In many applications of radar systems, detection of targets in environments with heavy clutter and interference can be difficult. It is desired that a radar system should detect targets at a further range as well as be able to detect these targets with very few false positive or negative readings. In a cognitive radar system, there are ways that these negative effects can be mitigated and target detection can be significantly improved. An important metric to focus on for increasing target detectability is the signal-to-clutter ratio (SCR). Cognitive radar offers solutions to issues such as this with the use of a priori knowledge of targets and environments as well as real time adaptations. A feature of cognitive radar that is of interest is the ability to adapt and optimize transmitted waveforms to a given situation. A database is used to hold a priori and dynamic knowledge of the operational environment and targets to be detected, such as clutter characteristics and target radar cross-section (RCS) estimations. Assuming this knowledge is available or can be estimated in real-time, the transmitted waveform can be tailored using methods such as transmission of a spectrum corresponding to the target-to-clutter ratio (TCR). These methods provide significant improvement in distinguishing targets from clutter or interference.