Publication Date
2016
Document Type
Dissertation
Committee Members
Christopher Baker (Committee Member), Fred Garber (Committee Member), Muralidhar Rangaswamy (Committee Member), Brian Rigling (Advisor), Wu Zhiqiang (Committee Member)
Degree Name
Doctor of Philosophy (PhD)
Abstract
Today's radars face an ever increasingly complex operational environment, intensified by the numerous types of mission/modes, number and type of targets, non-homogenous clutter and active interferers in the scene. Thus, the ability to adapt ones transmit waveform, to optimally suit the needs for a particular radar tasking and environment, becomes mandatory. This requirement brings with it a host of challenges to implement including the basic decision of what to transmit. In this dissertation, we discuss six original contributions, including the development of performance prediction models for constrained radar waveforms, that aid in the decision making process of an adaptive radar in selecting what to transmit. It is critical that the algorithms and performance prediction models developed be robust to varying radio frequency interference (RFI) environments. However, the current literature only provides toy examples not suitable in representing real-world interference. Therefore, we develop and validate two new power spectral density (PSD) models for interference and noise, derived from measured data, that allow us to ascertain the effectiveness of an algorithm under varying conditions. We then investigate the signal-to-interference-and-noise ratio (SINR) performance for a multi-constrained waveform design in the presence of colored interference. We set-up and numerically solve two optimization problems that maximize the SINR while applying a novel waveform design technique that requires the signal be an ordered subset of eigenvectors of the interference and noise covariance matrix. The significance of this work is the observation of the non-linearity in the SINR performance as a function of the constraints. This inspires the development of performance prediction models to obtain a greater understanding of the impact practical constraints have on the SINR. Building upon these results, we derive two new performance models, one for the constrained waveform SINR and one for the basis-dimension of the eigenvectors of the noise and interference covariance matrix required to achieve a particular modulus constraint. Radar waveforms typically require a constant modulus (constant amplitude) transmit signal to efficiently exploit the available transmit power. However, recent hardware advances and the capability for arbitrary (phase and amplitude) designed waveforms have forced a re-examination of this assumption to quantify the impact of modulus perturbation from phase only signals. The models are validated with measured data and through Monte Carlo (MC) simulation trials. Lastly, we develop the role of the integrated sidelobe (ISL) parameter for adaptive radar waveform design as it pertains to SINR performance. We seek to further extend the state-of-the-art by developing two new performance models for the integrated sidelobe metric. First, the corresponding SINR degradation, from optimal as the ISL constraint is applied and second, the basis dimension of the noise and interference covariance matrix required to generate the waveform. With our approach, we are able show exceptional ability to predict the impact to SINR as we tighten the ISL constraint in the waveform design. For all performance models, we include Monte Carlo simulation trials designed to measure the impact of ISL on SINR as well as compare performance when measured data is used to represent the interference and noise covariance matrix.
Page Count
167
Department or Program
Ph.D. in Engineering
Year Degree Awarded
2016
Copyright
Copyright 2016, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
