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We address the problem of maintaining resource availability in a networkedmulti-robot team performing distributed tracking of unknown number of targetsin an environment of interest. Based on our model, robots are equipped withsensing and computational resources enabling them to cooperatively track a setof targets in an environment using a distributed Probability Hypothesis Density(PHD) filter. We use the trace of a robot's sensor measurement noise covariancematrix to quantify its sensing quality. While executing the tracking task, if arobot experiences sensor quality degradation, then robot team's communicationnetwork is reconfigured such that the robot with the faulty sensor may shareinformation with other robots to improve the team's target tracking abilitywithout enforcing a large change in the number of active communication links. Acentral system which monitors the team executes all the network reconfigurationcomputations. We consider two different PHD fusion methods in this paper andpropose four different Mixed Integer Semi-Definite Programming (MISDP)formulations (two formulations for each PHD fusion method) to accomplish ourobjective. All four MISDP formulations are validated in simulation.

Resilience in multi-robot multi-target tracking with unknown number of targets through reconfiguration