MULTIPARAMETER STATISTICAL SENSITIVITY OF ACTIVE AND PASSIVE FILTERS.
AuthorZak, Francis Anthony.
Electric filters -- Data processing.
Electric filters, Active.
Electric filters, Active -- Data processing.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Degree ProgramGraduate College
Degree GrantorUniversity of Arizona
Showing items related by title, author, creator and subject.
PRESAMPLING FILTERING, SAMPLING AND QUANTIZATION EFFECTS ON THE DIGITAL MATCHED FILTER PERFORMANCEChang, Horen; Stanford Telecommunications, Inc. (International Foundation for Telemetering, 1982-09)Due to the increased capability and reduced cost of digital devices, there has recently been a growing trend to digitize the matched-filtering data detector in the receiver. Comparing with an idealized integrate-and-dump analog matched filter, the digital matched filter (DMF) requires more Eb /No in order to achieve the same bit error rate performance because of the presampling filtering, sampling, and quantization effects. This paper analyzes the performance degradation resulting, separately and jointly, from these three effects. Quantitative results are provided for commonly chosen sets of design parameters. For a given performance degradation budget and complexity limitation, these results could be applied to choose the optimum DMF design parameters including the presampling filter bandwidth, the sampling rate, the number of quantization bits, and the spacing between adjacent quantization levels.
What the collapse of the ensemble Kalman filter tells us about particle filtersMorzfeld, Matthias; Hodyss, Daniel; Snyder, Chris; Univ Arizona, Dept Math (TAYLOR & FRANCIS LTD, 2017)The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems. We explain that these seemingly contradictory statements offer insights about how PF function in certain high-dimensional problems, and in particular support recent efforts in meteorology to 'localize' particle filters, i.e. to restrict the influence of an observation to its neighbourhood.
An analysis of state-variable filters and development of modified state-variable filters.Oksasoglu, Ali. (The University of Arizona., 1991)This study extends the idea and the technique of realizing 2ⁿᵈ-order state variable filters to the realization of single block nᵗʰ-order state variable filters and introduces a modified realization with different design procedures. The effects of gain-bandwidth on the performance of state variable filter realizations with respect to properties, such as, magnitude and phase characteristics and the actual pole locations are investigated and discussed. Various methods of compensation for these effects are also addressed.