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Indoor location-based service is a hot research topic whose application market size is expected to grow from $\$ $ 41 billion by 2022 and $\$ $ 58 billion by 2023. As a portable communication device, smartphones have inherent advantages (such as being an essential gadget in daily life, embedding various sensors, and having powerful computing and storage ability), which provides a great opportunity for human activity recognition and pedestrian navigation. Smartphone-based pedestrian inertial navigation systems (PINSs) utilize measurements from inertial sensors embedded in smartphones to reckon the pedestrian’s location. Compared with other indoor position technologies, PINS has the advantage of autonomy and continuity, and can achieve high-precision positioning in a short period. Though various PINS methods based on the smartphone have been proposed, up-to-date review papers that summarize relevant technologies, methods, and solutions of PINS are relatively less so far in the literature. This article aims to provide an elaborated, timely, and valuable survey of different infrastructure-free pedestrian positioning and navigation techniques, including pedestrian inertial navigation methods, performance evaluation, and applications. Finally, current challenges and future research trends are discussed. This work propels a better understanding of existing pedestrian inertial positioning methods. It is helpful for researchers to further design and develop more accurate and robust pedestrian positioning and navigation systems based on inertial sensors embedded in the smartphone. [ABSTRACT FROM AUTHOR]

Inertial navigation for autonomous underwater vehicles (AUVs) is challenging because of the drift error caused by the noise and measurement errors of inertial sensors, typically packaged as an inertial measurement unit (IMU), integrated over time. To mitigate the drift error, recent AUV state estimation approaches incorporate external references or environmental information obtained from exteroceptive sensors, with increased costs and limited operational domains. For improved navigation under sensor constraints, this article proposes an active perception framework that exploits vehicle motion to estimate the flow state together with the vehicle state using IMU and depth sensors only. The proposed framework uses the estimated flow state as external information to improve vehicle state estimation. We construct a linear time-varying system for the flow state, separated from a nonlinear system for the vehicle state. This formulation allows us to analyze uniform complete observability for the flow state, which is found to depend on vehicle motion. Then, along with vehicle and flow state estimators, we design a vehicle controller to enable vehicle motion to maximize an information metric pertaining to estimation performance based on either observability or constructability Gramian for the flow state. The proposed framework is validated through simulations for a case study with a vehicle descending through the water column in a time-varying flow field. The effectiveness of the framework is demonstrated by comparing results obtained from its four implementations with those from baseline approaches without active perception. [ABSTRACT FROM AUTHOR]

Existing research has reported that the topological obstructions pose inherent difficulty to attitude estimation. This article copes with the problem of obtaining a good estimation of attitude and gyro-bias using inertial measurements. First, we propose a quaternion-based scheme for the design and stability analysis of nonlinear attitude observers. Under the scheme, we expose the potential pitfalls of traditional observers: almost global asymptotic stability (GAS) and slow convergence for a continuous state-feedback observer, nonrobustness to arbitrary small measurement noises for a discontinuous state-feedback observer. Second, to address these limitations, we develop a generic quaternion-based hybrid scheme with guaranteed GAS and robustness to measurement noises, in the sense that knowledge of a specific potential function suffices to design an attitude and gyro-bias observer with desired properties. Furthermore, we give a new observer within the scheme. Simulation and experimental results demonstrate the fast convergence and robustness of the proposed observer. [ABSTRACT FROM AUTHOR]

In this article, the extrinsic parameter estimation of a camera mounted on an intelligent vehicle is addressed. The trifocal tensor is utilized to construct vision dynamics which relates image coordinates, velocity signals, and extrinsic parameters. Artificial visual patterns such as chessboards and planar reference objects used in homography-based methods are no longer required. An auxiliary tensor decouples the rotational extrinsic parameters from the translational ones. A key frame strategy is adopted to deal with the field of view constraint and an unknown distance is eliminated from the vision dynamics to counter the scale change caused by key frame switching. The Lyapunov method is used to design nonlinear observers, which estimate the extrinsic parameters at each time step based all collected valid historical data. Performance of the proposed method is verified by both simulation and experimental results. [ABSTRACT FROM AUTHOR]

In this paper we present a state estimation scheme for Unmanned Aircrafts (UAs) utilizing dynamics based models and multi-sensor data fusion. Employing the UA dynamics in estimation can substantially enhance the estimator performance, but obtaining accurate dynamics parameters for each UA is computationally costly and complex. To eliminate these issues, we propose two decoupled Extended Kalman Filters (EKFs), namely the Rotational Decoupled Extended Kalman Filter (RDEKF) and the Translational Decoupled Extended Kalman Filter (TDEKF). The dynamics parameters in these filters are identified in real-time using the Deep Neural Network and the Modified Relay Feedback Test (DNN-MRFT) approach. This approach doesn’t demand prior knowledge of the UA physical parameters, requiring only an Inertial Measurement Unit (IMU) and a positioning system for model classification. Our estimation scheme provides position, velocity and attitude estimates, in addition to smooth lag-free inertial acceleration estimates. We show experimentally the advantages of our approach on trajectory tracking problems that uses low rate position sensors. We also demonstrate how utilizing the estimated acceleration in feedback control can reduce the tracking error of an optimally tuned system by 43%. Moreover, the proposed estimator produces smooth estimates that leads to a reduction of controller action by 6.6%, when compared to kinematic based estimators. We compare the achieved results against other methods that require full prior knowledge of the UA parameters or the noise models, and show advantages in performance and real-time capability. [ABSTRACT FROM AUTHOR]

Jittering effects significantly degrade the performance of UAV millimeter-wave (mmWave) communications. To investigate the impacts of UAV jitter on mmWave communications, we firstly model UAV mmWave channel based on the geometric relationship between element antennas of the uniform planar arrays (UPAs). Then, we extract the relationship between (I) UAV attitude angles & position coordinates and (II) angle of arrival (AoA) & angle of departure (AoD) of mmWave channel, and we also derive the distribution of AoA/AoD at UAV side from the random fluctuations of UAV attitude angles, i.e., UAV jitter. In beam training design, with the relationship between attitude angles and AoA/AoD, we propose to generate a rough estimate of AoA and AoD from UAV navigation information. Finally, with the rough AoA/AoD estimate, we develop a compressed sensing (CS) based beam training scheme with constrained sensing range as the fine AoA/AoD estimation. Particularly, we construct a partially random sensing matrix to narrow down the sensing range of CS-based beam training. Numerical results show that our proposed UAV beam training scheme assisted by navigation information can achieve better accuracy with reduced training length in AoA/AoD estimation and is thus more suitable for UAV mmWave communications under jittering effects. [ABSTRACT FROM AUTHOR]

Aircraft attitude has always been an important technical parameter during flights, especially for stages such as aircraft take-off and landing. This article presents a monocular off-board vision system for relative attitude measurement of fixed-wing aircraft. In this system, a zoom imaging subsystem is set up to track and shoot the aircraft over a large distance range. Image sequences and corresponding servo data are transmitted to the processing computer for vision algorithms. A model-based attitude tracking algorithm, which requires the computer-aided design model of the aircraft, is performed to measure the relative attitude of the aircraft with respect to the reference platform. A 2-D visual tracking algorithm and a target detection algorithm are also employed to control the vision system. The proposed attitude tracking algorithm achieves competitive results on a publicly available dataset. The full system is validated in a real-flight experiment. The results revealed that the proposed system can run in real time on 1920×1080@40 Hz videos with an accuracy better than 0.5 $^\circ$. [ABSTRACT FROM AUTHOR]

In this article, the attitude control problem for a class of rigid body systems with switching submodes is investigated. To address such issue, an energy-optimal geometric switching control method is proposed for the first time. First, a switching Lie group method and a switching Lie group discrete variational integrator method based on the variational principle are developed. Then, continuous-time and discrete-time attitude switching dynamics models for rigid bodies are globally expressed on a special orthogonal matrix group (i.e., SO(3) group) to avoid the locality, singularity, and ambiguity caused by using the traditional Euler angle method, minimum representation method, or quaternion method. Second, the switching process of rigid bodies from initial attitude and angular velocity to desired attitude and angular velocity within a fixed maneuver time and the minimum energy consumption of rigid bodies with control saturation are solved. Furthermore, by solving the energy-optimal geometric control problem of attitude switching dynamics models, the global optimal geometric switching control and optimal switching time conditions under continuous-time and discrete-time are first derived. The obtained criteria ensure that the intrinsic geometric properties of attitude switching dynamics models will not be lost during the optimization process. Finally, some simulation results are presented to demonstrate the feasibility of the proposed techniques. [ABSTRACT FROM AUTHOR]

Pedestrian dead reckoning is a well-known approach for indoor navigation. There, the smartphone’s inertial sensors readings are used to determine the user position by utilizing empirical or bio-mechanical approaches and by direct integration. In this paper, we propose PDRNet, a deep-learning pedestrian dead reckoning framework, for user positioning. It includes a smartphone location recognition classification network followed by a change of heading and distance regression network. Experimental results using a publicly available dataset show that the proposed approach outperforms traditional approaches and other deep learning based ones. [ABSTRACT FROM AUTHOR]

This articlefocuses on the attitude estimation problem for a Mars landing that employs an inertial measurement unit and a velocimeter. At the beginning of the parachute descent phase, high dynamic oscillatory motion may degrade the gyroscope performance or saturate the gyroscope, thereby producing large attitude estimation errors and a high landing risk. To address this problem, an attitude estimation algorithm with nadir vector correction is proposed by combining data from the IMU and the velocimeter. First, the body-frame nadir vector is determined from the gravitational acceleration, which is estimated in conjunction with the body velocity aided by velocimeter measurements in the framework of the Kalman filter. The observability analysis shows that the nadir vector may be estimated as long as two velocimeter beams are available. Next, to mitigate the large attitude knowledge errors caused by high dynamic motion, a purely deterministic approach is used to combine the estimated nadir vector with the inertial navigation system propagated attitude such that the attitude knowledge error along the nadir vector direction can be reduced. Simulation results demonstrated that the proposed algorithms can cope with gyroscope performance degradation or gyroscope saturation, enabling the embedded autonomy of lander systems. [ABSTRACT FROM AUTHOR]

Accurate orientation estimation using low-cost inertial and magnetic sensors is important. However, since the magnetometer measurements affect pitch and roll estimates in addition to yaw determination, the resultant accuracy reduction under magnetic disturbances is adverse for special occasions where high-precision pitch and roll estimates are essential. This article presents a double-extended Kalman filter (DEKF) orientation estimator with unit quaternion as output, which decouples magnetometer effects on pitch and roll estimates by decomposing the orientation into tilt quaternion and heading quaternion. The tilt quaternion, including pitch and roll information, is estimated by the first EKF where accelerometer and gyroscope measurements are processed and the inaccurate heading information is discarded. The heading quaternion, solely describing the yaw orientation, is determined by the second EKF, which fuses the vertical component of angular rate measurements and the horizontal component of magnetic measurements. True quaternion is denoted by the multiplicative of tilt and heading quaternions. The separate property of DEKF limits the influences of magnetic disturbances on yaw estimation, making the pitch and roll angles immune to them. Experiment results show that the proposed method solves the coupled disturbance problem without losing accuracy. [ABSTRACT FROM AUTHOR]

Orientation estimation using inertial and magnetic sensors has permeated into various applications. However, robust roll and pitch estimates are still challenging for the orientation estimators when the sensors are exposed to magnetic disturbances. In this article, we proposed a decoupled orientation estimation approach (DOEA) to separate and achieve accurate roll and pitch estimates from the magnetic measurements. In the proposed DOEA, we formulate a reference vector, which is perpendicular to the gravitational vector and keeps constant size and direction, and derive its covariances to replace the magnetic field vector as the observation references. The sensor measurements of the two orthogonal homogeneous fields are further fused in the extended Kalman filter frame to correct the predicted orientation of the gyroscope integration. To validate the performance of the proposed DOEA, we compare it with three state-of-the-art approaches in four different experiments. In the magnetically disturbed environment without reliability validation of the sensors, experiment results show that the DOEA could provide robust and accurate roll and pitch measurements with 0.001- and 0.007-rad root-mean-square error (RMSE). The influence of the magnetic disturbances on the DOEA is controlled only on the yaw direction. In the wide-range static and dynamic mixed motion tracking of the robot, experiment results show that the proposed DOEA achieves 0.035-, 0.024-, and 0.018-rad RMSE of roll, pitch, and yaw estimates without singularities. Moreover, the proposed DOEA converges faster than the popular optimization filters and achieves computational efficiency 56% faster than the two-step orientation estimator, which is beneficial for realization on the embedded system. [ABSTRACT FROM AUTHOR]

Two novel robust nonlinear stochastic full pose (i.e., attitude and position) estimators on the Special Euclidean Group $\mathbb {SE}(3)$ are proposed using the available uncertain measurements. The resulting estimators utilize the basic structure of the deterministic pose estimators adopting it to the stochastic sense. The proposed estimators for six degrees of freedom (DOF) pose estimations consider the group velocity vectors to be contaminated with constant bias and Gaussian random noise, unlike nonlinear deterministic pose estimators which disregard the noise component in the estimator derivations. The proposed estimators ensure that the closed-loop error signals are semi-globally uniformly ultimately bounded in mean square. The efficiency and robustness of the proposed estimators are demonstrated by the numerical results which test the estimators against high levels of noise and bias associated with the group velocity and body-frame measurements and large initialization error. [ABSTRACT FROM AUTHOR]

This article addresses the problem of estimating the attitude and gyro bias using biased gyro and inertial vector measurements. Two nonlinear observers are presented. One of the two is the passive observer evolving on the semisphere of quaternion, which decouples the gyro measurements from the reconstructed attitude. The other is the explicit observer evolving on the SO(3), which uses directly body-frame measurements of known inertial vectors. The proposed observers achieve faster convergence for large attitude estimation errors than similar observers in literature, guarantee asymptotical stability for almost arbitrary initial conditions, achieve similar steady-state performance with lower computational cost than multiplicative extended Kalman filter, which is the workhorse of attitude estimation, and are suitable for implementation using a low-cost inertial measurement unit. Simulation and experimental results prove the fast convergence and steady-state performance of the proposed observers. [ABSTRACT FROM AUTHOR]

Magneto-Inertial Measurement Units (MIMUs) are a valid alternative tool to optical stereophotogrammetry in human motion analysis. The orientation of a MIMU may be estimated by using sensor fusion algorithms. Such algorithms require input parameters that are usually set using a trial-and-error (or grid-search) approach to find the optimal values. However, using trial-and-error requires a known reference orientation, a circumstance rarely occurring in real-life applications. In this article, we present a way to suboptimally set input parameters, by exploiting the assumption that two MIMUs rigidly connected are expected to show no orientation difference during motion. This approach was validated by applying it to the popular complementary filter by Madgwick et al. and tested on 18 experimental conditions including three commercial products, three angular rates, and two dimensionality motion conditions. Two main findings were observed: i) the selection of the optimal parameter value strongly depends on the specific experimental conditions considered, ii) in 15 out of 18 conditions the errors obtained using the proposed approach and the trial-and-error were coincident, while in the other cases the maximum discrepancy amounted to 2.5 deg and less than 1.5 deg on average. [ABSTRACT FROM AUTHOR]

In many industrial fields, cooling fan systems have been widely implemented in electronic equipment such as network hosts, product line junction boxes, manufacturing facilities, computer numerical control (CNC) machine systems, cooling units of array servers, and many other power systems. These systems usually require high computation power and release high heat. Once the cooling fan systems malfunction or the cooling efficiency degrades, it could result in lower system performances or even cause serious damage to the core systems. Thus, there is a growing interest in monitoring and detecting the cooling fan operation status. As a result, the status of the cooling fan systems needs to be monitored in real-time. In this article, dynamics modeling, parameter identification, and online fan speed monitoring of a cooling fan system are presented. First, the nonlinear model of the cooling fan system is derived from blade aerodynamics with a driving motor. Next, a discrete model is applied based on the bilinear transformation of the description of the dynamic behavior and is further used for the least-square (LS) parameter estimation. To suppress the measurement noise, a regulation filter (RF) is further presented to improve the parameter identification precision. In addition, a Levenberg–Marquardt (LM) optimization is further applied for parameter refinement. Simulation comparison studies are considered to validate the proposed method. Moreover, many experiments are conducted to verify the feasibility and reliability for different types of fans. Unlike common conventional monitoring methods, the proposed framework does not apply constant threshold or need any training stages. The alarm threshold is adjusted automatically according to the current operation status. Finally, an embedded measurement and monitoring instrument is developed for demonstrating the effectiveness of the proposed method. Experiments firmly verify the novelty of the model-reference-based online cooling fan monitoring techniques. [ABSTRACT FROM AUTHOR]

Attitude information obtained from low-cost magnetic, angular rate, and gravity (MARG) sensors is prone to be deteriorated by disturbances such as external acceleration and magnetic anomaly in operational environments. To address the problem, a robust attitude estimation algorithm using low-cost MARG sensors is proposed. An error state Kalman filter (ESKF) is used for data fusing, in which the attitude errors are interpreted as a small rotation vector, and the gyro bias variation is also estimated. The nominal state and the error state are propagated with unit quaternion and rotation vector, respectively. An attitude correction is followed which combines the nominal and error parts by quaternion multiplication. The robustness of the proposed algorithm is embodied in resisting accuracy degradation of the attitude estimation caused by both external acceleration and magnetic anomalies. While external accelerations and magnetic disturbances are detected online, the accuracy reduction in attitude estimation is prevented by adaptively adjusting the related measurement noise covariance matrix. Besides, a two-step measurement update strategy is designed to guarantee that the roll and pitch update is separated from the yaw update. Various rotation and land vehicle dynamic tests have been conducted to validate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article proposes two novel covariance-tuning methods to form a robust Kalman filter (RKF) algorithm for attitude (i.e., roll and pitch) estimation using the measurements of only an inertial measurement unit (IMU). KF-based and complementary filtering (CF)-based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF-based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is a difficult and time-consuming task. Specifically, the IMU-only attitude estimation filters are prone to the external accelerations unless their covariances are adapted to gain robustness. The proposed algorithms provide an adaptive method for tuning the measurement noise covariance such that they can accurately estimate the attitude in the two axes. The first method relies on a single tuning factor, whereas the second one tunes the covariance with different (multiple) factors for each measurement axis. The proposed methodologies are tested and compared with other existing filtering algorithms in the literature under different dynamical conditions and using real-world experimental datasets in order to validate their effectiveness. Results show that highly dynamic scenarios, especially the multiple tuning factor strategy, can increase the attitude estimation accuracy more than two-times compared to the competitive algorithms. [ABSTRACT FROM AUTHOR]

Deadlift is one of the most common weight training exercises that can help build waist muscles. However, many trainers with less experience may not receive the expected effect or even damage themselves. To exercise in a healthier way, a portable and quantifiable deadlift evaluation system is proposed in this article. The system employs a body sensor network (BSN), including the inertial and surface electromyography (sEMG) information, to reconstruct the human motion and segment the deadlift into certain phases to realize the detailed analysis. A two-step extended Kalman filter (EKF) that can reduce the magnetic interference is presented to estimate the attitude and the accuracy of the method is validated by the OptiTrack system. We also carry out the kinematic and sEMG analysis among trainers with different levels. The experiment results reveal that our system can significantly discriminate the technical differences for those trainers, which demonstrated that the system can provide valuable information for the deadlift training. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things (IoT), people’s demand for location-based services is increasingly urgent. Based on magnetometer, accelerometer, and rate gyro, i.e., MARG sensors, the position and attitude estimation methods such as complementary filter (CF), Kalman filter (KF), and their various modifications have been the research hot spot. However, the CF-based methods are empirical and lack robustness; the KF-based methods are memory-free observers, whose solution may diverge when the filter lacks uniform observability. In this article, a virtual-measurement-combined extended KF (VMC-EKF) method is proposed by fusing the carrier’s motion state with EKF method. Similar to the graph optimization, the proposed method can measure the key information in VMC phase, and thus remove the requirement of uniform observability. The number of virtual measurements can be estimated based on carrier’s motion state and its gradient, which determines the number of iterations of the prediction phase and the correction phase. In order to verify the performance of the proposed method, a series of numerical simulation experiments, turntable experiments, and foot-mounted experiments are carried out. The corresponding testing platform is set up based on MPU9250, which is a typical and low-cost motion tracking integrated circuit (IC) of MARG sensors. The raw data of MARG sensors can communicate with the host computer via wired or wireless communication, and then be imported into MATLAB for processing and analysis by the compared methods. The test results show that the proposed method can achieve fast convergence of attitude estimation and avoid the divergence of position estimation compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The main goal of this article is to fully explore the capability of the “No Motion No Integration” (NMNI) technique for the optimization of the Euler angles in orientation tracking. The gyroscope is the critical component in the inertial measurement unit for angle detection in Industry 4.0. An upgraded NMNI model is introduced to remove the drift of the gyroscope significantly for the advanced measurement approach for roll, pitch, and yaw. A model of threshold update is implemented into the NMNI algorithm to compensate for the increased offset of temperature. This preprocessing method is applied to the Madgwick filter and Mahony filter to acquire the optimal performance. The experiments were carried out by using a low-cost platform equipped with microelectromechanical system sensors. A pan-tilt unit with high accurate positioning was used to move the sensors and obtain a reference angle during both static and dynamic experiments. A substantial improvement was clearly demonstrated after the optimization process. The measurements of the Euler angles have minimized noise and tracks around the reference points properly. The results show strong competition from both fusion filters where the fused Mahony accomplishes more stable less variation in roll and pitch, but the fused Madgwick shows more precision in heading/yaw estimation. [ABSTRACT FROM AUTHOR]

Accurate and robust orientation estimation using magnetic and inertial measurement units (MIMUs) has been a challenge for many years in long-duration measurements of joint angles and pedestrian dead-reckoning systems and has limited several real-world applications of MIMUs. Thus, this research aimed at developing a full-state Robust Extended Kalman Filter (REKF) for accurate and robust orientation tracking with MIMUs, particularly during long-duration dynamic tasks. First, we structured a novel EKF by including the orientation quaternion, non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector. Next, the a posteriori error covariance matrix equation was modified to build a REKF. We compared the accuracy and robustness of our proposed REKF with four filters from the literature using optimal filter gains. We measured the thigh, shank, and foot orientation of nine participants while performing short- and long-duration tasks using MIMUs and a camera motion-capture system. REKF outperformed the filters from literature significantly (p < 0.05) in terms of accuracy and robustness for long-duration tasks. For example, for foot MIMU, the median RMSE of (roll, pitch, yaw) were (6.5, 5.5, 7.8) and (22.8, 23.9, 25) deg for REKF and the best filter from the literature, respectively. For short-duration trials, REKF achieved significantly (p < 0.05) better or similar performance compared to the literature. We concluded that including non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector, as well as using a robust filter structure, is required for accurate and robust orientation tracking, at least in long-duration tasks. [ABSTRACT FROM AUTHOR]

This paper focuses on the design and comparison of different deep neural networks for the real-time prediction of locomotor and transition intentions of one osseointegrated transfemoral amputee using only data from inertial measurement units. The deep neural networks are based on convolutional neural networks, recurrent neural networks, and convolutional recurrent neural networks. The architectures’ input are features in both the time domain and the time-frequency domain, which are derived from either one inertial measurement unit (placed above the prosthetic knee) or two inertial measurement units (placed above and below the prosthetic knee). The prediction of eight different locomotion modes (i.e., sitting, standing, level ground walking, stair ascent and descent, ramp ascent and descent, walking on uneven terrain) and the twenty-four transitions among them is investigated. The study shows that a recurrent neural network, realized with four layers of gated recurrent unit networks, achieves (with a 5-fold cross-validation) a mean F1 score of 84.78% and 86.50% using one inertial measurement unit, and 93.06% and 89.99% using two inertial measurement units, with or without sitting, respectively. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has been confirmed as an effective method to develop inertial measurement unit (IMU) based pedestrian inertial navigation system (INS). Nevertheless, collecting data for training the DL models is always a challenge. Conventional motion capture systems are expensive and they can be applicable within a restricted range. The real time kinematic-global positioning system (RTK-GPS) has concerns of low data collection rate and outdoor usage limitations. Hence, this paper presents a feasible and easily deployable hand-push odometer platform (HPOP) that was modified from a conventional wheeled walker. The 30Hz HPOP speed information is arranged by combining the dual foot-mounted IMUs’ data for the training of long short-term memory (LSTM) models to develop a pedestrian walking speed estimator, where the training dataset contains 858,751 data items. Moreover, the Fick angle is further utilized with the estimated walking speed to form a pedestrian INS. In a 2m*2.6m rectangle path, the absolute path tracking error was 0.1024m; the RMSE of walking speed was 0.04768m/s; path walking distance error was 0.089m. In a 52.46m*8.16m basement corridor area, a 1.06m homing positioning error was investigated in a 136.6m round trip corridor path experiment. [ABSTRACT FROM AUTHOR]

Two novel nonlinear pose (i.e., attitude and position) filters developed directly on the Special Euclidean Group $\mathbb {SE}(3)$ able to guarantee prescribed characteristics of transient and steady-state performance are proposed. The position error and normalized Euclidean distance of attitude error are trapped to arbitrarily start within a given large set and converge systematically and asymptotically to the origin from almost any initial condition. The transient error is guaranteed not to exceed a prescribed value while the steady-state error is bounded by a predefined small value. The first pose filter operates based on a set of vectorial measurements coupled with a group of velocity vectors and requires preliminary pose reconstruction. The second filter, on the contrary, is able to perform its function using a set of vectorial measurements and a group of velocity vectors directly. Both proposed filters provide reasonable pose estimates with superior convergence properties while being able to use measurements obtained from low-cost inertial measurement, landmark measurement, and velocity measurement units. The simulation results demonstrate the effectiveness and robustness of the proposed filters considering large error in initialization and high level of uncertainties in velocity vectors as well as in the set of vector measurements. [ABSTRACT FROM AUTHOR]

Accurate attitude for wearable devices and smartphones is needed for many applications. The major challenge is to cope with the acceleration resulting from the user or smartphone dynamics. To that end, a two-stage adaptive complementary filter for attitude estimation is proposed. Upon identifying the smartphone location on the user using a deep learning approach, the accelerometers weights in each axis are adjusted according to an optimized gain map. To evaluate the benefits of the proposed approach it is compared to commonly used algorithms both in simulation and experiments. [ABSTRACT FROM AUTHOR]

In many pose estimation problems, rotation matrices can not always be estimated subject to all nonlinear rigidity constraints. Therefore, engineers tend to obtain the nearest rotation matrix of an improper one, which is called the rotation orthonormalization problem. In this brief, we show a circuit synthesis of such problem by using only simple algebraic components. We give theoretical convergence analysis of the designed circuit. By using the proposed circuit, rotation orthonormalization can be easily performed without the need of previous sophisticated processes like singular value decomposition (SVD) and eigen-decomposition (EIG). Experiments of the developed method’s characteristics are conducted. The circuitized scheme has also been implemented on an FPGA platform. [ABSTRACT FROM AUTHOR]

This paper proposes a novel quaternion-based attitude estimation method for land-vehicle applications by fusing the low-cost magnetic, angular rate, and gravity (MARG) sensors and global navigation satellite systems measured velocity (GNSS-V). A structure of three-layer cascaded indirect Kalman filter (IKF) is devised to minimize the interactions of heterogeneous error sources on attitude quaternion solutions. It sequentially fuses the Earth's gravity vector, magnetic vector, and GNSS-V vector pairs with quaternion state constraints from Euler angles in each local filter. To further ensure the robustness of quaternion-based attitude solution, the external acceleration term is adaptively compensated within a sliding window of GNSS-V information. Meanwhile, independent and cross check procedures for observation vectors are also established for excluding individual sensor fault by taking land-vehicle dynamics into account. It is of significant importance that, in this contribution, the GNSS measured velocity is fully utilized in all potential aspects of aiding attitude determination with MARG sensors. Real-world vehicular experiments are carried out to evaluate the performance of our proposed method. The presented results verify the validity and efficiency thus the developed method may be promising for implemention in land-vehicle applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]