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A novel multitarget tracking algorithm that combines the maximum entropy fuzzy (MEF) clustering data association technique together with Gaussian particle filter (GPF) is presented. Firstly, the MEF clustering approach is provided to deal with the data association problem that arises due to the uncertainty of the measurements, which eliminates those invalidate measurements. Since GPF has much-improved performance and versatility over other Gaussian filters, especially when nontrivial nonlinearities are present, this paper employs it and joint association innovations to update each target state independently. Finally, the proposed algorithm is applied to multitarget bearings-only tracking. Simulation results demonstrate the effectiveness of the algorithm.