Implementation and performance of a GPS/INS tightly coupled assisted PLL architecture using MEMS inertial sensors.

Sensors (Basel)

Polytechnique Fédérale de Lausanne, Institute of Microengineering (IMT), Electronics and Signal Processing Laboratory, Neuchâtel, Switzerland.

Published: February 2014

The use of global navigation satellite system receivers for navigation still presents many challenges in urban canyon and indoor environments, where satellite availability is typically reduced and received signals are attenuated. To improve the navigation performance in such environments, several enhancement methods can be implemented. For instance, external aid provided through coupling with other sensors has proven to contribute substantially to enhancing navigation performance and robustness. Within this context, coupling a very simple GPS receiver with an Inertial Navigation System (INS) based on low-cost micro-electro-mechanical systems (MEMS) inertial sensors is considered in this paper. In particular, we propose a GPS/INS Tightly Coupled Assisted PLL (TCAPLL) architecture, and present most of the associated challenges that need to be addressed when dealing with very-low-performance MEMS inertial sensors. In addition, we propose a data monitoring system in charge of checking the quality of the measurement flow in the architecture. The implementation of the TCAPLL is discussed in detail, and its performance under different scenarios is assessed. Finally, the architecture is evaluated through a test campaign using a vehicle that is driven in urban environments, with the purpose of highlighting the pros and cons of combining MEMS inertial sensors with GPS over GPS alone.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3958239PMC
http://dx.doi.org/10.3390/s140203768DOI Listing

Publication Analysis

Top Keywords

mems inertial
16
inertial sensors
16
gps/ins tightly
8
tightly coupled
8
coupled assisted
8
assisted pll
8
navigation performance
8
inertial
5
sensors
5
navigation
5

Similar Publications

Innovative Modeling of IMU Arrays Under the Generic Multi-Sensor Integration Strategy.

Sensors (Basel)

December 2024

Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada.

This research proposes a novel modeling method for integrating IMU arrays into multi-sensor kinematic positioning/navigation systems. This method characterizes sensor errors (biases/scale factor errors) for each IMU in an IMU array, leveraging the novel Generic Multisensor Integration Strategy (GMIS) and the framework for comprehensive error analysis in Discrete Kalman filtering developed through the authors' previous research. This work enables the time-varying estimation of all individual sensor errors for an IMU array, as well as rigorous fault detection and exclusion for outlying measurements from all constituent sensors.

View Article and Find Full Text PDF

In recent years, microelectromechanical systems (MEMS) technology has developed rapidly, and low precision inertial devices have achieved small volume, light weight, and mass production. Under this background, array technology has emerged to achieve high precision inertial measurement under the premise of low cost. This paper reviews the development of MEMS inertial measurement unit (IMU) array technology.

View Article and Find Full Text PDF

The scale factor of thermal sensitivity serves as a crucial performance metric for micro-electromechanical system (MEMS) gyroscopes, and is commonly employed to assess the temperature stability of inertial sensors. To improve the temperature stability of the scale factor of MEMS gyroscopes, a self-compensation method is proposed. This is achieved by integrating the primary and secondary relevant parameters of the scale factor using the partial least squares regression (PLSR) algorithm.

View Article and Find Full Text PDF

To address problems in the integrated navigation error law of unmanned aerial vehicles (UAVs), this paper proposes a method for measuring the error rule in visual inertial odometry based on scene matching corrections. The method involves several steps to build the solution. Firstly, separate models were constructed for the visual navigation model, the Micro-Electromechanical System (MEMS) navigation model, and the scene matching correction model.

View Article and Find Full Text PDF
Article Synopsis
  • * A scoping review conducted between late 2023 and early 2024 identified and summarized findings from 31 studies exploring various types of wearable sensors and their effectiveness in fall risk prediction models.
  • * It recommends using specific sensors in certain body positions and suggests that while certain walking tests are useful, further empirical research is necessary to optimize the models for community application.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!