This work considers the problem of filtering a system in which the dynamic noise occasionally has an impulse value that is an order of magnitude or more larger than its typical expected distribu-tion. This is particularly challenging when the ratio of measurement noise to typical dynamic noise is large enough that the impulse dynamic noise cannot be easily distinguished from a large random occurrence of measurement noise. A new filter model is proposed using a multiple model approach in which one of the models is an impulse. The implementation of the model is demonstrated in a Kalman filter framework. Simulation results show the improvement of the new filter over existing methods across a range of measurement, typical, and impulse dynamic noises. The filter is then ap-plied to three different problems: 2D human motion tracking using ultra-wideband (UWB) position measurements, power system state estimation on a coupled bus, and handling outlier measurement noise in UWB tracking. In each case the new filter demonstrates a 2-4% improvement over existing state-of-the-art techniques
