Adaptive Kalman Filter for MEMS-IMU based Attitude Estimation under External Acceleration and Parsimonious use of Gyroscopes

International audienceThis paper presents a viable quaternion-based Adaptive Kalman Filter (q-AKF) that is designed for rigid body attitude estimation. This approach is an alternative to overcome the limitations of the classical Kalman filter. The q-AKF processes data from a small inertial/magnetic sensor module containing triaxial gyroscopes, accelerometers, and magnetometers. The proposed approach addresses two challenges. The first one concerns attitude estimation during various dynamic conditions, in which external acceleration occurs. Although external acceleration is one of the main source of loss of performance in attitude estimation methods, this problem has not been sufficiently addressed in the literature. An adaptive algorithm compensating external acceleration from the residual in the accelerometer is proposed. At each step, the covariance matrix associated with the external acceleration is estimated to adaptively tune the filter gain. The second challenge is focused on the energy consumption issue of gyroscopes for long-term battery life of Inertial Measurement Units. We study the way to reduce the gyro measurement acquisition while maintaining acceptable attitude estimation. Through numerical simulations, under external acceleration and parsimonious gyroscope's use, the efficiency of the proposed q-AKF is illustrated

Energy-aware Adaptive Attitude Estimation Under External Acceleration for Pedestrian Navigation

International audienceIn this paper, we consider the problem of rigid bodyattitudeestimationunderexternalaccelerationusingasmallinertial/magneticsensors module containing a triad of gyroscope, accelerometer,andmagnetometer.Thepaperisfocusedontwomainchallenges. The first challenge concerns the attitude estimationduring dynamic case, in which external acceleration occurs. Thislatter leads to lose performance in attitude estimation methods. Aquaternion-based adaptive Kalman filter (q-AKF) compensatingexternal acceleration from the residual in the accelerometer isdesigned. At each step, the covariance matrix of the externalacceleration is estimated to tune the filter gain adaptively. Thesecond challenge is related to the energy consumption issue ofgyroscope. In order to ensure a longer battery life for the InertialMeasurement Units (IMUs), we study the way to reduce the gyromeasurements acquisition by switching on/off the sensor whilemaintaining an acceptable attitude estimation. A smart detectionapproach isproposed to decide whether the body is indynamic orstatic case. The efficiency of the q-AKF is demonstrated throughnumerical simulations and experimental tests

Improving Inertial Velocity Estimation Through Magnetic Field Gradient-based Extended Kalman Filter

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Robust Hinf tracking control design for a class of switched linear systems using descriptor redundancy approach

International audienceThe work presented in this paper concerns the output feedback tracking control for a class of Switched Linear Systems (SLS) with external disturbances. The main result is based on a descriptor redundancy formulation of the closedloop dynamics. The proposed approach allows the avoiding of the crossing terms appearance between the controller's and the switched system's matrices leading to easier Linear Matrix Inequality (LMI) formulation. Multiple Lyapunov functional methods are utilized to the stability analysis and controller design. By introducing the Proportional-Derivative (PD) controller, a robust Hinf output feedback tracking performance has been satisfied. The efficiency of the proposed synthesis procedure has been illustrated by a numerical example

We love to read — a collaborative endeavor to build the foundation for lifelong readers

This article presents a model of a reading motivation project for a group of fourth grade students. The project incorporates strategies shown to promote engagement in literacy: opportunities for choice, reflection and social interaction. It features the use of metacognitive activities where students set weekly goals and reflect upon how they are growing as readers

Modelling and Control Structure of a Phosphorite Sinter Process with Grey System Theory

International audienceThe sintering process of phosphorite ore occurs with a large amount of return caused by untimely process control. The control task of the phosphorite ore sintering is to regulate parameters of the process to obtain a high-quality sinter. The parameter clearly responsible for the sinter quality is the temperature in the wind box (also called burn through point (BTP)). Therefore, in order to solve the real-time control task, it is necessary to predict the BTP. In this paper, the grey system theory is used as a predictive approach, which makes it possible to obtain an adequate model that has the character of a "generalized energy system" and uses a small initial sample. Based on the grey model GMC(1,n), which is constructed in real-time by using real data at the beginning of the process, the temperature is well predicted at the end of the sintering process. When the temperature does not match the set value or to find out an optimal regulation, a control synthesis is carried out through an optimization of the prediction according to the "particle swarm" algorithm

Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation

International audienceThis paper proposes an advanced solution to improve the inertial velocity estimation of a rigid body, for indoor navigation, through implementing a magnetic field gradient-based Extended Kalman Filter (EKF). The proposed estimation scheme considers a set of data from a triad of inertial sensors (accelerometer and gyroscope), as well as a determined arrangement of magnetometers array. The inputs for the estimation scheme are the spatial derivatives of the magnetic field, from the magnetometers array, and the attitude, from the inertial sensors. As it was shown in the literature, there is a strong relation between the velocity and the measured magnetic field gradient. However, the latter usually suffers from high noises. Then, the novelty of the proposed EKF is to develop a specific equation to describe the dynamics of the magnetic field gradient. This contribution helps to filter, first, the magnetic field and its gradient and second, to better estimate the inertial velocity. Some numerical simulations that are based on an open source database show the targeted improvements. At the end of the paper, this approach is extended to position estimation in the case of a foot-mounted application and the results are very promising

Data Fusion-Based Descriptor Approach for Attitude Estimation underaccelerated maneuvers

International audienceThis paper proposes the design of an attitude estimation algorithm for a rigid body subject to accelerated maneuvers. Unlike the current literature where the process model is usually driven by triaxial gyroscope measurements, we investigate a new formulation of the state-space model where the process model is given by triaxial accelerometer measurements. The observation model is given by triaxial gyroscope and magnetometer measurements. The proposed model is written as a descriptor system and takes the external acceleration sensed by the accelerometer into account. Based on this model, a Quaternion Descriptor Filter (QDF) is developped and its performance is evaluated through simulations and experimental tests in pedestrian navigation

Design of a decentralized tracking control for a class of switched large-scale systems

International audienceThis paper proposes a new design of a decentralized output-feedback tracking control for a class of switched large-scale systems with external bounded disturbances. The controller proposed herein is synthesized to satisfy the robust H tracking performance with local disturbance attenuation levels. Based on multiple switched Lyapunov functions, sufficient conditions proving the existence of the proposed controller are formulated in terms of Linear Matrix Inequalities (LMI). A deep simulation is proposed to illustrate the effectiveness of the obtained results

A Comparative Analysis of Attitude Estimation for Pedestrian Navigation with Smartphones

International audienceWe investigate the precision of attitude estimation solutions in the context of Pedestrian Dead-Reckoning (PDR) with commodity smartphones and inertial/magnetic sensors. We propose a concise comparison and analysis of a number of attitude filtering methods in this setting. We conduct an experimental study with a precise ground truth obtained with a motion capture system. We precisely quantify the error in attitude estimation obtained with each filter which combines a 3-axis accelerometer, a 3-axis magnetometer and a 3-axis gyroscope measurements. We discuss the obtained results and analyse advantages and limitations of current technology for further PDR research