Contributions to spatio-temporal signal processing
Abstract
In many applications, such as satellite communications and imaging, an array of sensors is used to spatially and temporally sample a propagating wavefield that has contributions from the signals of interest as well as interference and additive noise. Since the propagating field is a function of both space and time, its dependence on either or both of these features may be exploited in order to enhance the detectability of the signals of interest. Traditionally, direction of arrival information has been used to spatially filter the incident wavefields. However, there is an emerging class of cyclic direction finding techniques that also exploits cyclostationarity (which is a temporal property) to further attenuate the effects of co-channel interference and noise. Part I of this dissertation is centered around the development of three new techniques which are designed to advance the state of the art in spatio-temporal signal processing. Part II focuses on the development of a new algorithm for time delay estimation in spread spectrum communications. The apparently dissimilar problems investigated in Parts I and II are unified by the commonality in the techniques used. In Part II, we apply the MUSIC algorithm, which has traditionally been used for direction finding, to the problem of time delay estimation in spread spectrum communications. The contributions of this dissertation are the (i) development of a new self-calibration and direction finding algorithm (assuming a single source); (ii) theoretical performance evaluation of the calibration algorithm; (iii) derivation of a Constrained Cramer-Rao Lower Bound on mean-squared estimation error; (iv) development of a new self-calibration algorithm (assuming multiple sources); (v) development of a new frequency focusing-cyclic direction finding technique that generates virtual array 'observations'; (vi) development of a new time delay estimation algorithm for spread spectrum communications that maps the usual K-dimensional timing problem (for a K-user system) to K one-dimensional problems and; (vii) derivation of a closed form expression for the Cramer-Rao Lower Bound for mean-squared time delay estimation. For each problem, a representative body of simulations is presented to verify estimator performance.
Subject Area
Electrical engineering|Remote sensing
Recommended Citation
Wylie, Marilynn Philamena, "Contributions to spatio-temporal signal processing" (1995). Dissertations available from ProQuest. AAI9543158.
https://repository.upenn.edu/dissertations/AAI9543158