We study the problem of controlling multiple 2-D directional sensors while maximizing an objective function based on the information gain corresponding to multiple target locations. We assume a joint prior Gaussian distribution for the target locations. A sensor generates a (noisy) measurement of a target only if the target lies within the field-of-view of the sensor, where the statistical properties of the measurement error depend on the location of the target with respect to the sensor and direction of the sensor. The measurements from the sensors are fused to form global estimates of target locations. This problem is combinatorial in nature-the computation time increases exponentially with the number of sensors. We develop heuristic methods to solve the problem approximately, and provide analytical results on performance guarantees. We then improve the performance of our heuristic approaches by applying an approximate dynamic programming approach called rollout. In addition, we address a variant of the above problem, where the goal is to map the sensors to the targets while maximizing the abovementioned objective function. This mapping problem also turns out to be combinatorial in nature, so we extend one of the above heuristics to solve this mapping problem approximately. We compare the performance of these heuristic approaches analytically and empirically.