Heading constraint algorithm for foot-mounted PNS using low-cost IMU

doi:10.23919/JSEE.2022.000067

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (3): 727-736.doi: 10.23919/JSEE.2022.000067

• CONTROL THEORY AND APPLICATION • Previous Articles Next Articles

Heading constraint algorithm for foot-mounted PNS using low-cost IMU

Jing GUI, Heming ZHAO*(), Xiang XU()

¹ School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China

Received:2021-04-20 Online:2022-06-18 Published:2022-06-24
Contact: Heming ZHAO E-mail:hemzhao@163.com;hsianghsu@163.com
About author:|GUI Jing was born in 1997. He received his B.S. degree from Soochow University, Suzhou, China, in 2019. He is currently pursuing his M.S. degree in the School of Electronic and Information Engineering, Soochow University. His current research interests include low-cost inertial navigation systems and pedestrian navigation.E-mail: jgui716@163.com||ZHAO Heming was born in 1957. He received his M.S. degree from Soochow University, Suzhou, China, in 1982. He is a doctoral tutor and a dean in the School of Electronic and Information Engineering, Soochow University. He is a member of IEEE and a senior member of Chinese Electronic Institute, and he is a member of the editorial boards of signal processing. His current research interests include speech signal processing and intelligent computing. E-mail: hemzhao@163.com||XU Xiang was born in 1988. He received his M.S. degree from Harbin Engineering University, Harbin, China, in 2014, and his Ph.D. degree from Southeast University, Nanjing, China, in 2018. He is currently a lecturer with the School of Electronic and Information Engineering, Soochow University. His current research interests include initial alignment for inertial navigation, integrated navigation for autonomous underwater vehicle, attitude estimation for low-cost inertial measurement unit and information fusion. E-mail: hsianghsu@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61803278).

Abstract

Abstract:

Foot-mounted pedestrian navigation system (PNS) is a common solution to pedestrian navigation using micro-electro mechanical system (MEMS) inertial sensors. The inherent problems of inertial navigation system (INS) by the traditional algorithm, such as the accumulated errors and the lack of observation of heading and altitude information, have become obstacles to the application and development of the PNS. In this paper, we introduce a heuristic heading constraint method. First of all, according to the movement characteristics of human gait, we use the generalized likelihood ratio test (GLRT) detector and introduce a time threshold to classify the human gait, so that we can effectively identify the stationary state of the foot. In addition, based on zero velocity update (ZUPT) and zero angular rate update (ZARU), the cumulative error of the inertial measurement unit (IMU) is limited and corrected, and then a heuristic heading estimation is used to constrain and correct the heading of the pedestrian. After simulation and experiments with low-cost IMU, the method is proved to reduce the localization error of end-point to less than 1% of the total distance, and it has great value in application.

Key words: pedestrian navigation system (PNS), zero velocity update (ZUPT), gait detection, heading constraint

Jing GUI, Heming ZHAO, Xiang XU. Heading constraint algorithm for foot-mounted PNS using low-cost IMU[J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 727-736.

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References 22

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Symbol	Parameter	Value
${l_s}$	Step length/m	1.2
${l_h}$	Max height/m	0.15
${t_{{\rm{sw}}} }$	Swing phase cycle/s	1.0
${t_{{\rm{st}}} }$	Stance phase cycle/s	0.5
${\theta _{{\rm{max}}} }$	Max pitch/rad	0.6
${t_c}$	Turning cycle/s	0.5
${{\boldsymbol{b}}_g}$	Gyroscope bias/(°/s)	$\left[ {\begin{array}{*{20}{c}} {1.5}&2&{1.2} \end{array}} \right]$
${{\boldsymbol{b}}_a}$	Accelerometer bias/(m/s²)	$\left[ {\begin{array}{*{20}{c}} {0.1}&{0.2}&{0.15} \end{array}} \right]$
${{\boldsymbol{n}}_g}$	Gyroscope noise/ $({ {{\rm{rad}}} \mathord{\left/ {\vphantom { {rad} {\sqrt s } } } \right. } {\sqrt {\rm{s}} } } )$	$\left[ {\begin{array}{*{20}{c}} {0.1}&{0.1}&{0.1} \end{array}} \right] $
${{\boldsymbol{n}}_a}$	Accelerometer noise/ $( { {{\rm{m} } } \mathord{\left/ {\vphantom { {m} { { { {s^2} } \mathord{\left/ {\vphantom { { {s^2} } {\sqrt {Hz} } } } \right. } {\sqrt {Hz} } } } } } \right. } { { { {{\rm{s}}^2} } \mathord{\left/ {\vphantom { { {s^2} } {\sqrt {Hz} } } } \right. } {\sqrt {{\rm{Hz}}} } } } })$	$\left[ {\begin{array}{*{20}{c}} {0.1}&{0.1}&{0.1} \end{array}} \right] $
${\psi _0}$	Initial yaw/(°)	0
$f$	Sample frequency/Hz	100

Parameter	Gyroscope	Accelerometer
Maximum range	$\pm 2\,000\;{^\circ \mathord{\left/ {\vphantom {^\circ {\rm{s} } } } \right. } {\rm{s} } }$	$ \pm 16g$
Sensitivity	$16.4\;{ { {\rm{LSB} } } \mathord{\left/ {\vphantom { { {\rm{LSB} } } {\left( { {^\circ \mathord{\left/ {\vphantom {^\circ {\rm{s} } } } \right. } {\rm{s} } } } \right)} } } \right. } {\left( { {^\circ \mathord{\left/ {\vphantom {^\circ s} } \right. } {\rm{s} } } } \right)} }$	$2\;048{ { {\rm{LSB} } } \mathord{\left/ {\vphantom { {LSB} g} } \right. } g}$
Non-linearity	0.2 %	0.5 %
Initial bias	$\pm 5\;{^\circ \mathord{\left/ {\vphantom {^\circ s} } \right. } {\rm{s} } }$	$\pm 60\;{{\rm{m}}g }$
Noise density	${ {0.05\;{\rm{rad} } } \mathord{\left/ {\vphantom { {0.05\,{\rm{rad} } } {\sqrt s } } } \right. } {\sqrt {\rm{s} } } }$	$0.4\;{ { {{\rm{m}}g } } \mathord{\left/ {\vphantom { { {\rm{m}}g} {\sqrt {Hz} } } } \right. } {\sqrt { {\rm{Hz} } } } }$

Algorithm	Total distance / m		End-point / m		Positioning error /%
Algorithm	Test1	Test2	Test1	Test2	Test1	Test2
Proposed method	110	590	(0.48,?0.58)	(?0.86,?1.73)	0.68	0.33
ZUPT+ZARU	110	590	(?1.97,?0.12)	(42.65,59.23)	1.79	12.37
ZUPT only	110	590	(?8.10,4.82)	(56.41,58.26)	8.56	13.74

Heading constraint algorithm for foot-mounted PNS using low-cost IMU

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