Grade Inertial Sensors Research Articles

Real time localization solution for land vehicle application using low-cost integrated sensors with GPS

The technique proposed in this research demonstrates a real time nonlinear data fusion solution based on extremely low-cost and grade inertial sensors for land vehicle navigation. Here, the utilized nonlinear multi-sensor data fusion (MSDF) is based on the combination between extremely low-cost micro electrical mechanical systems (MEMS) inertial, heading, pressure, and speed sensors in addition to satellite-based navigation system. The integrated navigation system fuses position and velocity states from the Global Positioning System (GPS), the velocity measurements from an odometer, heading angle observation from a magnetometer and navigation states from an inertial navigation system (INS). The implemented system performance is assessed through the post-processing of collected raw measurements and real time experimental work. The system that runs the real-time experiments is established on three connected platforms, two of them are based on a 32-bit ARMTM core and the third one is based 16-bit AVR ATMEL microcontroller. This microcontroller is connected to an on-board diagnostics (OBD) shield to collect the vehicle speed measurements. The raw data obtained from the integrated sensors is saved and post processed in MATLAB®. In normal conditions, the estimated position errors are reduced through the usage of INS/GPS integration with heading observation angle from a magnetometer. In GPS-denied environments, the integrated system uses the observations from INS, magnetometer in addition to the velocity from odometer to ensure a continuous and accurate navigation solution. A complementary filter (CF) is implemented to estimate and improve the pitch and roll angles calculations. In addition to that, an unscented Kalman filter (UKF) is used cascaded with the designed CF to complete the designed sensors fusion algorithm. Experimental results show that the designed MSDF can achieve a good level of accuracy and a continuous localization solution of a land vehicle in different GPS availability cases and can be implemented on the available in the market processors to be run in real time.

Robust Positioning for Road Information Services in Challenging Environments

Next-generation Intelligent Transportation Systems (ITS) of future road traffic monitoring will be required to provide reports on traffic status, road conditions, and driver behaviour. Road surface anomalies contribute to increasing the risk of traffic accidents, reduced driver comfort and increased vehicles’ damage. The conventional integrated Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) positioning solutions can suffer from errors because of inertial sensor noises and biases, especially when low-cost and commercial grade inertial sensors are used. In this work, we use a reduced inertial sensor system utilizing Micro-Electro-Mechanical-System (MEMS) based inertial sensors, to integrate with the GNSS receiver and provide robust positioning in urban canyons. To provide acceptable performance in challenging urban environments, our method de-noises the MEMS-based inertial sensor measurements using a technique based on a Bi-orthonormal search, which separates the monitored motion dynamics from both the inertial sensor bias errors and high-frequency noises. As a result, the performance of the positioning system is improved, providing reliable positioning accuracy during extended GNSS outages that occur in various areas. To show the significant enhancement achieved by the proposed approach, we examined the system performance over three road test trajectories involving MEMS-based inertial sensors and GNSS receivers mounted on our test vehicle. The superior performance of our proposed INS/GNSS integrated positioning system is demonstrated in this paper during various GNSS outages, in different areas, and under multiple driving scenarios.

Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance.

In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians’ lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT.

Decentralized INS/GNSS System With MEMS-Grade Inertial Sensors Using QR-Factorized CKF

Special approaches are required for integration of global navigation satellite system (GNSS) with a strap-down inertial navigation system (INS), particularly based on low-cost micro-electro mechanical system (MEMS)-grade inertial sensors. The proposed approach should be computationally efficient, mathematically nonsingular, and executable on small digital signal processor (DSP) modules. This paper presents a new INS/GNSS navigation system based on a direct decentralized integration scheme. Based on the proposed QR-factorized cubature Kalman filter (CKF) structure, two cascade filters are implemented for separate estimation of the orientation attitude-heading angles and the 3-D position/velocity components. Owing to the QR-factorization, the numerical errors in the update process of estimation covariance matrix are removed. Considering the nonlinear dynamics of the strap-down INS as well as the large uncertainties included in stochastic model of the MEMS-grade inertial sensors, the QR-factorized CKF yields enhanced accuracy and reliability compared with the pure Kalman filter. The decentralized integration scheme provides the separate estimation of orientation components from the position and velocity vectors. Therefore, the propagation of the position and velocity estimation errors into the orientation filter section does not occur. The performance of the presented system is assessed through real data of vehicular field tests.

Analysis of accuracy requirements for an inertial navigation system in synthetic aperture radars

This paper describes the effect of trajectory signal phase distortion on the image received by a millimeter-wave automobile synthetic aperture radar (SAR). Calculations of the requirements for the errors of the sensors included in a strapdown inertial system that provide the resultant image with acceptable quality are given. Parameters of different grade inertial sensors are analyzed; recommendations for choosing inertial sensors depending on SAR operating conditions and required resolution are analyzed.

MEMS and FOG Technologies for Tactical and Navigation Grade Inertial Sensors-Recent Improvements and Comparison.

In the following paper, we present an industry perspective of inertial sensors for navigation purposes driven by applications and customer needs. Microelectromechanical system (MEMS) inertial sensors have revolutionized consumer, automotive, and industrial applications and they have started to fulfill the high end tactical grade performance requirements of hybrid navigation systems on a series production scale. The Fiber Optic Gyroscope (FOG) technology, on the other hand, is further pushed into the near navigation grade performance region and beyond. Each technology has its special pros and cons making it more or less suitable for specific applications. In our overview paper, we present latest improvements at NG LITEF in tactical and navigation grade MEMS accelerometers, MEMS gyroscopes, and Fiber Optic Gyroscopes, based on our long-term experience in the field. We demonstrate how accelerometer performance has improved by switching from wet etching to deep reactive ion etching (DRIE) technology. For MEMS gyroscopes, we show that better than 1°/h series production devices are within reach, and for FOGs we present how limitations in noise performance were overcome by signal processing. The paper also intends a comparison of the different technologies, emphasizing suitability for different navigation applications, thus providing guidance to system engineers.

Validation of nonlinear integrated navigation solutions

There exist numerous navigation solutions already implemented into various navigation systems. Depending on the vehicle in which the navigation system is used, it can be distinguished in most cases among; navigation, tactical, and commercial grade categories of such systems. The core of these systems is formed by inertial sensors, i.e. accelerometers and angular rate sensors/gyros. Navigation and tactical grade systems commonly rely on fiber optic/ring laser gyros and servo/quartz accelerometers with high resolution, sensitivity, and stability. In the case of cost-effective navigation systems, for example piloted light and ultralight aircraft, usually use commercial grade sensors, where the situation differs. The sensor outputs are less stable and sensitive, and suffer from manufacturing limits leading to temperature dependency, bias instability, and misalignment which introduces non-negligible disturbances. These conditions commonly limit the applicability of the navigation solution since its stand-alone operation using free integration of accelerations and angular rates is not stable. This paper addresses a cost-effective solution with commercial grade inertial sensors, and studies the performance of different approaches to obtain navigation solution with robustness to GNSS outages. A main goal of this paper is thus comparison of a nonlinear observer and two extended Kalman filter solutions with respect to the accuracy of estimated quantities and their sensitivity to GNSS outages. The performance analyses are carried out on real flight data and evaluated during phases of the flight when the solutions are challenged by different environmental disturbances.

Personal Navigation Algorithms Based on Wireless Networks and Inertial Sensors

Abstract The work aims at a development of positioning algorithm suitable for low-cost indoor or urban pedestrian navigation application. The sensor fusion was applied to increase the localization accuracy. Due to required low application cost only low grade inertial sensors and wireless network based ranging were taken into account. The wireless network was assumed to be preinstalled due to other required functionality (for example: building control) therefore only received signal strength (RSS) range measurement technique was considered. Wireless channel loss mapping method was proposed to overcome the natural uncertainties and restrictions in the RSS range measurements The available sensor and environment models are summarized first and the most appropriate ones are selected secondly. Their effective and novel application in the navigation task, and favorable fusion (Particle filtering) of all available information are the main objectives of this thesis.

An elastic deformation measurement method for helicopter based on double-IMUs/DGPS TRAMS

Because of the severe vibration during flight process, the elastic deformation caused by the weight of avionic devices which are placed in the head nacelle of the helicopter such as millimeter wave radar cannot be measured through the optical-imaging and accelerometer-induction method. In order to survey this elastic deformation in real-time, this paper proposes the double-IMUs/DPGS tightly-coupled relative attitude measurement system (TRAMS): double IMUs are comprised by the same accuracy grade inertial sensors; one IMU is assembled in the head nacelle, while the other is in the airframe; the double-IMUs tightly-coupled with the DPGS respectively can measure the attitudes (pitch, roll and heading) of the nacelle and helicopter airframe in high accuracy; the relative attitudes between the two single IMU/DPGS TRAMSs reflect the elastic deformation between the nacelle and airframe. The double-IMUs/DPGS TRAMS have been effectively verified through the ground experiment in a vehicle and two flight tests in a military helicopter. The results present satisfactory stability and repeatability. This system can be popularized to other dynamic deformation measurement backgrounds.

Low cost two dimension navigation using an augmented Kalman filter/Fast Orthogonal Search module for the integration of reduced inertial sensor system and Global Positioning System

Due to their complementary characteristics, Global Positioning System (GPS) is integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low-cost. In this study, a low cost reduced inertial sensor system (RISS) is considered. It consists of a MEMS-grade gyroscope and the vehicle built-in odometer. The system works together with GPS to provide 2D navigation for land vehicles. With adequate accuracy, Kalman filter (KF) is the commonly used estimation technique to achieve the data fusion of GPS and inertial sensors in case of high-end IMUs. However, due to the inherent error characteristics of MEMS grade devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be handled by KF and its linear error models. To overcome the problem, Fast Orthogonal Search (FOS), a nonlinear system identification technique, is suggested for modeling the higher order RISS errors. As a general-purpose numerical method, FOS algorithm has the ability to figure out the system nonlinearity efficiently with a tolerance of arbitrary stochastic system noise. Even using online short-term training data, this method is still able to build an accurate nonlinear model that predicts the system dynamics. Motivated by the above merits, an augmented KF/FOS module is proposed by cascading FOS algorithm to a traditional KF structure. By estimating and reducing both linear and nonlinear RISS errors, the proposed method is supposed to offer substantial enhancement on the positioning accuracy of MEMS-based RISS during GPS outages. In order to examine the effectiveness of the proposed technique, the KF/FOS module is applied on the low cost RISS together with GPS in a land vehicle for several road test trajectories. The performance of the proposed method is compared to KF-only solution, both assessed with respect to a reference offered by a high-end solution. The experimental results confirm that KF/FOS module outperforms KF-only method. The results also show the applicability of the proposed method for real-time vehicle applications.

Experimental Results on an Integrated GPS and Multisensor System for Land Vehicle Positioning

Global position system (GPS) is being widely used in land vehicles to provide positioning information. However, in urban canyons, rural tree canopies, and tunnels, the GPS satellite signal is usually blocked and there is an interruption in the positioning information. To obtain positioning solution during GPS outages, GPS can be augmented with an inertial navigation system (INS). However, the utilization of full inertial measurement unit (IMU) in land vehicles could be quite expensive despite the use of the microelectromechanical system (MEMS)‐based sensors. Contemporary research is focused on reducing the number of inertial sensors inside an IMU. This paper explores a multisensor system (MSS) involving single‐axis gyroscope and an odometer to provide full 2D positioning solution in denied GPS environments. Furthermore, a Kalman filter (KF) model is utilized to predict and compensate the position errors of the proposed MSS. The performance of the proposed method is examined by conducting several road tests trajectories using both MEMS and tactical grade inertial sensors. It was found that by using proposed MSS algorithm, the positional inaccuracies caused by GPS signal blockages are adequately compensated and resulting positional information can be used to steer the land vehicles during GPS outages with relatively small position errors.

MEMs for Aerospace Navigation

The MEMS aerospace market has its own specificities in terms of market size, standards, and performance characteristics (very long term stability, reliability, and safety levels). Improvements are needed for future applications. THALES Avionics has a twenty-year experience in quartz and silicon MEMS design and manufacturing and is recognized as a leader by the French MOD in this field. MEMS pressure sensors and accelerometers are manufactured in large volume and used for safety-critical applications. THALES technology policy focused on planar architecture, die vacuum packaging, and deep reactive ion etching (DRIE) allowing good characteristics for sensors in development. A Silicon Vibrating Beam Accelerometer single chip is now under development. Its operating principle is described with two resonators in push-pull configuration. A tuning fork planar rate gyro is also developed with exactly the same technology for industrial efficiency. Performance results will be addressed. The development and industrialization road-map of theses inertial products is described for the following five years. Gyro-compassing grade inertial sensors would be available during the next decade allowing low-cost, high-grade navigators using simultaneously GNSS receivers and inertial MEMS navigators.

Integrating Inertial Sensors With Global Positioning System (GPS) for Vehicle Dynamics Control

This paper demonstrates a method of estimating several key vehicle states—sideslip angle, longitudinal velocity, roll and grade—by combining automotive grade inertial sensors with a Global Positioning System (GPS) receiver. Kinematic Kalman filters that are independent of uncertain vehicle parameters integrate the inertial sensors with GPS to provide high update estimates of the vehicle states and the sensor biases. Using a two-antenna GPS system, the effects of pitch and roll on the measurements can be quantified and are demonstrated to be quite significant in sideslip angle estimation. Employing the same GPS system as an input to the estimator, this paper develops a method that compensates for roll and pitch effects to improve the accuracy of the vehicle state and sensor bias estimates. In addition, calibration procedures for the sensitivity and cross-coupling of inertial sensors are provided to further reduce measurement error. The resulting state estimates compare well to the results from calibrated models and Kalman filter predictions and are clean enough to use in vehicle dynamics control systems without additional filtering.