Bionic robotic fish attitude detection based on the limiting filtering-extended Kalman filtering algorithm

Bionic robotic fish is one of the important equipment for marine resources exploration in recent years, as the key link of position control for bionic robotic fish, attitude detection is the basis for bionic robotic fish to complete the complex exploration tasks. In order to solve the problem of robotic fish attitude detection, it needs to implement the data fusion based on the obtained original data from accelerometer, magnetometer, gyroscope. However, it’s a nonlinear system for robotic fish attitude detection in practice, and it may occur abnormal data deviation caused by the system external interference or internal disturbance from the sensors, to solve this problem, in this paper, it proposes the limiting filtering-extended Kalman filtering (LF-EKF) algorithm, which combines the limiting filtering (LF) and extended Kalman filtering (EKF) algorithms to realize the data fusion and complete the attitude detection. The simulation results show that the proposed algorithm can obtain the results with good performance.