Abstract
Cognitive radar problems involve the selection of actions based on the uncertain knowledge of a system state that is partially observed through noisy measurements. This process of sequential decision making under uncertainty can be considered as a stochastic optimization problem. This paper explicitly makes the connection between cognitive radar and stochastic optimization by presenting a framework for describing cognitive radar problems in terms of stochastic optimization, thereby pointing to ways to employ stochastic optimization for designing perception-action cycles in a cognitive radar.