Design Of Mobile Positioning System Based On Strapdown Inertial Navigation In Indoor Environmen

Indoor positioning technology has a wide range of applications and important significance in many fields such as service operation in buildings,location of rescue workers and mine hole detection.Since the wireless communication positioning technology depends on external devices in the environment,and the cost of indoor positioning by sensors such as laser radar is relatively high,the low-cost and autonomous strapdown inertial navigation indoor positioning technology is a research hotspot at present.In order to improve the positioning accuracy of mobile robots in indoor environments,this paper analyzes the main errors of inertial navigation components,proposes a wavelet denoising method based on an improved threshold function,studies the problem of attitude angle resolution,proposes an adaptive Kalman filtering method based on improved weighting,and designs a mobile positioning system based on inertial navigation.The specific research content is as follows:In order to improve the sensor data accuracy of inertial navigation system,the main error sources of the data measured by inertial sensors and the shortcomings of the traditional wavelet threshold denoising method were analyzed.A wavelet denoising method with improved threshold function was proposed,and the optimal combination of wavelet basis function and decomposition level was selected according to the similar signal-to-noise ratio to denoise the gyroscope data and reduce the errors in the collected data.Complete the preprocessing of the original data of inertial sensor.Aiming at the problem of low attitude solving accuracy of strapdown inertial navigation system,the principle of Sage-Husa adaptive filtering is analyzed,an improved weighted measurement noise covariance matrix is designed,and an extended Kalman filtering method with improved adaptive filtering process is proposed.In this method,the measurement noise covariance is adjusted by setting different weight values,and the measurement noise is self-adaptive filtering,so as to realize the high-precision solution of attitude Angle and lay a foundation for the position solution.The hardware system is designed to complete the acquisition and transmission of inertial sensor data.The method proposed in this paper is realized based on the upper computer and the pose calculation experiment of the inertial navigation system is completed,and The experimental results are analyzed and compared.Experimental results show that compared with the traditional adaptive extended filtering algorithm,the mean absolute error and root mean square error of course Angle calculated by the designed system are reduced by 78.28%and 84.23%,respectively,and the mean relative error of position is 4.81%.Therefore,the inertial navigation system designed in this paper can realize the autonomous positioning of the moving experiment car in the indoor environment,and has the accuracy and effectiveness.