On-line sensor modeling using a neural Kalman filter

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 source. While there are well known techniques to compensate for those that result from such issues as noise and sensor accuracy limitations, other types of errors, 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 upon the neural extended Kalman filter is proposed as a technique to provide for on-line 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. Here, the sensor dynamics are learned rather than the target dynamics.