Filtering optimization method and system for six-push drone accelerator measurement value

Abstract

The invention relates to the technical field of a six-push drone and particularly relates to a filtering optimization method and system for a six-push drone accelerator measurement value. The method is characterized in that firstly, an acceleration measurement value at the time K is filtered by a Kalman filter; secondly, the acceleration measurement value and an acceleration prediction value at the time K are obtained; the covariance at the time K is obtained; thirdly, the Kalman gain at the time K is calculated; fourthly, an optimal prediction value at the time is obtained; iterative calculation is performed based on the optimal prediction value at the time K to obtain an optimal prediction value at the time K+1. The method is advantaged in that real-time filtering optimization of the accelerator measurement value is performed to obtain measurement data closer to the real acceleration; the optimized acceleration data is subjected to secondary integration processing, more accurate underwater drone displacement data can be obtained, the non-true extreme value occurrence probability and the error of acceleration measurement are greatly reduced, and underwater drone positioning accuracy is improved through a nine-axis motion attitude module.

Description

technical field [0001] The invention relates to the technical field of six-push UAVs, in particular to a filter optimization method for measuring values ​​of accelerators of six-push UAVs. Background technique [0002] At present, Liutui underwater drone uses MPU605 six-axis motion attitude sensor and processing components, combined with IST Aisen magnetometer, to achieve a complete nine-axis motion attitude fusion output. [0003] Underwater drones are currently unable to achieve underwater positioning through GPS electromagnetic signals. Currently, the most commonly used underwater positioning methods are underwater acoustic echo technology positioning or inertial navigators. Underwater drones plan to use a nine-axis motion attitude module to simulate The inertial navigator converts the integral of the accelerometer (accelerator) measurement value into each axial displacement of the underwater drone to obtain the underwater position. However, the accelerometer measurement...

AI Technical Summary

Problems solved by technology

However, the accelerometer measurement data in the nine-axis motion attitude module of the underwater UAV is inaccurate, because it is only a simple application for rough positioning, and there are many errors (measurement noise) and non-real extremum jumps. Will cause jumps and inaccuracies in the underwater drone displacement calculated by the integral

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