This paper presents the performance evaluation of our wireless miniature Inertial Measurement Unit (IMU) WB-4 by compared with the Vicon motion capture system. In particular, a magnetic field calibration method is introduced to improve the sensor orientation estimate accuracy. The WB-4 IMU primarily contains a motherboard for motion sensing, a Bluetooth module for wireless data transmission with PC, and a Li-Polymer battery for power supply. The motherboard is provided with a 32-bit microcontroller and 3-axis miniaturized MEMS accelerometer, 3-axis gyroscope and 3-axis magnetometer to estimate the sensor orientation based on an extended Kalman filter algorithm. In our previous research of WB-4 IMU performance evaluation, the factory calibration parameters of the magnetometer were used for the sensor fusion, which resulted in a higher error on the yaw angle in respect to roll and pitch. This study presents a magnetic calibration method for overcoming that limitation. The experimental results showed that the wireless WB-4 IMU could achieve better orientation performance in all the directions after the implementation of the magnetic calibration method. The yaw angle accuracy was significantly improved from previous error 5.46 degree to 1.77 degree. View full abstract»

This paper presents a modified complementary separate-bias Kalman filter for orientation determination of pedestrian motions by using a inertial measurement unit (IMU) module, which contains gyroscopes, accelerometers and magnetometers as an Attitude and Heading Reference System (AHRS). The filter consists of two main functions: the complementary separate-bias Kalman filtering avoids the modelling of pedestrian motions and fuses the sensed data; the magnetic disturbance detection and minimization provides robustness and stability when the sensor module is experiencing local magnetic disturbances. Test case includes stairs climbing indoors and long-distance walking outdoors. In both case the filter is able to provide stable orientation data and minimize the impact of local magnetic field disturbance. View full abstract»

Attitude determination systems utilizing low cost MEMS sensors are increasingly becoming important due to its advantages in terms of the quickly improved precision, robust, high dynamic response and more significantly inexpensive costs of development and usage. However the large noises inherent in low cost MEMS sensors degrade the derived attitude precision if utilized through the conventional methods, e.g. initial alignment, strapdown inertial navigation mechanization. Therefore the novel application approach suitable for MEMS needs to be investigated. This paper describes an attitude determination system that is based on low cost MEMS inertial sensor, a triad of magnetometers and a commercial GPS receiver. Two main issues are addressed in the paper; firstly determination of the attitude initials, the algorithm is based on a quaternion formulation, a representative of attitude, of Wahbapsilas problem, whereby the error quaternion becomes the estimated state and is corrected by two observations of the earth magnetic field and gravity respectively. After the estimates converge, the derived attitude parameters are employed to initialize the inertial navigation calculations. Due to the large noises in MEMS sensor, there is a demand for external velocity and/or position corrections in the MEMS navigation calculations when system experiences translational motions. Hence secondly, GPS solutions are integrated in a Kalman filter by providing external velocity and position observations. A Kalman dynamic model is designed appropriate for MEMS sensor noise characteristics. The bias and drift are estimated by the integrated Kalman filter, which enables the online calibrations of MEMS sensor. The proposed approach has been developed and its efficiency is demonstrated by various experimental scenarios with real MEMS data and they are compared with Novatel SPAN-IMU reference. View full abstract»

We address the problem of position estimation for a rigid body using an inertial measurement unit (IMU) and a set of spatially distributed magnetometers. We take advantage of the magnetic field disturbances usually observed indoors. This is particularly relevant when GPS is unavailable (e.g. during military operations in urban areas). We illustrate our technique with several experimental results obtained with a Kalman filter. We also present our testing bench which consists of low cost sensors (IMU and magnetometers). View full abstract»

This paper presents a novel indoor localization solution using a smart phone. Instead of building the inertial measurement unit (IMU), the integrated calibrated sensors inside the smart phone provide all the sensor information needed. Meanwhile, we avoid the complicated calibration process, when the calibration machines or workstations are not available. Since smart phones are meant to be held in hand, algorithms and methods based on walking speed reset can not be utilized. Therefore, correct orientation and step length information are indispensable. In this study, a modified Kalman filter based sensor data fusion was used to achieve accurate orientation data by detecting and minimizing the effect of magnetic field disturbance. Three methods are presented and compared to calculate each step length based on vertical acceleration using biomechanic model or empirical relation. The experimental results show that the proposed solution is capable of tracking the person indoors and to achieve a tracking accuracy of less than 0.3m. View full abstract»

In this study, an unscented Kalman filter (UKF) algorithm, which integrates mathematical model of the attitude dynamics with the measurements of inertial measurement unit (IMU) and the magnetometers, is designed. The identified vector is arranged from the Euler angles, angular rates, and the unknown constant components of the external torques (the gravity-gradient, magnetic field pressure and the sun radiation) acting on the pico satellite. Because of the inherent nonlinear dynamics and the nonlinear measurement model caused by the magnetometer measurements, UKF is selected as a filter algorithm. Performance of the proposed algorithm is demonstrated via simulations for a cube pico satellite. View full abstract»

This paper presents a viable quaternion-based complementary observer (CO) that is designed for rigid body attitude estimation. We claim that this approach is an alternative one to overcome the limitations of the extended Kalman filter. The CO processes data from a small inertial/magnetic sensor module containing triaxial angular rate sensors, accelerometers, and magnetometers, without resorting to GPS data. The proposed algorithm incorporates a motion kinematic model and adopts a two-layer filter architecture. In the latter, the Levenberg Marquardt algorithm preprocesses acceleration and local magnetic field measurements, to produce what will be called the system's output. The system's output together with the angular rate measurements will become measurement signals for the CO. In this way, the overall CO design is greatly simplified. The efficiency of the CO is experimentally investigated through an industrial robot and a commercial IMU during human segment motion exercises. These results are promising for human motion applications, in particular future ambulatory monitoring. View full abstract»

A MAG-muIMU which is based on MEMS gyroscopes, accelerometers, and magnetometers is developed for real-time estimation of human hand motions. Appropriate filtering, transformation and sensor fusion techniques are combined in the ubiquitous digital writing instrument to record handwriting on any surface. In this paper, we discuss the design of an extended Kalman filter based on MAG-muIMU (micro inertial measurement unit with magnetometers) for real-time attitude tracking. The filter utilizes the gyroscope propagation for transient updates and correction by reference field sensors, such as gravity sensors, magnetometers or star trackers. A process model is derived to separate sensor bias and to minimize wideband noise. The attitude calculation is based on quaternion which, when compared to Euler angles, has no singularity problem. Testing with synthetic data and actual sensor data proved the filter will converge and accurately track the attitude of a rigid body. Our goal is to implement this algorithm for motion recognition of a 3D ubiquitous digital pen. View full abstract»

This work presents an orientation tracking system based on a double stage Kalman filter for sensor fusion in 9D IMU. The IMU is composed by a 3D gyro, a 3D accelerometer and a magnetic compass. The filter was divided into two stages to reduce algorithm complexity. Gyro data are used to first estimate the angular position, then the first stage corrects roll and pitch angles using accelerometer data. The second stage processes magnetic compass data to correct the yaw angle. One of the advantages of this kind of filter is that a magnetic anomaly does not influence roll and pitch estimation accuracy. The flexibility is also desirable, because if the magnetic compass is not available, it is simply possible to switch off the second stage of the filter. In this work an ASIP was designed to process the filter algorithm and a proof of concept on FPGA was successfully realized. In the future the ASIP will be integrated within the logic of a new 6D sensor that could be optionally interfaced with an external magnetic compass. View full abstract»

In this paper, we describe an attitude estimation algorithm based on the direction cosine matrix (DCM) attitude representation and analyze its performance. This algorithm is appropriate for the implementation of a low-cost attitude and heading reference system (AHRS) which is composed of micro electrical mechanical system (MEMS) inertial measurement unit (IMU) and magnetometer triad. To reduce the computational burden, we estimate only six elements of the nine elements of the DCM. Kalman filtering is used to fuse the angular rate, specific force and magnetometer triad measurements. The DCM model has an advantage over other attitude representations because it has linear measurement equations of accelerometer and magnetometer triads. For the DCM orthogonalization, we recommend a low computational burden algorithm within the integration filter. Finally experimental data is used to verify the efficiency of the algorithm. View full abstract»

For the autonomous flight, navigation, guidance and control of the EFIGENIA unmanned aerial vehicle it is essential to have high performance 6-DOF attitude and heading reference system measurements. This paper presents the design and development of a real-time intelligent attitude and heading reference system I-AHRS, as in the hardware, as in the intelligent digital neural network software scheme, analysis, design and construction for the orientation calculation for the EFIGENIA EJ-1B MOZART and the EFIGENIA EJ-2B MARIA autonomous unmanned aerial vehicles UAVs. The EFIGENIA I-AHRS consists of three MEMS accelerometers, three MEMS rate-gyros and three magneto-resistive transducers that send its outputs to a digital neural network in which is possible to develop a strategy for attitude estimation. Additionally it is well known that the Kalman Filter is an option as multi-sensor data fusion and integration, however it has some adaptability limitations. In this paper, FPGA reconfigurable hardware digital neural network architecture is presented and utilized to replace the Kalman Filter in the integration of MEMS IMU inertial sensors signals and the Magneto resistive sensors. View full abstract»

This paper presents a novel indoor localization and monitoring system based on inertial sensors for emergency responders. The system utilizes acceleration, angular rate and magnetic field sensors and consists of three parts. The first part is a modified Kalman filtering which implements the sensor data fusion and meanwhile detects and minimizes the magnetic field disturbances, so as to provide a long term stable orientation solution. The second part is zero velocity updating which resets the velocity within still phase to deliver accurate position information. The last part of the system is body movement monitoring, which is achieved by calculating the relative position of each body segment based on the transformation of coordinate frame of each body segment. The experimental result shows that the system is able to track person indoors in both walking and running cases, and to monitor the body movement during whole period of experiment. View full abstract»

This paper presents a real-time heading estimation algorithm using IMU and strapdown magnetometer without any other external heading reference. To calibrate the magnetic deviation, sensor errors caused by hard iron effect and initial heading of strapdown magnetometers are considered. In our approach, sensor output distortion due to the soft iron effect is ignored, which is relatively small. First, for the estimation of heading angle, system and measurement model is derived, which is nonlinear. Then particle filter and extended Kalman filter is introduced for performance comparison. The proposed algorithm for the integration of IMU and magnetometer is verified via numerical simulation in Matlab. Simulation result demonstrates accurate heading estimation error within 1 degree for both algorithms when there exists small initial heading error and hard iron effect, yet particle filter provides more robust and precise result than the extended Kalman filter in case the initial heading error and biases are large. View full abstract»

This paper presents an application-specific integrated processor for an angular estimation system that works with 9-D inertial measurement units. The application-specific instruction-set processor (ASIP) was implemented on field-programmable gate array and interfaced with a gyro-plus-accelerometer 6-D sensor and with a magnetic compass. Output data were recorded on a personal computer and also used to perform a live demo. During system modeling and design, it was chosen to represent angular position data with a quaternion and to use an extended Kalman filter as sensor fusion algorithm. For this purpose, a novel two-stage filter was designed: The first stage uses accelerometer data, and the second one uses magnetic compass data for angular position correction. This allows flexibility, less computational requirements, and robustness to magnetic field anomalies. The final goal of this work is to realize an upgraded application-specified integrated circuit that controls the microelectromechanical systems (MEMS) sensor and integrates the ASIP. This will allow the MEMS sensor gyro plus accelerometer and the angular estimation system to be contained in a single package; this system might optionally work with an external magnetic compass. View full abstract»

It is an important and fundamental ability for a mobile robot to know its position and attitude. This article introduces several approaches for solving an indoor mobile robot positioning problem based on recursive estimation algorithm. Sensor information from a low cost inertial measurement unit, wheel mounted encoders and Wi-Fi is fused to get current robot position. Since one cannot ignore the nature properties of robot dynamic constraints, the method purposed in this paper involves incorporation of those constraints. The final results are based on field experiment. View full abstract»

This paper presents a modular architecture to develop a wearable system for real-time human motion capture. The system is based on a network of smart inertial measurement units (IMUs) distributed on the human body. Each of these modules is provided with a 32-bit RISC microcontroller (MCU) and miniaturized MEMS sensors: three-axis accelerometer, three-axis gyroscopes, and three-axis magnetometer. The MCU collects measurements from the sensors and implement the sensor fusion algorithm, a quaternion-based extended Kalman filter to estimate the attitude and the gyroscope biases. The design of the proposed IMU, in order to overcome the problems of the commercial solution, aims to improve performance and to reduce size and weight. In this way, it can be easily embedded in a tracksuit for total body motion reconstruction with considerable enhancement of the wearability and comfort. Furthermore, the main achievements will be presented with a performance comparison between the proposed IMU and some commercial platforms. View full abstract»