Research On Pedestrian Navigation Technology Based On Mems Inertial Measurement

With the satellite positioning technology and the rapid development of wireless Internet technology,positioning and navigation have been integrated into all aspects of daily life.As people on the indoor location of the growing demand,such as in shopping malls,exhibitions and other indoor places often need to know the location of personnel information.Indoor wireless location technology requires the deployment of a large number of indoor signal transmission equipment,it is expensive and positioning accuracy to be improved.Inertial navigation technology only rely on its own sensors to achieve autonomous navigation for pedestrian navigation provides technical reference.At present,the corresponding products have been applied to specific areas,such as the location of firefighters in the indoor fire scene and mine workers positioning,but the inertial sensor of the positioning device is of high precision and high cost,it is not suitable for use in consumer electronics.Therefore,this paper is based on low-cost inertial sensors to study the pedestrian navigation technology.Firstly,the strapdown inertial navigation theory is studied and the update algorithm suitable for pedestrian navigation is discussed.The navigation attitude matrix calculation is the key algorithm in strapdown inertial navigation system.The transformation model between the carrier coordinate system and the navigation coordinate system is constructed.Quaternion algorithm is used to establish the differential equation of attitude updating.The equation is solved by Runge-Kutta method.Then the measured acceleration is transformed into the navigation coordinate system through the attitude matrix,and the navigation parameters such as velocity and position are obtained by the integral algorithm.Low-cost three-axis vector field sensor there is a more serious error,the method of calibration and compensation for sensor error needs to be discussed and researched.Three-axis vector field sensor due to differences in the manufacturing process there are zero bias,scale factor and non-orthogonal angle error.In this paper,the mathematical model of sensor error is established,and the method of least squares ellipsoid fitting is used to calculate the error parameter.The experimental results show that the proposed method can effectively estimate the main error parameters of the sensor and improve the accuracy of the navigation system after error compensation.As the random drift error of the sensor is difficult to calibrate before use,a filter is required to reduce the accumulation of errors.The complementary filter has the advantages of simple structure,small computation and fast convergence with gradient descent method.The Kalman filter can effectively estimate the random drift of the gyroscope by establishing the equation of state and the measurement equation.In this paper,it is proposed that the corresponding measurement equations can be established according to the trusted time of observations,which can effectively improve the estimation accuracy.After the data fusion of the inertial sensor,there is still a serious d rift of the speed,and the drift of the velocity is corrected by the method of zero speed judgment and correction according to the gait feature of pedestrian walking.Finally,a comparative experiment is conducted to analyze the accuracy of the two algorithms,which are applied to pedestrian navigation.The first test is carried out on a rotating platform.The turntable can provide accurate rotation angle information.The attitude information output by the navigation system is compared with the given angle of the turntable,the validity and precision of sensor data fusion are analyzed.The second test examines the positioning trajectory of the pedestrian navigation system in an indoor environment and analyzes the errors.Two test results show that the sensor data fusion algorithm designed in this paper can meet the needs of pedestrian navigation.