An underwater maneuvering target tracking method with compensation mechanism under short-term sensor failure

In the field of underwater maneuvering target tracking, the detection of sensors is not effective in real time. Under man-made or non-man-made interference, the observer only receives the noise value or abnormal value, that is, the sensor will fail for a short time. Once the sensor fails to obtain correct data continuously for a period of time, this is fatal to the filter. Aiming at the short-term failure of sensors, this paper proposes a tracking method for surface targets with compensation mechanism. This method uses the idea of deep learning to extract enough training samples from the observation data, and uses the samples to train the Long Short-Term Memory (LSTM) neural network with time series prediction ability offline. Secondly, the trained neural network model is used to compensate the sensor's observations online, combined with the unscented Kalman filter theory, to further correct the filter gain and covariance, thereby reducing the sensor's uncertainty measurement error and improving the target tracking filter Accuracy. The simulation results show that the unscented Kalman filter based on LSTM neural network compensation can predict the sensor measurement value, greatly reduce the target tracking error caused by the sensor uncertainty measurement problem, and meet the actual requirements.