Cognitive Radar (CR) is a paradigm shift from a traditional radar system in that previous knowledge and current measurements obtained from the radar channel are used to form a probabilistic understanding of its environment. Moreover, CR incorporates this probabilistic knowledge into its task priorities to form illumination and probing strategies thereby rendering it a closed-loop system. Depending on the hardware's capabilities and limitations, there are various degrees of freedom that a CR may utilize. Here we will concentrate on two: temporal, where it is manifested in adaptive waveform design; and spatial, where adaptive beamsteering is used for search-and-track functions. This work is divided into three parts. First, comprehensive theory of SNR and mutual information (MI) matched waveform design in signal-dependent interference is presented. Second, these waveforms are used in a closed-loop radar platform performing target discrimination and target class identification, where the extended targets are either deterministic or stochastic. The CR's probabilistic understanding is updated via the Bayesian framework. Lastly, we propose a multiplatform CR network for integrated search-and-track application. The two radar platforms cooperate in developing a four-dimensional probabilistic understanding of the channel. The two radars also cooperate in forming dynamic spatial illumination strategy, where beamsteering is matched to the channel uncertainty to perform the search function. Once a target is detected and a track is initiated, track information is integrated into the beamsteering strategy as part of CR's task prioritization.

In this dissertation, methods utilizing matched illumination theory to optimally design waveforms for enhanced target detection and identification in the context of through-the-wall radar (TWR) are explored. The accuracy of assumptions made in the waveform design process is evaluated through simulation. Additionally, the moisture profile of an adobe wall is investigated, and it is shown that the moisture profile of the wall will introduce significant variations in the matched illumination waveforms and subsequently, affect the resulting ability of the radar system to correctly identify and detect a target behind the wall. Experimental measurements of adobe wall moisture and corresponding dielectric properties confirms the need for accurate moisture profile information when designing radar waveforms which enhance signal-to-interference-plus-noise ratio (SINR) through use of matched illumination waveforms on the wall/target scenario. Furthermore, an evaluation of the ability to produce an optimal, matched illumination waveform for transmission using simple, common radar systems is undertaken and radar performance is evaluated.