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Online Sensor Modeling Using a Neural Kalman Filter

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3 Author(s)
Stubberud, S.C. ; Rockwell Collins, Poway ; Kramer, K.A. ; Geremia, J.A.

Sensor-measurement systems rely upon knowledge of the functional dynamics between system states and the measured outputs. Errors in sensor measurements come from a variety of sources. While there are well-known techniques to compensate for those errors that result from such issues as noise and sensor-accuracy limitations, other types, such as those that are more deterministic, can result in biases that are not easily compensated for in standard systems. A modification of an adaptive tracking technique based on the neural extended Kalman filter is proposed as a technique to provide for online calibration for the sensor models. Previously, the technique has been applied to tracking problems and successfully improved the motion model of a target when a maneuver occurs. In this new application of the technique, the sensor dynamics are learned rather than the target dynamics.

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:56 ,  Issue: 4 )