Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters

Song, Ju Yeon; Park, Chan Gook

doi:10.3390/s18041281

Cited by 25 publications

(19 citation statements)

References 46 publications

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“…The second is the improvement of pedestrian navigation positioning accuracy. The research of many scholars has shown that [25]- [29] the ZUPT algorithm can greatly improve the error divergence of MIMU inertial devices and improve the positioning accuracy and stability of the system by utilizing the dynamic and static characteristics of the foot during walking.…”

Section: Problem Analysismentioning

confidence: 99%

A Novel Zero Velocity Detection Method Based on the Plantar Pressure

Wang¹,

Guo²,

Wu³

et al. 2020

Preprint

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The zero velocity update(ZUPT) algorithm is the core of a foot-mounted pedestrian navigation system. The zero velocity detection method is the premise and guarantee of the effective application of the ZUPT algorithm. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. The detection accuracy of the existing zero velocity detection methods is easy to be affected by users and environment. A novel zero velocity detection method based on the plantar pressure is proposed in this paper, which has higher detection accuracy and better environmental adaptability. First, the paper analyzes the motion characteristics of foot during walking. Second, the inherent relationship between the plantar pressure and the gait change during walking is studied based on the pressure sensor. Then, the model of the zero velocity detection method using the plantar pressure is established. Finally, the indoor and outdoor multi-scene experiments show that this method not only has a high detection accuracy, but also has good adaptability to users and walking environment.

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Section: Problem Analysismentioning

confidence: 99%

A Novel Zero Velocity Detection Method Based on the Plantar Pressure

Wang¹,

Guo²,

Wu³

et al. 2020

Preprint

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“…Many scholars have used inertial sensors for indoor positioning for many years [16,17]. By integrating the acceleration and angular velocity of the inertial sensor, the position of a pedestrian is obtained.…”

Section: Related Workmentioning

confidence: 99%

An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter

Chen

Peng

et al. 2018

IJGI

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Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.

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“…However, this method is complex and its precision is limited by the numbers of sensors. The study in [19] proposed that installing IMUs on the upper body can avoid interference from cables laid underground and other surface magnetic interference sources. However, magnetic anomalies originating from sources like steel structures cannot be properly handled by this method.…”

Section: Introductionmentioning

confidence: 99%

Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter

Zhang²,

Wan

et al. 2020

Sensors

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In pedestrian inertial navigation, multi-sensor fusion is often used to obtain accurate heading estimates. As a widely distributed signal source, the geomagnetic field is convenient to provide sufficiently accurate heading angles. Unfortunately, there is a broad presence of artificial magnetic perturbations in indoor environments, leading to difficulties in geomagnetic correction. In this paper, by analyzing the spatial distribution model of the magnetic interference field on the geomagnetic field, two quantitative features have been found to be crucial in distinguishing normal magnetic data from anomalies. By leveraging these two features and the classification and regression tree (CART) algorithm, we trained a decision tree that is capable of extracting magnetic data from distorted measurements. Furthermore, this well-trained decision tree can be used as a reject gate in a Kalman filter. By combining the decision tree and Kalman filter, a high-precision indoor pedestrian navigation system based on a magnetically assisted inertial system is proposed. This system is then validated in a real indoor environment, and the results show that our system delivers state-of-the-art positioning performance. Compared to other baseline algorithms, an improvement of over 70% in the positioning accuracy is achieved.

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Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters

Cited by 25 publications

References 46 publications

A Novel Zero Velocity Detection Method Based on the Plantar Pressure

A Novel Zero Velocity Detection Method Based on the Plantar Pressure

An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter

Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter

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