In this paper, we develop a decentralized collaborative sensing algorithm where the sensors are located on-board autonomous unmanned aerial vehicles. We develop this algorithm in the context of a target tracking application, where the objective is to maximize the tracking performance measured by the meansquared error between the target state estimate and the ground truth. The tracking performance depends on the quality of the target measurements made at the sensors, which depends on the relative location of the sensors with respect to the target. Our goal is to control the motion of the swarm of vehicles with on-board sensors to maximize target tracking performance. Each sensor (on-board the vehicle) generates local noisy measurements of the target location, and the sensors maintain and update target state estimates via Bayesian data fusion rules using local measurements and the information received from neighboring sensors. The quality of the data fusion depends on the network graph over which the sensors exchange information, and this determines the overall target tracking performance. We also assume that each sensor is powered by a limited energy source; which we assume is drained by how frequently sensors exchange information. The goal is to optimize the collective motion of the vehicles/sensors (also determines the network graph connectivity) such that the mean-squared target tracking error and the network energy costs are jointly minimized. This problem belongs to a class of hard optimization problems called conflicting objective limited resource optimization (COLRO). We develop a fast heuristic algorithm, using dynamic programming principles, to solve this COLRO problem in real-time.