Low-Cost MEMS-Based Pedestrian Navigation Technique for GPS-Denied Areas

Ali, Abdelrahman; El‐Sheimy, Naser

doi:10.1155/2013/197090

Cited by 44 publications

(27 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The peak detection algorithm is based on filtering the magnitude of acceleration signal followed by applying a threshold on the acceleration signal over a sliding window [16,17]. Its current implementation depends on high accuracy foot mounted accelerometers, which differ significantly from phone-embedded sensors.…”

Section: ) Peak Detectionmentioning

confidence: 99%

Low cost infrastructure free form of indoor positioning

Gupta

Box

Wilson

2014

2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

This paper is concerned with smartphone based inertial sensors for locating pedestrians. Two set of experiments were conducted over seven subjects. One set of the subjects walked along a straight path at a perceived normal and speedy pace, and the other set of subjects traversed a simple map consisting of two L shaped paths. In the experiments, a smartphone -HTC 2710e was handheld, with screen facing upwards was used to record the acceleration, magnetometer and gyroscope samples at 20Hz. The accelerometer samples are then analyzed to detect and count the footsteps, while gyroscope and magnetometer samples are used to estimate the heading. The results of our experiment show that the average error in foot step detection rate is less than 3% in normal walking and 5% in speedy walking using Fast Fourier Transform (FFT) based approach. While the most nearly correct estimated displacement is via Weiner approach with average percentage error of 5.74% in normal walking and 8.07% in speedy walking. The average percentage error in heading is less than 11 0 in turnings in both the L shaped paths.

show abstract

Section: ) Peak Detectionmentioning

confidence: 99%

Low cost infrastructure free form of indoor positioning

Gupta

Box

Wilson

2014

2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

show abstract

“…The magnetic field is susceptible to contamination in complex indoor environments, and this can cause deviation of heading estimation [26]. Due to the accumulation of errors, a gyroscope is insufficient for long-term heading estimation [27]. Therefore, an accurate heading is still a challenge for pedestrian navigation with a smartphone.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

2019

View full text Add to dashboard Cite

With the popularity of smartphones and the development of microelectromechanical system (MEMS), the pedestrian dead reckoning (PDR) algorithm based on the built-in sensors of a smartphone has attracted much research. Heading estimation is the key to obtaining reliable position information. Hence, an adaptive Kalman filter (AKF) based on an autoregressive model (AR) is proposed to improve the accuracy of heading for pedestrian dead reckoning in a complex indoor environment. Our approach uses an autoregressive model to build a Kalman filter (KF), and the heading is calculated by the gyroscope, obtained by the magnetometer, and stored by previous estimates, then are fused to determine the measurement heading. An AKF based on the innovation sequence is used to adaptively adjust the state variance matrix to enhance the accuracy of the heading estimation. In order to suppress the drift of the gyroscope, the heading calculated by the AKF is used to correct the heading calculated by the outputs of the gyroscope if a quasi-static magnetic field is detected. Data were collected using two smartphones. These experiments showed that the average error of two-dimensional (2D) position estimation obtained by the proposed algorithm is reduced by 40.00% and 66.39%, and the root mean square (RMS) is improved by 43.87% and 66.79%, respectively, for these two smartphones.

show abstract

“…This is because the pedestrian navigation system is used in an unknown environment. Compared with other positioning technologies based on self-contained sensors, the inertial measurement unit (IMU) has the advantage of strong independence and mainly adopts sensors, such as accelerometers and gyroscopes, to calculate pedestrian position information [5,6].…”

Section: Introductionmentioning

confidence: 99%

3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition

Wang

Kong

et al. 2018

Complexity

View full text Add to dashboard Cite

An inertial measurement unit-based pedestrian navigation system that relies on the intelligent learning algorithm is useful for various applications, especially under some severe conditions, such as the tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments, such as those involving fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an optimal gait recognition algorithm to improve the accuracy of gait detection. Then a learning-based moving direction determination method was proposed. With the Kalman filter and a zero-velocity update algorithm, different gaits could be accurately recognized, such as going upstairs, downstairs, and walking flat. According to the recognition results, the position change in the vertical direction could be reasonably corrected. The obtained 3D trajectory involving both horizontal and vertical movements has shown that the accuracy is significantly improved in practical complex environments.

show abstract

Low-Cost MEMS-Based Pedestrian Navigation Technique for GPS-Denied Areas

Cited by 44 publications

References 14 publications

Low cost infrastructure free form of indoor positioning

Low cost infrastructure free form of indoor positioning

A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition

Contact Info

Product

Resources

About