Monopulse radar tracking using an adaptive interacting multiple model method with extended Kalman filters

In this paper we investigate an adaptive interacting multiple model (AIMM) tracker using the extended Kalman filter. This adaptive algorithm is based on the interacting multiple model (IMM) tracking technique with the addition of an adaptive acceleration model to track behavior that falls in between the fixed model dynamics. In previous research, we found that the adaptive model matches more closely the hue system dynamics when the target kinematics lie in between the fixed models, thus improving the overall performance of the tracking system. We also showed that the AIMM outperforms other existing adaptive approaches while reducing computational complexity. In this paper, we further investigate these superior qualities of the AIMM by considering a more realistic radar-tracking scenario where monopulse radar range, azimuth, and elevation measurements are processed using extended Kalman filters in the AIMM. Here a more complex three-dimensional simulation is implemented instead of the simplified two dimensional problem considered in our previous research. Again, the results show that the AIMM outperforms the classical IMM when the target is maneuvering.