Multisensor Fusion Navigation Integrity Monitoring for Terrain Awareness Prediction Warning Algorithm

The airborne sensor data provide the navigation information required for warning computation to the terrain awareness warning algorithm through navigation fusion. Therefore, when the sensor data are abnormal, it will affect the reliability of the terrain awareness warning. To improve the warning reliability and prevent the controlled flight into terrain (CFIT) accident, a multisensor fusion navigation integrity monitoring for terrain awareness prediction warning algorithm was proposed. First, the algorithm constructs a multisensor fusion navigation integrity monitoring method to detect abnormal sensor information, provides reliable navigation information for the warning algorithm according to the abnormal identification results, and ensures the reliability of terrain awareness. Second, when the sensor data are integrity, the algorithm uses the backpropagation (BP) neural network to predict the navigation information in short time to achieve the terrain awareness prediction warning, increasing the reaction time. The algorithm is tested by using a real terrain elevation map and simulated flight trajectory. The test results show that the multisensor fusion navigation integrity monitoring can effectively detect sensor anomalies within 6 s and provide reliable navigation information for warning calculation, which ensures the reliability of the terrain awareness warning. The adopted prediction warning method can increase the reaction time by about 5 s, and the accuracy of the prediction warning is above 90%, improving the pilot's ability to actively avoid threatening terrain.