Research On The Key Technology Of Rocket Booster Parachute Parachute Navigation System

With the increasing demand for near-Earth defense and deep space exploration,many countries have increased their investment in scientific research on aerospace field.As a crucial means to launch payloads into space,how to recover and reuse the booster in a safe and controlled manner is a hot issue in scientific research.Parachute recovery is a kind of recovery means with low cost and technical difficulty as well as less impact on rocket carrying capacity,which is an essential research direction for China’s rocket recovery technology.In the parachute recovery process,real-time accurate acquisition of booster navigation parameters and status information is an important prerequisite for achieving landing point control.Aiming to improve the positioning accuracy and robustness of the navigation system for the special working environment of rockets,the paper conducts the following research on the key technologies of the parachute navigation system at the hardware and algorithm aspects,respectively.(1)Analysis of the process of parachute recovery and the overall mission requirements for the system are presented.A high-precision integrated navigation computing platform based on FPGA and DSP architecture was developed,and the basic principles and hardware design of navigation and positioning module,acquisition and storage module,communication module are introduced.In Chapter 5,a vehicle test is designed to evaluate the function and positioning accuracy of the navigation system,the results show that the performance satisfies the requirements of the booster parachute navigation system.(2)Analysed the error model of MEMS inertial devices,constructed the inertial navigation solution algorithm based on the two-sample equivalent rotation vector and quaternion,introduced the error model for strapdown navigation algorithm.A barometer fusion algorithm based on two-stage complementary filtering was constructed to complement the altitude information.A dual-mode navigation algorithm using BDS/GPS is applied to improve the positioning accuracy and stability of the satellite navigation system.Finally,the Kalman filterbased SINS/GNSS loose coupling integrated navigation model is given.(3)In order to solve the problem of degraded filter estimation accuracy or even result divergence due to the noise parameter mismatch,an improved variable structure multi-model adaptive algorithm was proposed in this paper.The algorithm uses extreme learning mechanism to build a model set adapter to obtain the optimal set of noise parameters based on the temporal and frequency characteristic parameters of the sensor and state of the booster,the interactive output of state is obtained by state estimator.The algorithm alleviates the problems of excessive competition and limited model coverage of existing multi-model algorithms,improves the accuracy and real-time of model identification,greatly exploit the advantages of multi-model structure to improve the positioning accuracy of integrated navigation system.