Low-cost Microelectromechanical System Sensors Research Articles

Semantic Proximity Update of GNSS/INS/VINS for Seamless Vehicular Navigation Using Smartphone Sensors

The advancement of microelectromechanical systems (MEMS) and the Internet of Things (IoT) have enabled a wide range of applications based on smartphones. However, the existing navigation methods using these low-cost MEMS sensors cannot provide acceptable information for location-based applications in various environments. Their technical limitations, such as severe signal attenuation, reflections, blockages, error accumulation, and low quality of images degrade the performance of GNSS, INS, and camera. To mitigate these limitations, especially in indoor vehicle navigation, we first analyze the performance of the existing fusion algorithm, then we propose Semantic Proximity Update (SPU) based on a pre-trained model of real-time object detection to enhance the integration of Global Navigation Satellite System (GNSS), Inertial Navigation System (INS) and Visual Inertial Navigation System (VINS). SPU consists of the detection of geo-referenced objects and the relative movement to infer the absolute position. The proposed INS/GNSS/VINS/SPU can maintain long-term acceptable accuracy regardless of the indoor/outdoor environment. It only requires the use of smartphone sensors; thus, this scheme has no additional cost for users. Experimental results indicated that the errors of this scheme in horizontal positioning and three-dimensional positioning were 51.6% and 86.8% lower, respectively than those of a conventional integration.

Fault and calibration shift detection in a robust MEMS accelerometer array

The latest generation micro-electro-mechanical system(MEMS) accelerometers offer high bandwidth and low noisefloors previously limited to piezoelectric (PZT) based sensors.These relatively low cost MEMS sensors drastically expandthe financially practical applications for high frequency,vibration based, prognostics health management (PHM).This paper examines a robust array of MEMS accelerometersfor applications where sensor access after deploymentis difficult or infeasible. Three identical single axis MEMSaccelerometers were place in an array for testing. Insteadof a typical tri-axial configuration, the three sensors werealigned on a common axis. An auto-correlation algorithmwas used to detect gross system faults of individual sensorsin the array. A separate algorithm was developed to detectabnormal sensor sensitivity drift. The 3 sensor array wastested under a variety of conditions to test the developedalgorithms; power supply voltages were systematically variedaffecting the ratio-metric accelerometer sensitivity andindividual sensor mounts were purposely compromised tosimulate common fault symptoms. A decision logic treewas then implemented to respond to both types of faults.Results show the feasibility of implementing robust MEMSaccelerometer arrays using the latest generation of high bandwidthMEMS accelerometers. Planned future work includesdeploying the sensor array on tribology test equipment tovalidate MEMS sensor effectiveness compared to traditionalPZT based accelerometers.

Simultaneous compensation of systematic errors of a low-cost MEMS triaxial accelerometer and its temperature dependency without accurate laboratory equipment

PurposeLaboratory calibration methods are time-consuming and require accurate devices to find the error coefficients of the low-cost microelectromechanical system (MEMS) accelerometer. Besides, low-cost MEMS sensors highly depend on temperature because of their silicon property and the effect of temperature on error coefficients should also be considered for compensation. This paper aims to present a field calibration method in which the accelerometer is placed in different positions without any accurate equipment in a few minutes and its temperature is changed by a simple device like a hairdryer.Design/methodology/approachIn this paper, a non-linear cost function is defined based on this rule that the magnitude of the acceleration measured by the accelerometer in static mode is equal to the gravity plus error factors. Also, the dependency of error coefficients of the accelerometer is presented as a second-order polynomial in this cost function. By minimizing the cost function, the accelerometer error coefficients include bias, scale factor and non-orthogonality and their temperature dependency are obtained simultaneously.FindingsSimulation results in MATLAB and empirical results of a MPU6050 accelerometer verify the good performance of the proposed calibration method.Originality/valueFinding a fast and simple field calibration method to calibrate a low-cost MEMS accelerometer and compensate for the temperature dependency without using accurate laboratory equipment can help a wide range of industries that use advanced and expensive sensors or use expensive laboratory equipment to calibrate their sensors, to decrease their costs.

Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller.

Accurate attitude and heading reference system (AHRS) play an essential role in navigation applications and human body tracking systems. Using low-cost microelectromechanical system (MEMS) inertial sensors and having accurate orientation estimation, simultaneously, needs optimum orientation methods and algorithms. The error of attitude estimation may lead to imprecise navigation and motion capture results. This paper proposed a novel intermittent calibration technique for MEMS-based AHRS using error prediction and compensation filter. The method, inspired from the recognition of gyroscope’s error and by a proportional integral (PI) controller, can be regulated to increase the accuracy of the prediction. The experimentation of this study for the AHRS algorithm, aided by the proposed prediction filter, was tested with real low-cost MEMS sensors consists of accelerometer, gyroscope, and magnetometer. Eventually, the error compensation was performed by post-processing the measurements of static and dynamic tests. The experimental results present about 35% accuracy improvement in attitude estimation and demonstrate the explicit performance of proposed method.

On the Usage of Low-Cost MEMS Sensors, Strapdown Inertial Navigation, and Nonlinear Estimation Techniques in Dynamic Positioning

In this article, we suggest that a strapdown inertial navigation system based on microelectromechanical system (MEMS) inertial sensors is a useful addition to a vessel with dynamic positioning (DP). We conduct full-scale experiments with MEMS inertial sensors on board a DP vessel operating off the Norwegian coast. The vessel operates in different scenarios, and the purpose is to showcase how low-cost MEMS sensors may complement or replace existing DP sensor systems. Employing nonlinear observers for estimating attitude, heave, velocity, and position, we go through the benefits and disadvantages, and some caveats, for the sensors and methods used in this article. Two different MEMS units are evaluated, aided by gyrocompasses and position reference systems. We evaluate the attitude, heave, and dead reckoning capabilities obtained with the presented estimators, in relation to relevant class notation, ultimately motivating the inclusion of new sensors and methods for dynamic positioning. The results related to attitude and heave are compared with data from well-proven industry standard vertical reference units while dead reckoning is evaluated with respect to the onboard position reference systems.

A Robust EMD-Based RVFL Network Fusion Algorithm for Low-Cost GPS/INS Integrated System

Global positioning system (GPS) and inertial navigation system (INS) are commonly combined to overcome disadvantages of each and constitute an integrated system that realizes long-term precision. However, the performance of the integrated system deteriorates on which GPS is unavailable. Especially when low-cost inertial sensors based on the microelectromechanical system (MEMS) are used, performance of the integrated system degrades severely over time. In this study, in order to minimize the adverse impact of high-level stochastic noise from low-cost MEMS sensors, denoising technology based on empirical mode decomposition (EMD) is employed to improve signal quality before navigation solution by which significant improvement of removing noise is achieved. Moreover, a random vector functional link (RVFL) network-based fusion algorithm is presented to estimate and compensate position error during GPS outage such that error accumulation is suppressed quickly when INS is working standalone. Performance of the proposed approach is evaluated by experimental results. It is indicated from comparison that the proposed algorithm takes advantages such as better accuracy and lower complexity and is more robust than the commonly reported methods and is more appropriate for real-time and low-cost application.

A Pervasive Integration Platform of Low-Cost MEMS Sensors and Wireless Signals for Indoor Localization

Location service is fundamental to many Internet of Things applications such as smart home, wearables, smart city, and connected health. With existing infrastructures, wireless positioning is widely used to provide the location service. However, wireless positioning has the limitations such as highly depending on the distribution of access points (APs); providing a low sample-rate and noisy solution; requiring extensive labor costs to build databases; and having unstable RSS values in indoor environments. To reduce these limitations, this paper proposes an innovative integrated platform for indoor localization by integrating low-cost microelectromechanical systems (MEMS) sensors and wireless signals. This proposed platform consists of wireless AP localization engine and sensor fusion engine, which is suitable for both dense and sparse deployments of wireless APs. The proposed platform can automatically generate wireless databases for positioning, and provide a positioning solution even in the area with only one observed wireless AP, where the traditional trilateration method cannot work. This integration platform can integrate different kinds of wireless APs together for indoor localization (e.g., WiFi, Bluetooth low energy, and radio frequency identification). The platform fuses all of these wireless distances with low-cost MEMS sensors to provide a robust localization solution. A multilevel quality control mechanism is utilized to remove noisy RSS measurements from wireless APs and to further improve the localization accuracy. Preliminary experiments show the proposed integration platform can achieve the average accuracy of 3.30 m with the sparse deployment of wireless APs (1 AP per 800 m2).

Smart reinforcement steel bars with low-cost MEMS sensors for the structural health monitoring of RC structures

Structural health monitoring (SHM) of civil structures and infrastructures is increasingly drawing the attention of stakeholders and of the research community because of the emerging need in the optimal maintenance and management of the existing assets. The monitoring of reinforced concrete (RC) structures through measuring local strains is a valuable procedure but only a limited number of alternative technologies are currently available, mainly including electric strain gages and fibre optic sensors. The difficulty of their installation, the high cost of their sensing and/or interrogating equipment and their uncertain long-term reliability push for alternative low-cost and distributed systems, easier to install and use. This paper presents a new concept of an instrumented reinforcement steel bar, achieving strain-sensing capabilities through the incorporation of an ordinary low-cost embedded Micro Electro-Mechanical Systems (MEMS) sensor. According to this concept, a sealed cavity is made in the bar, filled with a fluid and hosting the MEMS sensor; the temperature and pressure readings collected by the MEMS sensor are used to evaluate the cavity volume variations and are thus correlated with the bar axial deformation. The proposed smart system shows significant advantages over current strain-measurement devices, proving cheap, durable, easy to install, robust to mechanical shocks and chemical aggression. The theoretical framework of the new concept and the results of a preliminary mechanical test campaign are here presented and discussed.

Calibration of a micro-electro mechanical system-based accelerometer for vehicle navigation

Micro-electro mechanical system-based inertial sensors have broad applications in moving objects including in vehicles for navigation purposes. The low-cost micro-electro mechanical system sensors are normally subject to high dynamic errors such as linear or nonlinear bias, misalignment errors and random noises. In the class of low cost sensors, keeping the accuracy at a reasonable range has always been challenging for engineers. In this paper, a novel method for calibrating low-cost micro-electro mechanical system accelerometers is presented based on soft computing approaches. The method consists of two steps. In the first step, a preliminary model for error sources is presented based on fuzzy subtractive clustering algorithm. This model is then improved using adaptive neuro-fuzzy systems. A Kalman filter is also used to calculate the vehicle velocity and its position based on calibrated measured acceleration. The performance of the presented approach has been validated in the simulated and real experimental driving scenarios. The results show that this method can improve the accuracy of the accelerometer output, measured velocity and position of the vehicle by 79.11%, 97.63% and 99.28%, in the experimental test, respectively. The presented procedure can be used in collision avoidance and emergency brake assist systems.

Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.

Integrated Navigation System Design for Micro Planetary Rovers: Comparison of Absolute Heading Estimation Algorithms and Nonlinear Filtering.

This paper provides algorithms to fuse relative and absolute microelectromechanical systems (MEMS) navigation sensors, suitable for micro planetary rovers, to provide a more accurate estimation of navigation information, specifically, attitude and position. Planetary rovers have extremely slow speed (~1 cm/s) and lack conventional navigation sensors/systems, hence the general methods of terrestrial navigation may not be applicable to these applications. While relative attitude and position can be tracked in a way similar to those for ground robots, absolute navigation information is hard to achieve on a remote celestial body, like Moon or Mars, in contrast to terrestrial applications. In this study, two absolute attitude estimation algorithms were developed and compared for accuracy and robustness. The estimated absolute attitude was fused with the relative attitude sensors in a framework of nonlinear filters. The nonlinear Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) were compared in pursuit of better accuracy and reliability in this nonlinear estimation problem, using only on-board low cost MEMS sensors. Experimental results confirmed the viability of the proposed algorithms and the sensor suite, for low cost and low weight micro planetary rovers. It is demonstrated that integrating the relative and absolute navigation MEMS sensors reduces the navigation errors to the desired level.

A 3D indoor positioning system based on low-cost MEMS sensors

A positioning system in the absence of GPS is important in establishing indoor directional guidance and localization. Inertial Measuring Units (IMUs) can be used to detect the movement of a pedestrian. In this paper, we present a three-dimensional (3D) indoor positioning system using foot mounted low cost Micro-Electro-Mechanical System (MEMS) sensors to locate the position and attitude of a person in 3D view, and to plot the path travelled by the person. The sensors include accelerometers, gyroscopes, and a barometer. The pedestrians motion information is collected by accelerometers and gyroscopes to achieve Pedestrian Dead-Reckoning (PDR) which is used to estimate the pedestrian’s rough position. A zero velocity update (ZUPT) algorithm is developed to detect the standing still moment. A Kalman filter is combined with the ZUPT to eliminate non-linear errors in order to obtain accurate positioning information of a pedestrian. The information collected by the barometer is integrated with the accelerometer data to detect the altitude changes and to obtain accurate height information. The main contribution of this research is that the approach proposed fuses barometer and accelerometer in Kalman filter to obtain accurate height information, which has improved the accuracy at x axis and y axis. The proposed system has been tested in several simulated scenarios and real environments. The distance errors are around 1%, and the positioning errors are less than 1% of the total travelled distance. Results indicate that the proposed system performs better than other similar systems using the same low-cost IMUs.

DEVELOPMENT OF A PEDESTRIAN INDOOR NAVIGATION SYSTEM BASED ON MULTI-SENSOR FUSION AND FUZZY LOGIC ESTIMATION ALGORITHMS

Abstract. This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system to extend its usability. The experiment results of the proposed navigation system demonstrate good navigation performance in indoor environment with the accurate initial location and direction.

A Handheld Inertial Pedestrian Navigation System With Accurate Step Modes and Device Poses Recognition

In this paper, a handheld inertial pedestrian navigation system (IPNS) based on low-cost microelectromechanical system sensors is presented. Using the machine learning method of support vector machine, a multiple classifier is developed to recognize human step modes and device poses. The accuracy of the selected classifier is >85%. A novel step detection model is created based on the results of the classifier to eliminate the over-counting and under-counting errors. The accuracy of the presented step detector is >98%. Based on the improvements of the step modes recognition and step detection, the IPNS realized precise tracking using the pedestrian dead reckoning algorithm. The largest location error of the IPNS prototype is ~40 m in an urban area with a 2100-m-long distance.

Low-Cost MEMS Sensors and Vision System for Motion and Position Estimation of a Scooter

The possibility to identify with significant accuracy the position of a vehicle in a mapping reference frame for driving directions and best-route analysis is a topic which is attracting a lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate position, orientation and velocity of the system with high measurement rates. In this work we test a system which uses low-cost sensors, based on Micro Electro-Mechanical Systems (MEMS) technology, coupled with information derived from a video camera placed on a two-wheel motor vehicle (scooter). In comparison to a four-wheel vehicle; the dynamics of a two-wheel vehicle feature a higher level of complexity given that more degrees of freedom must be taken into account. For example a motorcycle can twist sideways; thus generating a roll angle. A slight pitch angle has to be considered as well; since wheel suspensions have a higher degree of motion compared to four-wheel motor vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a “Vespa” scooter; which can be used as alternative to the “classical” approach based on GPS/INS sensor integration. Position and orientation of the scooter are obtained by integrating MEMS-based orientation sensor data with digital images through a cascade of a Kalman filter and a Bayesian particle filter.

Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors

Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.

A Small Low-Cost Hybrid Orientation System and Its Error Analysis

Inertial orientation systems have many potential uses beyond air- and spacecraft applications. Compared to the conventional large-scale system, the novel aspects of an orientation system based on micro electromechanical systems (MEMS) are small size, lightweight, low-power consumption, and low cost by the inclusion of microsensors with on-chip functionality, such as micromachined rate gyroscopes, accelerometers, and etc. However, the performance associated with these low-cost MEMS sensors is generally not satisfied. A conventional strapdown method based on integral of angular rate to figure out attitudes will inevitably induce long-term drift. For eliminating this accumulative error and thus using the low-cost navigation system for a long-duration mission, a hybrid configuration by combining a MEMS-based azimuth-level detector with the conventional strapdown system is proposed in this paper. The azimuth-level detector is composed of three-axis MEMS accelerometers and three-axis MEMS magnetometers. With an absolute solution based on the gravity and the earth magnetic field rather than the integral algorithm, attitude and heading estimation without drift errors can be obtained so as to facilitate adjusting the attitude and heading for the conventional strapdown system. The hierarchical computation process for estimating the attitude and heading using the hybrid system is depicted first. Then, comprehensive system errors including transformation errors of the computation, such as skew, scale, and drift errors, are analyzed. Finally, it is verified by both of formula analysis and experimental test that the accumulative errors can be effectively eliminated via this hybrid scheme.