This paper presents an algorithmic framework for conducting search and identification missions using multiple heterogeneous agents. Dynamic objects of type "neutral" or "target" move through a discretized environment. Probabilistic representation of the current level of situational awareness - knowledge or belief of object locations and identities - is updated with imperfect observations. Optimization of search is formulated as a mixed-integer program to maximize the expected number of targets found and solved efficiently in a receding horizon approach. The search effort is conducted in tandem with object identification and target interception tasks, and a method for assignment of these missions among agents is developed. The proposed framework is demonstrated in simulation studies, and an implementation of its decision support capabilities in a recent field experiment is reported.
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