scholarly journals Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Zhian Deng ◽  
Xin Liu ◽  
Zhiyu Qu ◽  
Changbo Hou ◽  
Weijian Si
Keyword(s):  
Inertial Sensors ◽  
Body Motion ◽  
Hand Movements ◽  
Estimation Errors ◽  
Daily Lives ◽  
Filtering Algorithm ◽  
Pedestrian Navigation ◽  
Heading Estimation ◽  
Estimation System ◽  
Previous User

Heading estimation using inertial sensors built-in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA-based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.

Transmission rate conditions for distributed filtering in sensor networks against eavesdropper

2021 ◽  
pp. 014233122110056
Author(s):  
Bingya Zhao ◽  
Ya Zhang
Keyword(s):  
Sensor Networks ◽  
Topological Entropy ◽  
Kalman Filtering ◽  
Transmission Rate ◽  
Necessary Condition ◽  
Estimation Errors ◽  
Positive Role ◽  
Filtering Algorithm ◽  
Wiretap Channels ◽  
Kalman Filtering Algorithm

This paper studies the distributed secure estimation problem of sensor networks (SNs) in the presence of eavesdroppers. In an SN, sensors communicate with each other through digital communication channels, and the eavesdropper overhears the messages transmitted by the sensors over fading wiretap channels. The increasing transmission rate plays a positive role in the detectability of the network while playing a negative role in the secrecy. Two types of SNs under two cooperative filtering algorithms are considered. For networks with collectively observable nodes and the Kalman filtering algorithm, by studying the topological entropy of sensing measurements, a sufficient condition of distributed detectability and secrecy, under which there exists a code–decode strategy such that the sensors’ estimation errors are bounded while the eavesdropper’s error grows unbounded, is given. For collectively observable SNs under the consensus Kalman filtering algorithm, by studying the topological entropy of the sensors’ covariance matrices, a necessary condition of distributed detectability and secrecy is provided. A simulation example is given to illustrate the results.

Improved estimation is a prerequisite for successful terminal guidance

2005 ◽  
Vol 219 (2) ◽  
pp. 145-156 ◽  
Author(s):  
J Shinar ◽  
V Turetsky
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Estimation Errors ◽  
New Approach ◽  
Target Acceleration ◽  
The Future ◽  
New Ideas ◽  
Estimation System ◽  
Terminal Guidance

Successful interception of manoeuvring anti-surface missiles that are expected in the future can be achieved only if the estimation errors against manoeuvring targets can be minimized. The paper raises new ideas for an improved estimation concept by separating the tasks of the estimation system and by explicit use of the time-to-go in the process. The outcome of the new approach is illustrated by results of Monte Carlo simulations in generic interception scenarios. The results indicate that if an eventual ‘jump’ in the commanded target acceleration is detected sufficiently rapidly, small estimation errors and consequently precise guidance can be obtained.

scholarly journals Detection of (In)activity Periods in Human Body Motion Using Inertial Sensors: A Comparative Study

Sensors ◽  
2012 ◽  
Vol 12 (5) ◽  
pp. 5791-5814 ◽  
Author(s):  
Alberto Olivares ◽  
Javier Ramírez ◽  
Juan M. Górriz ◽  
Gonzalo Olivares ◽  
Miguel Damas
Keyword(s):  
Comparative Study ◽  
Human Body ◽  
Inertial Sensors ◽  
Body Motion

A Mobile Motion Capture System Based on Inertial Sensors and Smart Shoes

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Pyeong-Gook Jung ◽  
Sehoon Oh ◽  
Gukchan Lim ◽  
Kyoungchul Kong
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Health Condition ◽  
Body Motion ◽  
Whole Body ◽  
Motion Capture System ◽  
Outdoor Sports ◽  
Motion Capture Systems

Motion capture systems play an important role in health-care and sport-training systems. In particular, there exists a great demand on a mobile motion capture system that enables people to monitor their health condition and to practice sport postures anywhere at any time. The motion capture systems with infrared or vision cameras, however, require a special setting, which hinders their application to a mobile system. In this paper, a mobile three-dimensional motion capture system is developed based on inertial sensors and smart shoes. Sensor signals are measured and processed by a mobile computer; thus, the proposed system enables the analysis and diagnosis of postures during outdoor sports, as well as indoor activities. The measured signals are transformed into quaternion to avoid the Gimbal lock effect. In order to improve the precision of the proposed motion capture system in an open and outdoor space, a frequency-adaptive sensor fusion method and a kinematic model are utilized to construct the whole body motion in real-time. The reference point is continuously updated by smart shoes that measure the ground reaction forces.

Enable Traditional Laptops with Virtual Writing Capability Leveraging Acoustic Signals

2020 ◽  
Author(s):  
Li Lu ◽  
Jian Liu ◽  
Jiadi Yu ◽  
Yingying Chen ◽  
Yanmin Zhu ◽  
...  
Keyword(s):  
Character Recognition ◽  
Trajectory Optimization ◽  
Optimization Method ◽  
Acoustic Signals ◽  
Hand Movements ◽  
Average Error ◽  
Daily Lives ◽  
Doppler Shifts ◽  
Touch Screens ◽  
Additional Hardware

Abstract Human–computer interaction through touch screens plays an increasingly important role in our daily lives. Besides smartphones and tablets, laptops are the most prevalent mobile devices for both work and leisure. To satisfy the requirements of some applications, it is desirable to re-equip a typical laptop with both handwriting and drawing capability. In this paper, we design a virtual writing tablet system, VPad, for traditional laptops without touch screens. VPad leverages two speakers and one microphone, which are available in most commodity laptops, to accurately track hand movements and recognize writing characters in the air without additional hardware. Specifically, VPad emits inaudible acoustic signals from two speakers in a laptop and then analyzes energy features and Doppler shifts of acoustic signals received by the microphone to track the trajectory of hand movements. Furthermore, we propose a state machine-based trajectory optimization method to correct the unexpected trajectory and employ a stroke direction sequence model based on probability estimation to recognize characters users write in the air. Experimental results show that VPad achieves the average error of 1.55 cm for trajectory tracking and the accuracy over 90% of character recognition merely through built-in audio devices on a laptop.

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