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Sequential Monte Carlo (SMC) methods, also referred to as particle filters, have been successfully applied to a variety of highly nonlinear problems such as target tracking with sensor networks. In this paper, we propose the application of a new class of SMC methods named cost-reference particle filters (CRPFs) to target tracking with mobile sensors. CRPF techniques have been shown to be a flexible and robust alternative when there is no knowledge about the probability distributions of the noise in the system. The sensors positioning during tracking is determined by the predicted target's location as obtained by the CRPF. The performance of the method is investigated by simulations and compared to tracking with standard particle filters (SPFs).