This paper presents the application of a novel target tracking technique employing tensor decompositions for discretizing the target state space. The time evolution of the conditional probability density is realized by a Fokker-Planck equation solver and the measurement update, as usual, by applying Bayes’ rule. The method is applicable to non-Gaussian and non-linear system equations and enables the treatment of complex non-Gaussian target state densities. In addition, the efficient tensor decomposition scheme, in principle, allows for high-dimensional target states. The new tracking filter is applied to the problem of tracking an agile air target using bearing measurements from distributed acoustic and electromagnetic array sensors based on real data. It is shown that the new filter is able to initiate and maintain the target track with localization errors comparable to those of a standard particle filter.