Research on motion track error detection and compensation algorithm based on MEMS sensor

Due to various errors in actual use, MEMS inertial sensors have large errors in the detection of motion trajectories. Therefore, it is necessary to analyse and model the errors to decrease the impact of error sources on the detection system. This errors mainly include systematic random errors and accumulated errors generated during double integral operation, and different filtering methods are used for different types of errors. For random errors, the wavelet fuzzy threshold method is used to filter the sensor output signal. For the accumulated error, the zero-state adaptive compensation algorithm is used to correct the acceleration and integral velocity. Experiments show that the wavelet threshold denoising algorithm combined with the zero-state adaptive compensation algorithm can enhance the preciseness of the MEMS inertial sensor in object trajectory detection.