This paper focuses on the development of decentralized collaborative sensing and sensor resource allocation algorithms where the sensors are located on-board autonomous unmanned aerial vehicles. We develop these algorithms in the context of single-target and multi-target tracking applications, where the objective is to maximize the tracking performance as measured by the mean-squared error between the target state estimate and the ground truth while minimizing the energy costs. 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 targets. Our goal is to control the motion of the swarm of vehicles with on-board sensors to maximize the target tracking performance. Each sensor 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 the neighboring sensors. The quality of the data fusion depends on the network graph over which the sensors exchange information and the relative distance between sensors, and these determine the overall target tracking performance. For the case of multi-target tracking scenario, we also introduce sensor assignment graph in order to allocate the sensors to appropriate targets and maximize the overall 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 of our study is to optimize the collective motion of the vehicles/sensors (also determines the network graph connectivity and sensor assignment graph connectivity for multi-target tracking) 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 fast heuristic algorithms, using dynamic programming principles, to solve this COLRO problem in real-time using a numerical optimization solver called Knitro, and we evaluate its performance against a widely used particle swarm optimization approach.

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