A zero velocity intervals detection algorithm based on sensor fusion for indoor navigation systems

Abstract

The Zero-velocity Update (ZUPT)-aided Extended Kalman Filter(EKF) algorithm is commonly deployed to resolve trajectories of pedestrians. To use the ZUPT, it is necessary to detect zero velocity intervals reliably. Existing zero velocity intervals detection algorithms cannot provides good performance at high gait speeds or stair climbing. In this paper, we propose a novel zero velocity intervals detection approach based on sensors fusion. In this method, the measurements of accelerometer, gyroscope and pressure sensor are combined together to detect the zero velocity intervals. A new detector is derived based on the general likelihood ratio test (LRT) framework. The test results show that the new detector performs much better than the angular rate energy (ARE) detector.

Random Forest Algorithm with a Half-Voting and Weighted Decision Trees for Interior Pedestrian Tracking

“…Park S.K et al [5] A zero-velocity interval detection method based on Markov model is designed, which needs to be judged by the output of the y-axis gyroscope. Ma M et al [6], the threshold is updated using the amplitude peak of the y-axis gyroscope. The above three methods have achieved good results in walking and running.…”

An Adaptive Zero Velocity Detection Algorithm Based on Multi-Sensor Fusion for a Pedestrian Navigation System

“…In this method, an additional accelerometer was attached to the chest and the difference of maximum acceleration change extracted from the chest acceleration was adopted to update the corresponding threshold for zero velocity detection. In Reference [ 19 ], the magnitude peak of y -axis gyroscope was used to update the threshold based on the pre-defined threshold function. In Reference [ 20 ], an algorithm based on a Markov model was developed which only uses the segmentation of the y -axis gyroscope outputs instead of the three-axis output.…”

“…In Reference [ 20 ], an algorithm based on a Markov model was developed which only uses the segmentation of the y -axis gyroscope outputs instead of the three-axis output. The method used in References [ 18 , 19 , 20 ] showed good performance under walking and running modes. However, none of these methods explored zero velocity detection when a person ascends or descends stairs.…”