conventional methods using metric like error rate and false positives rate. Properties like flexibility, scalability, and the data requirements for both conventional methods and FICA are examined. Results obtained in this work indicates that FICA results in lower error rates, and it's performance is better than conventional methods like multi-stage multi-stage support vector machines, and PCR. Furthermore, FICA provides the most simple, scalable, and flexible signal processing system. / Master of Science