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PEDESTRIAN DEAD-RECKONING SYSTEM USING KALMAN FILTER AND WALKING STATE ESTIMATION ALGORITHM AND METHOD FOR HEIGHT ESTIMATION THEREOF
PEDESTRIAN DEAD-RECKONING SYSTEM USING KALMAN FILTER AND WALKING STATE ESTIMATION ALGORITHM AND METHOD FOR HEIGHT ESTIMATION THEREOF
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机译：卡尔曼滤波和步行状态估计算法的行人死亡重估系统及其高度估计方法
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摘要
PURPOSE: A pedestrian inertial navigation system using a Kalman filter and a walking state predicting algorithm and a method for predicting the height of the pedestrian inertial navigation system are provided to detect a walking state and the pace of a pedestrian simultaneously with predicting a posture and a direction angle of the pedestrian by mounting a low-price type micro-electromechanical sensor module on a waist of the pedestrian so that a position of the pedestrian on a flatland is predicted. CONSTITUTION: A pedestrian inertial navigation system using a Kalman filter and a walking state predicting algorithm comprises an inertial sensor module(110), a step detection and step length estimation module(120), a posture and direction angle estimation module(130), and a height estimation module(140). The inertial sensor module includes a three-axis acceleration sensor, a three-axis gyro sensor, and an atmospheric pressure sensor. The pace detection and a step predicting module detects a walking pace of the pedestrian by using signals of the three-axis acceleration sensor and predicts the step of the pedestrian. The posture and direction angle estimation module predicts a posture of the pedestrian by mixing the signals of the three-axis acceleration sensor and three-axis gyro sensor and a direction angle of the pedestrian by measuring a yaw angle of the three-axis gyro sensor. The height estimation module calculate the height of the pedestrian inertial navigation system by using the Kalman filter based on a altitude value of the atmospheric pressure sensor and a z-axis speed value of the three-axis acceleration sensor and predicting the final height of the system by applying a zero-velocity speed correction value of the three-axis acceleration sensor to the walking state predicting algorithm. [Reference numerals] (111) Three-axis acceleration sensor; (112) Three-axis gyro sensor; (113) Atmospheric pressure sensor; (120) Step detection and step length estimation module; (130) Posture and direction angle estimation module; (140) Height estimation module;
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