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Today’s radars face an ever increasingly complex operational environment, intensifiedby the numerous types of mission/modes, number and type of targets, non-homogenousclutter 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, becomesmandatory. This requirement brings with it a host of challenges to implement includingthe basic decision of what to transmit. In this dissertation, we discuss six original contributions,including the development of performance prediction models for constrained radarwaveforms, that aid in the decision making process of an adaptive radar in selecting whatto transmit.It is critical that the algorithms and performance prediction models developed be robustto varying radio frequency interference (RFI) environments. However, the current literatureonly provides toy examples not suitable in representing real-world interference. Therefore,we develop and validate two new power spectral density (PSD) models for interference andnoise, derived from measured data, that allow us to ascertain the effectiveness of an algorithmunder varying conditions.We then investigate the signal-to-interference-and-noise ratio (SINR) performance fora multi-constrained waveform design in the presence of colored interference. We set-upand numerically solve two optimization problems that maximize the SINR while applyinga novel waveform design technique that requires the signal be an ordered subset of eigenvectorsof the interference and noise covariance matrix. The significance of this work is theobservation 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 understandingof the impact practical constraints have on the SINR.Building upon these results, we derive two new performance models, one for the constrainedwaveform SINR and one for the basis-dimension of the eigenvectors of the noiseand interference covariance matrix required to achieve a particular modulus constraint.Radar waveforms typically require a constant modulus (constant amplitude) transmit signalto efficiently exploit the available transmit power. However, recent hardware advancesand the capability for arbitrary (phase and amplitude) designed waveforms have forced are-examination of this assumption to quantify the impact of modulus perturbation fromphase 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 radarwaveform design as it pertains to SINR performance. We seek to further extend the stateof-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 appliedand second, the basis dimension of the noise and interference covariance matrix required togenerate the waveform. With our approach, we are able show exceptional ability to predictthe impact to SINR as we tighten the ISL constraint in the waveform design. For all performancemodels, we include Monte Carlo simulation trials designed to measure the impactof ISL on SINR as well as compare performance when measured data is used to representthe interference and noise covariance matrix. |
