We show how the performance of a sensor management algorithm can be improved by using the continuous transferable belief model (cTBM). Bayesian approaches can have problems modelling uncertainty whereas the transferable belief model (TBM) has been proven effective in dealing with this - the TBM achieves this by assigning support to sets of events rather than just singleton events. The discrete nature of such set theoretic uncertain reasoning approaches (including Dempster-Shafer approaches) can have problems modelling continuous signals such as the speed of a target; the cTBM has been developed to overcome such inherent problems. Existing work at Cardiff University classifies targets by combining the cTBM and a particle filter; each particle is used to construct a set of beliefs, which are then fused with the existing beliefs for classification - this is then used to update the particle filter. Williams et al. provide a framework for managing sensor networks by balancing the quality of information gained from a sensor network with the required communications cost when tracking a target. Our proposed new system integrates the above approaches, and has similar basic communications costs to the latter. It is now not only able to track a target but also classify it - the combination results in improved performance; this is shown in our simulation results from Monte Carlo trials with various scenarios.