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Design and Evaluation of Stochastic Processes as Physical Radar Waveforms


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Recent advances in waveform generation and in computational power have enabled the design and implementation of new complex radar waveforms. Still, even with these advances in computation, in a pulse agile mode, where the radar transmits unique waveforms at every pulse, the requirement to design physically robust waveforms which achieve good autocorrelation sidelobes, are spectrally contained, and have aconstant amplitude envelope for high power operation, can require expensive computation equipment and can impede real time operation. This work addresses this concern in the context of FM noise waveforms which have been demonstrated in recent years in both simulation and in experiments to achieve low autocorrelation sidelobes through the high dimensionality of coherent integration when operating in a pulse agile mode. However while they are effective, the approaches to design these waveforms requires the optimization of each individual waveform making them subject to the concern above. This dissertation takes a different approach. Since these FM noise waveforms are meant to be noise like in the first place, the waveforms here are instantiated as the sample functions of a stochastic process which has been specially designed to produce spectrally contained, constant amplitude waveforms with noise like cancellation of sidelobes. This makes the waveform creation process little more computationally expensive than pulling numbers from a random number generator (RNG) since the optimization designs a waveform generating function (WGF) itself rather than each waveform themselves. This goal is achieved by leveraging gradient descent optimization methods to reduce the expected frequency template error (EFTE) cost function for both the pulsed stochastic waveform generation (StoWGe) waveform model and a new CW version of StoWGe denoted CW-StoWGe.



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