Whether sensor model�s inaccuracies are a result of poor initial modeling or from sensor damage or drift, the effects can be just\r\nas detrimental. Sensor modeling errors result in poor state estimation. This, in turn, can cause a control system relying upon\r\nthe sensor�s measurements to become unstable, such as in robotics where the control system is applied to allow autonomous\r\nnavigation. A technique referred to as a neural extended Kalman filter (NEKF) is developed to provide both state estimation in a\r\ncontrol loop and to learn the difference between the true sensor dynamics and the sensor model. The technique requires multiple\r\nsensors on the control system so that the properly operating and modeled sensors can be used as truth. The NEKF trains a neural\r\nnetwork on-line using the same residuals as the state estimation. The resulting sensor model can then be reincorporated fully into\r\nthe system to provide the added estimation capability and redundancy.
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