The long-term goal of this work is to enable agents with low-information sensors to perform tasks usually restricted to ones with more sophisticated, high-information sensing capabilities. Our approach is to regulate the motion of these low-information agents to obtain “high-information” results. As a first step, we consider a multi-agent system tasked with locating and tracking a moving target using only noisy binary sensors that measure the presence (or lack thereof) of a target in the sensor's field of view. To generate effective paths for these agents, we use ergodic trajectory optimization with a novel mutual information map that is fast to compute and can handle the discontinuous measurement models often associated with low-information sensing. We compare our approach with existing motion planning methods in multiple simulated experiments. Our experiments show that agents using our method outperform purely coverage-based approaches as well as naive ergodic approaches.