Comparison of Three Navigation Algorithms Based on Strapdown Inertial Navigation System

With the rapid development of aviation technology and intelligent technology, various new unmanned aerial vehicles (UAV) and underwater unmanned aerial vehicles (UUV) have been launched one after another, showing a broad application prospect. Cross-Media Vehicle (CMV) has gradually become an important research hotspot for researchers all over the world because of its combination of UAV 's air maneuverability and UUV's underwater operation ability. Accurately obtaining the current navigation parameters such as attitude, speed and position of CMV is the premise of carrying out various tasks. The air and water environment can still receive GPS and BDS signals, and can be combined with strapdown inertial navigation system (SINS) for integrated navigation. When GPS and BDS signals are out of lock, they can still maintain high navigation accuracy in a short time. In the long-term underwater working environment, GPS and BDS signals cannot be transmitted, and other navigation methods are needed. Based on the strapdown inertial navigation system, this paper uses the measured data of the sports car to study and compare the pure inertial navigation, GPS / SINS integrated navigation, and BP neural network assisted GPS / SINS integrated navigation. The results show that: 1) pure inertial navigation has high accuracy in a short time, and its error will accumulate over time; 2) GPS / SINS integrated navigation has high precision and can be used in air and water environment. 3) BP neural network assisted GPS / SINS integrated navigation is suitable for underwater long-term navigation and positioning, and its accuracy is much higher than that of pure inertial navigation.