scholarly journals Deep neural network‐based adaptive zero‐velocity detection for pedestrian navigation system

2021 ◽  
Author(s):  
Liqiang Zhang ◽  
Boxuan Chen ◽  
Hu Li ◽  
Yu Liu
Keyword(s):  
Neural Network ◽  
Navigation System ◽  
Deep Neural Network ◽  
Pedestrian Navigation ◽  
Zero Velocity

Smooth estimation of human foot motion for zero-velocity-update-aided inertial pedestrian navigation system

Sensor Review ◽  
2015 ◽  
Vol 35 (4) ◽  
pp. 389-400 ◽  
Author(s):  
Hongyu Zhao ◽  
Zhelong Wang ◽  
Qin Gao ◽  
Mohammad Mehedi Hassan ◽  
Abdulhameed Alelaiwi
Keyword(s):  
Navigation System ◽  
Gait Cycle ◽  
Inertial Sensor ◽  
Computational Cost ◽  
Content Type ◽  
Human Foot ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Foot Motion ◽  
Smooth Transitions

Purpose – The purpose of this paper is to develop an online smoothing zero-velocity-update (ZUPT) method that helps achieve smooth estimation of human foot motion for the ZUPT-aided inertial pedestrian navigation system. Design/methodology/approach – The smoothing ZUPT is based on a Rauch–Tung–Striebel (RTS) smoother, using a six-state Kalman filter (KF) as the forward filter. The KF acts as an indirect filter, which allows the sensor measurement error and position error to be excluded from the error state vector, so as to reduce the modeling error and computational cost. A threshold-based strategy is exploited to verify the detected ZUPT periods, with the threshold parameter determined by a clustering algorithm. A quantitative index is proposed to give a smoothness estimate of the position data. Findings – Experimental results show that the proposed method can improve the smoothness, robustness, efficiency and accuracy of pedestrian navigation. Research limitations/implications – Because of the chosen smoothing algorithm, a delay no longer than one gait cycle is introduced. Therefore, the proposed method is suitable for applications with soft real-time constraints. Practical implications – The paper includes implications for the smooth estimation of most types of pedal locomotion that are achieved by legged motion, by using a sole foot-mounted commercial-grade inertial sensor. Originality/value – This paper helps realize smooth transitions between swing and stance phases, helps enable continuous correction of navigation errors during the whole gait cycle, helps achieve robust detection of gait phases and, more importantly, requires lower computational cost.

Pedestrian navigation system using MEMS sensors for heading drift and altitude error correction

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 270-281 ◽  
Author(s):  
Xiaochun Tian ◽  
Jiabin Chen ◽  
Yongqiang Han ◽  
Jianyu Shang ◽  
Nan Li
Keyword(s):  
Navigation System ◽  
Low Cost ◽  
Three Dimensional ◽  
Step Height ◽  
Reference Trajectory ◽  
Correction Algorithm ◽  
Content Type ◽  
Pedestrian Navigation ◽  
Smoothing Filter ◽  
Zero Velocity

Purpose This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space. Design/methodology/approach A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter. Findings The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent. Originality/value Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.

scholarly journals A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors

Sensors ◽  
2016 ◽  
Vol 16 (10) ◽  
pp. 1578 ◽  
Author(s):  
Xiaochun Tian ◽  
Jiabin Chen ◽  
Yongqiang Han ◽  
Jianyu Shang ◽  
Nan Li
Keyword(s):  
Navigation System ◽  
Inertial Sensors ◽  
Detection Algorithm ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Velocity Interval

MEMS Based Pedestrian Navigation System

2005 ◽  
Vol 59 (1) ◽  
pp. 135-153 ◽  
Author(s):  
Seong Yun Cho ◽  
Chan Gook Park
Keyword(s):  
Neural Network ◽  
Navigation System ◽  
Stance Phase ◽  
Finite Impulse Response ◽  
Step Length ◽  
Magnetic Compass ◽  
Pedestrian Navigation ◽  
Novel Technique ◽  
Seamless Positioning ◽  
Impulse Response Filter

In this paper we present a micro-electrical mechanical system (MEMS) based pedestrian navigation system (PNS) for seamless positioning. The sub-algorithms for the PNS are developed and the positioning performance is enhanced using the modified receding horizon Kalman finite impulse response filter (MRHKF). The PNS consists of a biaxial accelerometer and a biaxial magnetic compass mounted on a shoe. The PNS detects a step using a novel technique during the stance phase and simultaneously calculates walking information. Step length is estimated using a neural network whose inputs are the walking information. The azimuth is calculated using the magnetic compass, the walking information and the tilt compensation algorithm. Using the proposed sub-algorithms, seamless positioning can be accomplished. However, the magnetic compass based azimuth may have an error that varies according to the surrounding magnetic field. In this paper, the varying error is compensated using the MRHKF filter. Finally, the performance enhanced seamless positioning is achieved, and the performance is verified by experiment.

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

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3261 ◽  
Author(s):  
Ming Ma ◽  
Qian Song ◽  
Yang Gu ◽  
Yanghuan Li ◽  
Zhimin Zhou
Keyword(s):  
Sensor Fusion ◽  
Navigation System ◽  
Detection Algorithm ◽  
Stair Climbing ◽  
Detection Methods ◽  
Recognition Method ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Motion Modes ◽  
Detection Approach

The zero velocity update (ZUPT) algorithm is an effective way to suppress the error growth for a foot-mounted pedestrian navigation system. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. Existing zero velocity detection methods cannot provide good performance at high gait speeds or stair climbing. An adaptive zero velocity detection approach based on multi-sensor fusion is proposed in this paper. The measurements of an accelerometer, gyroscope and pressure sensor were employed to construct a zero-velocity detector. Then, the adaptive threshold was proposed to improve the accuracy of the detector under various motion modes. In addition, to eliminate the height drift, a stairs recognition method was developed to distinguish staircase movement from level walking. Detection performance was examined with experimental data collected at varying motion modes in real scenarios. The experimental results indicate that the proposed method can correctly detect zero velocity intervals under various motion modes.

Sign in / Sign up

Export Citation Format

Share Document