  This paper presents a cooperative multi-robot multi-target tracking framework
aimed at enhancing the efficiency of the heterogeneous sensor network and,
consequently, improving overall target tracking accuracy. The concept of
normalized unused sensing capacity is introduced to quantify the information a
sensor is currently gathering relative to its theoretical maximum. This
measurement can be computed using entirely local information and is applicable
to various sensor models, distinguishing it from previous literature on the
subject. It is then utilized to develop a distributed coverage control strategy
for a heterogeneous sensor network, adaptively balancing the workload based on
each sensor's current unused capacity. The algorithm is validated through a
series of ROS and MATLAB simulations, demonstrating superior results compared
to standard approaches that do not account for heterogeneity or current usage
rates.
