Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

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A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

Most portable systems like smart-phones are equipped with low cost consumer grade sensors, making them useful as Pedestrian Navigation Systems (PNS). Measurements of these sensors are severely contaminated by errors caused due to instrumentation and environmental issues rendering the unaided navigation solution with these sensors of limited use. The overall navigation error budget associated with pedestrian navigation can be categorized into position/displacement errors and attitude/orientation errors. Most of the research is conducted for tackling and reducing the displacement errors, which either utilize Pedestrian Dead Reckoning (PDR) or special constraints like Zero velocity UPdaTes (ZUPT) and Zero Angular Rate Updates (ZARU). This article targets the orientation/attitude errors encountered in pedestrian navigation and develops a novel sensor fusion technique to utilize the Earth’s magnetic field, even perturbed, for attitude and rate gyroscope error estimation in pedestrian navigation environments where it is assumed that Global Navigation Satellite System (GNSS) navigation is denied. As the Earth’s magnetic field undergoes severe degradations in pedestrian navigation environments, a novel Quasi-Static magnetic Field (QSF) based attitude and angular rate error estimation technique is developed to effectively use magnetic measurements in highly perturbed environments. The QSF scheme is then used for generating the desired measurements for the proposed Extended Kalman Filter (EKF) based attitude estimator. Results indicate that the QSF measurements are capable of effectively estimating attitude and gyroscope errors, reducing the overall navigation error budget by over 80% in urban canyon environment.

https://www.mdpi.com/1424-8220/11/12/11390/pdf

Use of Earth’s Magnetic Field for Mitigating Gyroscope Errors Regardless of Magnetic Perturbation

Inertial navigation system (INS) has an increasingly important role in indoor navigation, which mainly uses inertial measurement units based on a micro electro mechanical system (MEMS) to acquire data, and which is independent of the external environment. However, INS has serious accumulated errors, and thus, it was often integrated with wireless location systems (WLSs), such as ultra wideband (UWB) system, in order to enhance the position performance. Namely, MEMS-based inertial sensors have the problem of random errors. Besides, a UWB system is vulnerable to external environment conditions, such as the non-line-of-sight (NLOS) factor and multipath effects, and thus, many outliers are produced. In order to improve the overall performance of the INS/UWB system, this paper proposes the three-tier approach based on: 1) analysis and pre-filtering of random errors of MEMS-based inertial sensors, and use of a complementary filter to provide attitude information of navigation system; 2) use of the anti-magnetic ring (AMR) to eliminate the outliers from the UWB system in NLOS environment; and 3) improvement of positioning accuracy at information fusion level using the double-state adaptive Kalman filter. The proposed approach was verified by experiments that included AMR test and filter test. The obtained results have validated the proposed method efficiency.

Performance Enhancement of MEMS-Based INS/UWB Integration for Indoor Navigation Applications

................................................................................................................... ii Acknowledgements.................................................................................................. iv Dedication................................................................................................................ v Table of

Use of Earth's Magnetic Field for Pedestrian Navigation

We propose and experimentally realize the concept of frequency-division-multiplexed single-pixel imaging. Our technique relies entirely on metamaterial spatial light modulators, the advent of which has permitted advanced modulation techniques difficult to achieve with alternative approaches. So far, implementations of single-pixel imaging have used a single encoding frequency, making them sensitive to narrowband noise. Here, we implement frequency-division methods to parallelize the single-pixel imaging process at 3.2 THz. Our technique enables a trade-off between signal-to-noise ratio and acquisition speed—without altering detector integration time—thus realizing a key development due to the limitations imposed by slow thermal detectors in terahertz and far IR. In addition, our technique yields high image fidelity and marries communications concepts to single-pixel imaging, opening a new path forward for future imaging systems.

Frequency-division-multiplexed single-pixel imaging with metamaterials

This paper presents error modelling and error analysis of microelectromechnical systems (MEMS) inertial measurement unit (IMU) for a low-cost strapdown inertial navigation system (INS). The INS consists of IMU and navigation processor. The IMU provides acceleration and angular rate of the vehicle in all the three axes. In this paper, errors that affect the MEMS IMU, which is of low cost and less volume, are stochastically modelled and analysed using Allan variance. Wavelet decomposition has been introduced to remove the high frequency noise that affects the sensors to obtain the original values of angular rates and accelerations with less noise. This increases the accuracy of the strapdown INS. The results show the effect of errors in the output of sensors, easy interpretation of random errors by Allan variance, the increase in the accuracy when wavelet decomposition is used for denoising inertial sensor raw data. Defence Science Journal, 2009, 59(6), pp.650-658 , DOI:http://dx.doi.org/10.14429/dsj.59.1571

R. Ramalingam

Papers

Microelectromechnical Systems Inertial Measurement Unit Error Modelling and Error Analysis for Low-cost Strapdown Inertial Navigation System