We develop tractable solutions to the problem of controlling the directions of 2-D directional sensors for maximizing information gain corresponding to multiple targets in 2-D. The target locations are known with some uncertainty given by a joint prior distribution (Gaussian). A sensor generates a (noisy) measurement of a target only if the target lies within the field-of-view of the sensor, and the measurements from all the sensors are fused to form global estimates of target locations. This problem is hard to solve exactly - the computation time increases exponentially with the number of sensors. We develop heuristic methods to solve the problem approximately and provide lower and upper bounds on the optimal information gain. We improve the solutions from these heuristic approaches by formulating the problem as a dynamic programming problem and solving it using a rollout approach.