Title :
Interacting multiple model tracking using a neural extended Kalman filter
Author :
Owen, Mark W. ; Stubberud, Stephen C.
Author_Institution :
Orincon Corp., San Diego, CA, USA
Abstract :
We discuss the incorporation of the author´s neural extended Kalman filter (NEKF) into an interacting multiple model (IMI) architecture for use in multisensor target tracking. The NEKF is used in conjunction with a straight-line motion model. We compare this IMM implementation to a tracker with a straight-line motion model. The NEKF allows us to track through a manoeuvre with better precision than a straight-line motion model with high process noise because it is able to learn the manoeuvre online and improve the model prediction. Therefore, the NEKF better approximates the true dynamics of the target´s motion which improves the overall tracking performance
Keywords :
Kalman filters; covariance matrices; filtering theory; neural nets; nonlinear filters; sensor fusion; state estimation; target tracking; interacting multiple model tracking; model prediction; multisensor target tracking; neural extended Kalman filter; overall tracking performance; straight-line motion model; Acceleration; Equations; Filters; Mathematical model; Motion measurement; Neural networks; Noise measurement; Predictive models; State estimation; Target tracking;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.833522
