## 1 Introduction
In reference [22], the wind speed estimation method based on Kalman filtering technology and the information fusion technology of FADS/INS were studied; however, the selected Kalman filter state does not contain atmospheric data. [PERSON] [23] designed a FADS/INS data fusion algorithm based on the Kalman filter and applied it to Mars exploration to estimate atmospheric data. In this method, the surface pressure of the aircraft is taken as the measured value, and the atmospheric density, atmospheric static pressure and wind speed vector are selected as the system state variables to establish the system model. [PERSON] [24] designed an integrated navigation fusion algorithm for an inertial navigation system and an atmospheric data system based on Kalman filter theory, and the effectiveness of the algorithm was verified by simulation, which proves that the integration of an inertial navigation system and an atmospheric data system is feasible. [PERSON] [25] proposed the unscented kalman filter (UKF) algorithm to fuse FADS and INS data to estimate the real-time atmospheric data of the aircraft, the algorithm estimates the parameters such as angle of attack and sideslip angle by calculating the speed and height of the aircraft. The simulation results showed that the algorithm has improved the estimation accuracy and system stability compared with the original estimation methods such as Kalman filter. The above two theories, the fusion algorithm based on complementary filtering and the fusion algorithm based on Kalman filtering, have their own advantages and disadvantages. The structure of the fusion algorithm based on complementary filtering is simple and easy to implement. However, the requirements for filter parameters during maneuvering flight and cruise are contradictory, so the fusion algorithm based on complementary filtering cannot maintain high accuracy during the whole flight process. In addition, when the flight time is long, the accuracy of the fusion algorithm of INS and FADS based on complementary filtering will also decrease due to the significant decline in the accuracy of the inertial navigation system [26]. Furthermore, the fusion algorithm based on the Kalman filter has high accuracy when using a high-order filtering model, but the computational complexity is high and the calculation speed is slow, which is not conducive to real-time estimation. If the low-order model is used, the calculation accuracy decreases rapidly. At present, the INS and FADS fusion algorithm based on complementary filtering is widely used. However, due to the influence of the aircraft elastic deformation and the sensor error, the accumulation of these errors when the flight time is long reduces the accuracy of the flight parameters solved by INS, which leads to the low accuracy of flight parameters solved by the fusion algorithm. Moreover, due to the conflicting parameter requirements of the filter during maneuvering flight and cruise flight, the fusion algorithm based on complementary filtering cannot maintain high accuracy throughout the flight phase. Therefore, a variable gain complementary filtering algorithm [26] based on the inertial network [27] is designed in this paper. By fusing the measurement information of inertial sensors at different positions, the accuracy of flight parameters solved by INS is improved, so as to improve the accuracy of the flight parameters solved by the fusion algorithm. In addition, the filtering parameters are adjusted according to different stages to ensure the accuracy of fusion in different flight stages of the aircraft. The general research idea of this paper is shown in Figure 1. Inertial sensors (accelerometers and gyroscopes) are installed in different parts of the aircraft as inertial nodes, according to the angular velocity and acceleration measured by the network node sensors. The Kalman filter is designed to estimate the elastic deformation of the child nodes, and the elastic deformation compensation algorithm is established to compensate the elastic deformation of the child nodes.