This chapter has been devoted to the design of a tracker exploiting cognition at multiple levels. Specifically, environmental maps and characteristics of the targets, available in the dynamic database possibly learned from the feedback channel, have been used to gain improved tracking performance in a multiple targets scenario exploiting measurements provided by a tracking radar. Unlike the conventional tracking radar (which is very sensitive to false alarms and/or missed detections), the main advantage of the cognitive paradigm is the significant reduction in the number of false alarms, missed detections, false tracks, and improved true target track life. In the second part of the chapter, the focus has been on waveform selection to optimize the target tracking process. Specifically, it has been assumed that a waveform library is available at the transmitter, and the most suitable signal (in the sense of minimizing the predicted tracking estimation error) is chosen for the next dwell. The proposed algorithm is based on the use of feedback information from the receiver and exploits a standard KF. The performance of the proposed strategy has been studied in a challenging scenario accounting for a maneuvering target in the presence of thermal noise only or RF interference plus thermal noise. The results have highlighted that the adaptive feedback process guiding the waveform selection is able to provide advantages over the classic radar tracker, which does not resort to transmit adaptivity.