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Waveform design that allows for a wide variety of frequency-modulation (FM) has proven benefits. However, dictionary based optimization is limited and gradient search methods are often intractable. A new method is proposed using differential evolution to design waveforms with instantaneous frequencies (IFs) with cubic FM functions whose coefficients are constrained

Waveform design that allows for a wide variety of frequency-modulation (FM) has proven benefits. However, dictionary based optimization is limited and gradient search methods are often intractable. A new method is proposed using differential evolution to design waveforms with instantaneous frequencies (IFs) with cubic FM functions whose coefficients are constrained to the surface of the three dimensional unit sphere. Cubic IF functions subsume well-known IF functions such as linear, quadratic monomial, and cubic monomial IF functions. In addition, all nonlinear IF functions sufficiently approximated by a third order Taylor series over the unit time sequence can be represented in this space. Analog methods for generating polynomial IF waveforms are well established allowing for practical implementation in real world systems. By sufficiently constraining the search space to these waveforms of interest, alternative optimization methods such as differential evolution can be used to optimize tracking performance in a variety of radar environments. While simplified tracking models and finite waveform dictionaries have information theoretic results, continuous waveform design in high SNR, narrowband, cluttered environments is explored.
ContributorsPaul, Bryan (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel W (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
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Description
The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local, discrete convolution kernels, but this is not possible when samples are measured directly in the frequency domain. The concentration factor

The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local, discrete convolution kernels, but this is not possible when samples are measured directly in the frequency domain. The concentration factor edge detection method was therefore developed to realize an edge detector directly from spectral data. This thesis explores the possibilities of detecting edges from the phase of the spectral data, that is, without the magnitude of the sampled spectral data. Prior work has demonstrated that the spectral phase contains particularly important information about underlying features in a signal. Furthermore, the concentration factor method yields some insight into the detection of edges in spectral phase data. An iterative design approach was taken to realize an edge detector using only the spectral phase data, also allowing for the design of an edge detector when phase data are intermittent or corrupted. Problem formulations showing the power of the design approach are given throughout. A post-processing scheme relying on the difference of multiple edge approximations yields a strong edge detector which is shown to be resilient under noisy, intermittent phase data. Lastly, a thresholding technique is applied to give an explicit enhanced edge detector ready to be used. Examples throughout are demonstrate both on signals and images.
ContributorsReynolds, Alexander Bryce (Author) / Gelb, Anne (Thesis director) / Cochran, Douglas (Committee member) / Viswanathan, Adityavikram (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
RF convergence of radar and communications users is rapidly becoming an issue for a multitude of stakeholders. To hedge against growing spectral congestion, research into cooperative radar and communications systems has been identified as a critical necessity for the United States and other countries. Further, the joint sensing-communicating paradigm appears

RF convergence of radar and communications users is rapidly becoming an issue for a multitude of stakeholders. To hedge against growing spectral congestion, research into cooperative radar and communications systems has been identified as a critical necessity for the United States and other countries. Further, the joint sensing-communicating paradigm appears imminent in several technological domains. In the pursuit of co-designing radar and communications systems that work cooperatively and benefit from each other's existence, joint radar-communications metrics are defined and bounded as a measure of performance. Estimation rate is introduced, a novel measure of radar estimation information as a function of time. Complementary to communications data rate, the two systems can now be compared on the same scale. An information-centric approach has a number of advantages, defining precisely what is gained through radar illumination and serves as a measure of spectral efficiency. Bounding radar estimation rate and communications data rate jointly, systems can be designed as a joint optimization problem.
ContributorsPaul, Bryan (Author) / Bliss, Daniel W. (Thesis advisor) / Berisha, Visar (Committee member) / Kosut, Oliver (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2017