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

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Abdelrahman Ali ◽  
Naser El-Sheimy
Keyword(s):  
Low Cost ◽  
Pressure Sensors ◽  
Micro Electro Mechanical System ◽  
Attitude Estimation ◽  
Handheld Devices ◽  
Accelerometer Data ◽  
Pedestrian Navigation ◽  
Heading Estimation ◽  
Sensors Fusion ◽  
New Research

The progress in the micro electro mechanical system (MEMS) sensors technology in size, cost, weight, and power consumption allows for new research opportunities in the navigation field. Today, most of smartphones, tablets, and other handheld devices are fully packed with the required sensors for any navigation system such as GPS, gyroscope, accelerometer, magnetometer, and pressure sensors. For seamless navigation, the sensors’ signal quality and the sensors availability are major challenges. Heading estimation is a fundamental challenge in the GPS-denied environments; therefore, targeting accurate attitude estimation is considered significant contribution to the overall navigation error. For that end, this research targets an improved pedestrian navigation by developing sensors fusion technique to exploit the gyroscope, magnetometer, and accelerometer data for device attitude estimation in the different environments based on quaternion mechanization. Results indicate that the improvement in the traveled distance and the heading estimations is capable of reducing the overall position error to be less than 15 m in the harsh environments.

scholarly journals Review on Pressure Sensors: A Perspective from Mechanical to Micro-electro-mechanical systems

2021 ◽  
Author(s):  
Ankur Gupta
Keyword(s):  
High Performance ◽  
Low Cost ◽  
Pressure Sensors ◽  
Micro Electro Mechanical System ◽  
Single Chip ◽  
Micro Electro Mechanical Systems ◽  
Compact Size ◽  
Wide Range ◽  
Fabrication Procedure ◽  
Application Requirements

Swiftly emerging research prospects in the Micro-Electro-Mechanical System (MEMS) enable to build of complex and sophisticated microstructures on a substrate containing moving masses, cantilevers, flexures, levers, linkages, dampers, gears, detectors, actuators, and many more on a single chip. One of the MEMS initial products that emerged into the micro-system technology is the MEMS pressure sensor. Because of their high performance, low cost, and compact size, these sensors are extensively being adopted in numerous applications viz., aerospace, automobile, and bio-medical domain, etc. These application requirements drive and impose tremendous conditions on sensor design to overcome the tedious design and fabrication procedure before its reality. MEMS-based pressure sensors enable a wide range of pressure measurements as per the application requirements. Considering its vast utility in industries, this paper presents a detailed review of MEMS-based pressure sensors and their wide area of applications, their design aspects, and challenges, to provide state of an art gist to the researchers of a similar domain in one place.

Using Constraints for Shoe Mounted Indoor Pedestrian Navigation

2011 ◽  
Vol 65 (1) ◽  
pp. 15-28 ◽  
Author(s):  
Khairi Abdulrahim ◽  
Chris Hide ◽  
Terry Moore ◽  
Chris Hill
Keyword(s):  
Kalman Filter ◽  
Average Distance ◽  
Low Cost ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Average Return ◽  
Measurement Units ◽  
Heading Estimation ◽  
Mems Imu

Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’.The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

MEMS gyroscope/TAM-integrated attitude estimation based on moving horizon estimation

2016 ◽  
Vol 231 (8) ◽  
pp. 1451-1459 ◽  
Author(s):  
Jingli Huang ◽  
Guorong Zhao ◽  
Xiangyu Zhang
Keyword(s):  
Mechanical System ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Micro Electro Mechanical System ◽  
Attitude Estimation ◽  
Moving Horizon Estimation ◽  
Estimation Model ◽  
External Interference ◽  
Estimation System ◽  
The Military

To improve the accuracy of the attitude sensor micro electro mechanical system gyroscope in low cost satellite, a nonlinear moving horizon estimation algorithm based on micro-electro mechanical system gyroscope/three-axis magnetometer is proposed in this paper. First, a quaternion micro-electro mechanical system gyroscope/three-axis magnetometer-integrated attitude estimation model is established so as to improve the accuracy of micro-electro mechanical system gyroscope. Thanks to the concealment and autonomy, these two low cost sensors have great potential in the military area. Second, taking advantage of optimal problem in coping with constraints, a real time moving horizon estimation algorithm with equality constraint is designed to deal with the disability of solving quaternion normalization analytically in the frame work of Kalman. In this algorithm, Gauss–Newton iterative method is used to obtain the optimal state estimation in the “window”. Meanwhile, strong tracking filter of arrival cost is designed outside of the “window” to enhance system robustness for that three-axis magnetometer is vulnerable to external interference. Third, the proposed MHE is applied in the micro-electro mechanical system gyroscope/three-axis magnetometer attitude estimation system. The simulation results show that the method has higher accuracy and robustness.

UAV attitude estimation based on the dual filtering methods

2018 ◽  
Vol 6 (1) ◽  
pp. 21-31 ◽  
Author(s):  
Zhemin Zhuang ◽  
Zhijie Guo ◽  
Alex Noel Joseph Raj ◽  
Canzhu Guo
Keyword(s):  
Traditional Method ◽  
Low Cost ◽  
Attitude Estimation ◽  
Convergence Speed ◽  
Experimental Results ◽  
Convergence Time ◽  
Accelerometer Data ◽  
Traditional Methods ◽  
Content Type ◽  
Flight Data

Purpose A toy UAV performs tumbling, rolling, racing and other complex activities. It is based on low-cost hardware and hence requires a better algorithm to estimate the attitudes more accurately with low power consumption. The proposed technique based on optimized Madgwick filter and moving average filter (MAF) ensures improved convergence speed in estimating the attitude, achieves higher accuracy and provides robustness and stability of the toy UAV. The paper aims to discuss this issue. Design/methodology/approach Traditional methods are prone to problems such as slow convergence speed and errors in calculation of the attitude angles. These errors cause the vehicle to drift and tremble, thus affecting the overall stability of the vehicle. The proposed method combines the features of optimized Madgwick filter and MAF to provide better accuracy, achieved through the fusion of gyroscope and accelerometer data, and zero correction to eliminate the random drift error of the gyroscope and removal of high-frequency interference by MAF of the accelerometer data. The experimental results on actual flight data showed that the method was better than the conventional Madgwick and Mahony complementary filters. Findings The performance of the proposed method was analyzed by estimating the pitch and roll angles under the static and dynamic condition of the toy UAV. The results were compared with two traditional methods: Madgwick and Mahony complement filter. In the static condition, the variance and average error while estimating the attitudes was comparatively lower than the traditional method. For the dynamic conditions, the convergence time to achieve a prescribed swing angle was again lower than the traditional method. From these two experiments, it can be seen that the proposed method provides better attitude estimation at lower computation time. Originality/value The proposed method combines the optimized Madgwick filter and MAF to accuracy estimate the attitude of toy UAV. The algorithm mainly suits the toy UAVs which are based on low-cost hardware and require better control systems to ensure stability of the vehicle. The experimental results on real flight data illustrate that the method not only improves the convergence speed in estimating the attitude angle for large maneuvers of the toy UAV, but also achieves higher accuracy in the attitude estimation, thus ensuring the robustness and stability of the UAV.

scholarly journals Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1715
Author(s):  
Michele Alessandrini ◽  
Giorgio Biagetti ◽  
Paolo Crippa ◽  
Laura Falaschetti ◽  
Claudio Turchetti
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Network ◽  
Low Cost ◽  
Low Complexity ◽  
Human Activity Recognition ◽  
Motion Artifacts ◽  
Accelerometer Data

Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

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