scholarly journals An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yushuai Zhang ◽  
Jianxin Guo ◽  
Feng Wang ◽  
Rui Zhu ◽  
Liping Wang
Keyword(s):  
Indoor Localization ◽  
Inertial Navigation ◽  
Detection Algorithm ◽  
Position Estimation ◽  
Position Error ◽  
Indoor Navigation ◽  
Bayesian Filter ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Average Position

The specific objective of this study is to propose a low-cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT-aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT-aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real-world scenarios.

scholarly journals Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4782 ◽  
Author(s):  
Yong Hun Kim ◽  
Min Jun Choi ◽  
Eung Ju Kim ◽  
Jin Woo Song
Keyword(s):  
Magnetic Field ◽  
Importance Sampling ◽  
Dead Reckoning ◽  
Position Error ◽  
Magnetic Sensors ◽  
Map Matching ◽  
Multiple Sensors ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Pedestrian Dead Reckoning

This research proposes an algorithm that improves the position accuracy of indoor pedestrian dead reckoning, by compensating the position error with a magnetic field map-matching technique, using multiple magnetic sensors and an outlier mitigation technique based on roughness weighting factors. Since pedestrian dead reckoning using a zero velocity update (ZUPT) does not use position measurements but zero velocity measurements in a stance phase, the position error cannot be compensated, which results in the divergence of the position error. Therefore, more accurate pedestrian dead reckoning is achievable when the position measurements are used for position error compensation. Unfortunately, the position information cannot be easily obtained for indoor navigation, unlike in outdoor navigation cases. In this paper, we propose a method to determine the position based on the magnetic field map matching by using the importance sampling method and multiple magnetic sensors. The proposed method does not simply integrate multiple sensors but uses the normalization and roughness weighting method for outlier mitigation. To implement the indoor pedestrian navigation algorithm more accurately than in existing indoor pedestrian navigation, a 15th-order error model and an importance-sampling extended Kalman filter was utilized to correct the error of the map-matching-aided pedestrian dead reckoning (MAPDR). To verify the performance of the proposed indoor MAPDR algorithm, many experiments were conducted and compared with conventional pedestrian dead reckoning. The experimental results show that the proposed magnetic field MAPDR algorithm provides clear performance improvement in all indoor 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

An Improved Bayesian Zero-Velocity Detection Algorithm for Pedestrian Navigation Based on MIMU

2021 ◽  
Author(s):  
XiaoYu Zhang ◽  
Shaowu Dai ◽  
Hongde Dai ◽  
WenJie Quau ◽  
Yang Zhao
Keyword(s):  
Detection Algorithm ◽  
Pedestrian Navigation ◽  
Zero Velocity

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.

scholarly journals Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4410 ◽  
Author(s):  
Faisal Jamil ◽  
Naeem Iqbal ◽  
Shabir Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Prediction Algorithm ◽  
Measurement Unit ◽  
Sensor Reading ◽  
Learning Module ◽  
Estimation System

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

scholarly journals Indoor Navigation Using Particle Filter and Sensor Fusion

2012 ◽  
Vol 19 (2) ◽  
pp. 31-40
Author(s):  
Lukas Köping ◽  
Thomas Mühsam ◽  
Christian Ofenberg ◽  
Bernhard Czech ◽  
Michael Bernard ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Particle Filter ◽  
Indoor Localization ◽  
High Accuracy ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Sensor Data ◽  
Localization System ◽  
Multiple Sensor ◽  
Large Building

Abstract In this paper we present an indoor localization system based on particle filter and multiple sensor data like acceleration, angular velocity and compass data. With this approach we tackle the problem of documentation on large building yards during the construction phase. Due to the circumstances of such an environment we cannot rely on any data from GPS, Wi-Fi or RFID. Moreover this work should serve us as a first step towards an all-in-one navigation system for mobile devices. Our experimental results show that we can achieve high accuracy in position estimation.

Scenario-Dependent ZUPT-Aided Pedestrian Inertial Navigation with Sensor Fusion

2021 ◽  
Vol 29 (1) ◽  
pp. 3-31
Author(s):  
Y. Wang ◽  
◽  
Ch.-Sh. Jao ◽  
A.M. Shkel ◽  
◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Indoor Navigation ◽  
Sensor Calibration ◽  
Phase Detection ◽  
Pedestrian Navigation ◽  
Navigation Error ◽  
Navigation Accuracy ◽  
External Signals

Pedestrian navigation has been of high interest in many fields, such as human health monitoring, personal indoor navigation, and localization systems for first responders. Due to the potentially complicated navigation environment, selfcontained types of navigation such as inertial navigation, which do not depend on external signals, are more desirable. Pure inertial navigation, however, suffers from sensor noise and drifts and therefore is not suitable for long-term pedestrian navigation by itself. Zero-velocity update (ZUPT) aiding technique has been developed to limit the navigation error growth, but adaptivity of algorithms, model fidelity, and system robustness have been major a concern if not properly addressed. In this paper, we attempt to establish a common approach to solve the problem of self-contained pedestrian navigation by identifying the critical parts of the algorithm that have a strong influence on the overall performance. We first review approaches to improve the navigation accuracy in each of the critical part of implementation proposed by other groups. Then, we report our results on analytical estimations and experiments illustrating effects of combining inertial sensor calibration, stance phase detection, adaptive model selection, and sensor fusion.

A Zero-Velocity Detection Algorithm for Pedestrian Navigation Based on LSTM

2021 ◽  
Author(s):  
Langping An ◽  
Xianfei Pan ◽  
Mang Wang ◽  
Ze Chen ◽  
Zheming Tu ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Pedestrian Navigation ◽  
Zero Velocity
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