Abstract
Abstract Two single-sensor tracking algorithms, Joint Probabilistic Data Association (JPDA) and Mixture Reduction (MR), are extended for use in multisensor multitarget tracking situations, under the assumption that the sensor measurement errors are independent across sensors. In the formulations for both multisensor algorithms, the equations for the calculation of the data association probabilities have been put in the same form as for the JPDA, thus allowing previously developed fast JPDA computational techniques to be applicable. The computational complexity of these multisensor algorithms is discussed, and simulation results are presented demonstrating and comparing the performances of these and other multisensor fusion algorithms.
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