Autonomous Navigation and Positioning Model of Aircraft Based on Multi-source Opportunity Signals

The multi-source opportunity signal positioning model is an autonomous positioning and navigation technology based on multiple opportunity signals. By integrating mathematical derivation, Kalman filtering, RANSAC algorithm and statistical methods, this paper successfully established an accurate and robust real-time positioning and navigation model. First, for different types of opportunity signals (TOA, TDOA, DFD, AOA, RSSI), it is analyzed that at least three signals are needed to uniquely determine the position of the aircraft in three-dimensional space. Then, the Kalman filter algorithm is used to establish the real-time positioning model of the aircraft under ideal conditions without data deviation. Further, considering the data deviation, the RANSAC algorithm is used to identify and eliminate abnormal signals, optimize data input, and make the positioning result more accurate. Finally, the random deviation and drift of the opportunity signal are analyzed by combining statistical methods. The filtered data is processed again by Kalman filtering to obtain the accurate navigation trajectory of the aircraft in a complex environment. The robustness and adaptability of the model are excellent in noisy environments, which significantly improves the reliability and accuracy of the aircraft in real-time positioning and navigation.