Analysis of the NEKF performance in a multisensor environment
Stephen Craig Stubberud, Kathleen Ann Kramer · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
One technique that has been applied to the tracking of maneuvering targets is the neural extended Kalman filter. The technique adapts the motion model used by the Kalman filter tracker. This adaptation of the model is performed by a neural network that trains on-line using the same residuals as the track states. The behavior of this technique with multiple sensors providing different measurement types and different update rates has not previously been discussed; previous works have always employed a single sensor, usually providing a position measurement or a range-bearing measurement. In actual applications, multiple sensors are typically employed. These sensors often provide measurements at different rates or with the different accuracy. Such issues can have a detrimental effect on the performance of the neural network. The results of multiple-sensor data with variations in the update rates and measurement accuracy to an NEKF estimation system are analyzed. The analysis is based upon the case of two non-collocated sensors providing range-bearing measurements at varying rates applied to the tracking of an actual aircraft flight trajectory.