Filter Position Research Articles

Incorporating delayed star tracker measurements in Gokturk-2 satellite attitude filter

Star trackers (STRs) are widely used for fine attitude estimation. Due to the processing time for tasks such as star identification, tracking, and attitude solution, a STR can provide the attitude quaternion measurements with a time delay, which may be as high as a few 100 ms. Different methods are available for incorporating such delayed measurements in the attitude filter, such as the most straightforward method of propagating the measurements to the actual filter time. This study evaluates different methods to incorporate the delayed STR measurements in the Gokturk-2 satellite attitude filter, which is structured as a Multiplicative Extended Kalman Filter (MEKF). Methods are compared in terms of accuracy, computational load, and impact on the filter's optimality. They are evaluated using the simulated data for an Earth observation spacecraft in Sun-synchronous orbit and the actual STR data for the operational Gokturk-2 satellite.

SE(3)-Constrained Extended Kalman Filtering for Rigid Body Pose Estimation

In this article, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> -constrained extended Kalman filter is proposed in continuous time as well as in a more practical continuous-discrete framework. The filter allows for the state estimation of the 6-DOF rigid body motion while accounting for measurement error statistics and using the rotation matrix instead of quaternions or other attitude parameterizations. The proposed filter differs from the recently proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SO(3)$</tex-math></inline-formula> -constrained attitude filter in that only a subset of the configuration states are constrained in the present filter. Its effectiveness is demonstrated in a numerical example in which its performance is compared with that of an existing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> estimator from the literature and a Monte Carlo simulation is carried out to provide credence to the accuracy of the proposed filter.

Comparison of TRIAD+EKF and TRIAD+UKF Algorithms for Nanosatellite Attitude Estimation

Two most commonly used sensors on nanosatellites are magnetometer and sun sensor. In this paper, magnetometer and sun sensor measurements are combined gyro measurements to produce enhanced attitude estimation. Tri-Axial Attitude Determination (TRIAD) algorithm is used with Kalman filter to form a complete attitude filter. Sun sensor and magnetometer measurements are selected as inputs to TRIAD algorithm and output is fed to Kalman filter as a measurement. Two different Kalman filters, extended and unscented, are used with TRIAD algorithm. A comparison is given between performances of both Kalman filter.

Bayesian Filtering Position Estimation Algorithm Based on Bounded Grid

Bayesian Filtering Position Estimation Algorithm Based on Bounded Grid

Neural-Adaptive Stochastic Attitude Filter on SO(3)

This letter proposes a novel stochastic nonlinear neural-adaptive-based filter on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SO(3)$ </tex-math></inline-formula> for the attitude estimation problem. The proposed filter produces good results given measurements extracted from low-cost sensing units (e.g., IMU or MARG sensor modules). The filter is guaranteed to be almost semi-globally uniformly ultimately bounded in the mean square. In addition to Lie Group formulation, quaternion representation of the proposed filter is provided. The effectiveness of the proposed neural-adaptive filter is tested and evaluated in its discrete form under the conditions of large initialization error and high measurement uncertainties.

Estimation of pollutant sources in multi-zone buildings through different deconvolution algorithms.

Effective identification of pollution sources is particularly important for indoor air quality. Accurate estimation of source strength is the basis for source effective identification. This paper proposes an optimization method for the deconvolution process in the source strength inverse calculation. In the scheme, the concept of time resolution was defined, and combined with different filtering positions and filtering algorithms. The measures to reduce effects of measurement noise were quantitatively analyzed. Additionally, the performances of nine deconvolution inverse algorithms under experimental and simulated conditions were evaluated and scored. The hybrid algorithms were proposed and compared with single algorithms including Tikhonov regularization and iterative methods. Results showed that for the filtering position and algorithm, Butterworth filtering performed better, and different filtering positions had little effect on the inverse calculation. For the calculation time step, the optimal Tr (time resolution) was 0.667% and 1.33% in the simulation and experiment, respectively. The hybrid algorithms were found to not perform better than the single algorithms, and the SART (simultaneous algebraic reconstruction technique) algorithm from CAT (computer assisted tomography) yielded better performances in the accuracy and stability of source strength identification. The relative errors of the inverse calculation for source strength were typically below 25% using the optimization scheme.

Feasibility study of hand-carried ultrasound-guided retrievable inferior vena cava filter placement

BackgroundWith the development of ultrasonic diagnostic techniques in recent years, ultrasound-guided placement of inferior vena cava (IVC) filters has been widely used in clinics, and satisfactory results have been achieved. Our study aims to observe the accuracy of hand-carried ultrasound-guided retrievable vena cava filter placement, evaluate the feasibility and safety of this new method, and provide a scientific and effective interventional method and clinical data to prevent acute pulmonary embolism (PE) after battle injury of limbs.MethodsTwo hundred patients with post-traumatic thrombosis of the extremities were enrolled. The renal vein was located under the guidance of hand-carried ultrasound. The retrievable filter was fixed 1–2 cm below the opening of the renal vein. The self-expanding filter was used after the filter’s position was confirmed by injecting the contrast agent under the digital subtraction angiography (DSA) fluoroscopy.ResultsAll the 200 patients underwent the operation successfully. The position of the hand-carried ultrasound localizer was consistent with the DSA localizer. All the filters were expanded smoothly. No complications related to the operation occurred.ConclusionsThe study concluded that the hand-carried ultrasound-guided retrievable vena cava filter placement has a high success rate and can prevent acute PE after limbs’ battle injury.

A novel adaptive Kalman filter for Euler-angle-based MEMS IMU/magnetometer attitude estimation

This paper introduces a novel Euler-angle-based adaptive attitude filter for micro-electro-mechanical system inertial measurement unit (IMU)/magnetometer fusion. An intelligent coordinate switch algorithm is proposed to overcome the singularity problem that exists in the Euler-angle-based attitude calculation approach. This method rotates the physical IMU body frame and forms a quasi-computational coordinate to perform the attitude and filter update. In addition, a novel noise estimation theorem based on redundant measurement systems is introduced, proofed and employed to adaptively tune the measurement covariance matrix R in the filter. This algorithm uses gyroscope short-term accuracy to evaluate the attitude performance derived from different sensors and optimally assigns weight in the filter update. Simulated and practical experiments are carried out to test the validation of the proposed adaptive attitude filter. The results achieved demonstrate that this approach can provide a promising orientation solution.

Robust Attitude Estimation Using IMU-Only Measurements

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.

Feedback Linearization with Zero Dynamics Stabilization for Quadrotor Control

A control solution for an Unmanned Aerial Vehicle encapsulating a nonlinear inner-loop based on the application of feedback linearization to the attitude and altitude dynamics is proposed in this paper. Linear quadratic controllers with integrative action are implemented not only to the resulting inner-loop chain of integrators, but also to the outer-loop, that controls the horizontal movement and, consequently, stabilizes the zero-dynamics. The required full state-feedback relies on measurements from motion sensors and on-flight estimates provided by Kalman filters and a nonlinear attitude filter. In simulation, the capacity of trajectory tracking and withstanding significant deviations of the mass and inertia values of the proposed control structure are evaluated while considering saturation and noisy measurements. The simulations results were experimentally validated using a commercially available drone. The modeling and control system architecture are validated by the experimental results. Additionally, a comparison with the results achieved with a linear control solution developed in a previous work is drawn.

TLIO: Tight Learned Inertial Odometry

In this work we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. Strap-down IMU measurements provide relative state estimates based on IMU kinematic motion model. However the integration of measurements is sensitive to sensor bias and noise, causing significant drift within seconds. Recent research by Yan et al. (RoNIN) and Chen et al. (IONet) showed the capability of using trained neural networks to obtain accurate 2D displacement estimates from segments of IMU data and obtained good position estimates from concatenating them. This paper demonstrates a network that regresses 3D displacement estimates and its uncertainty, giving us the ability to tightly fuse the relative state measurement into a stochastic cloning EKF to solve for pose, velocity and sensor biases. We show that our network, trained with pedestrian data from a headset, can produce statistically consistent measurement and uncertainty to be used as the update step in the filter, and the tightly-coupled system outperforms velocity integration approaches in position estimates, and AHRS attitude filter in orientation estimates.

External Disturbances Rejection for Vector Field Sensors in Attitude and Heading Reference Systems.

The attitude and heading reference system (AHRS), which consists of tri-axial magnetometer, accelerometer, and gyroscope, has been widely adopted for three-dimensional attitude determination in recent years. It provides an economical means of passive navigation that only relies on gravity and geomagnetic fields. However, despite the advantages of small size, low cost, and low power, the magnetometer and accelerometer are susceptible to external disturbances, such as the magnetic interference from nearby ferromagnetic objects and current-carrying conductors, as well as the motional acceleration of the carrier. To eliminate such disturbances, a vector-based parallel structure is introduced for the attitude filter design, which can avoid the mutual interference between gravity and geomagnetic vectors. Meanwhile, an approach to estimate and compensate the external disturbances in real time for magnetometer and accelerometer is also presented. Compared with existing designs, the proposed filter architecture and external disturbance rejection algorithm can feasibly and effectively cooperate with mainstream data fusion techniques, including complementary filter and Kalman filter. According to experiment results, in the case that large and persistent external disturbances exist, the proposed method can improve the accuracy and robustness of attitude estimation, and it outperforms the existing methods such as switching filter and adaptive filter. Furthermore, through the experiments, the critical role of fading factor in handling the external disturbance is revealed.

RETRACTED ARTICLE: FPGA based peripheral myopathy monitoring using MFCV at dynamic contractions

This article describes the design of a wearable, remote inserted framework for the Peripheral Myopathy (PM) estimation in customary dynamic movements. This unique circumstance, the electromyographic investigation can give data about the muscle and nerve condition by evaluating the connected MFCV. The framework works with synchronized and digitized information tests from four electromyographic channels, which are situated on both hands of the individual test, misusing the rules given by an inserted positional filtering calculation. This work displays a novel approach for the assessment of MFCV that relies upon the excellent anode relative estimation standard. The system uses dynamic bitstream change of Electromyographic signs and low computational retort for utilization of bit stream cross-correlation. The structure totally chips away at Cyclone-V altera field programmable gate array. The test outcomes on five subjects (Human Beings) show the limit of the proposed procedure for planning the physiological muscle fibre conduction velocity values, as nitty–gritty in a therapeutic composition. In explicit, differentiating the remedial characteristics, obtained in controlled conditions, with the system evacuated muscle fibre conduction velocity, the proportionate preliminary conditions: preeminent bungle is, overall, 0.3 m/s. The framework restores a likelihood of invalid constant measures underneath of 2% (assuming the worst possible scenario).

Guaranteed Performance Nonlinear Observer for Simultaneous Localization and Mapping

A geometric nonlinear observer algorithm for Simultaneous Localization and Mapping (SLAM) developed on the Lie group of \mathbb{SLAM}_{n}\left(3\right) is proposed. The presented novel solution estimates the vehicle's pose (i.e. attitude and position) with respect to landmarks simultaneously positioning the reference features in the global frame. The proposed estimator on manifold is characterized by predefined measures of transient and steady-state performance. Dynamically reducing boundaries guide the error function of the system to reduce asymptotically to the origin from its starting position within a large given set. The proposed observer has the ability to use the available velocity and feature measurements directly. Also, it compensates for unknown constant bias attached to velocity measurements. Unit-qauternion of the proposed observer is presented. Numerical results reveal effectiveness of the proposed observer. Keywords: Nonlinear filter algorithm, Nonlinear observer for Simultaneous Localization and Mapping, Nonlinear estimator, nonlinear SLAM observer on manifold, nonlinear SLAM filter on matrix Lie Group, observer design, asymptotic stability, systematic convergence, Prescribed performance function, pose estimation, attitude filter, position filter, feature filter, landmark filter, gradient based SLAM observer, gradient based observer for SLAM, adaptive estimate, SLAM observer, observer SLAM framework, equivariant observer, inertial vision unit, visual, SLAM filter, SE(3), SO(3).

Application of Adaptive Cubature Kalman Filter to In-Pipe Survey System for 3D Small-Diameter Pipeline Mapping

A modified cubature Kalman filter (CKF) algorithm is proposed for an in-pipe survey system. This survey system can provide accurate three-dimensional (3D) location information for underground pipelines without external auxiliary location measurements. To move through a small-diameter pipe, the large size of the tactical grade inertial sensor is replaced with a low-cost micro-electromechanical system (MEMS) inertial measurement unit (IMU). Because the decrease in sensor accuracy increases the positioning error, it is necessary to improve the original filtering algorithm. The following improvements were made in relation to previous studies: geomagnetic and gravity sensors were introduced to obtain the observed vectors of the filter. The CKF was used to solve the nonlinearity of the filter's attitude errors. Furthermore, the CKF process noise matrix can be adaptively adjusted using the raw gyroscope measurements (ACKF) to maintain filter stability. Finally, the experimental results for the extended Kalman filter (EKF), unscented Kalman filter (UKF), CKF, and ACKF were compared to verify the effectiveness of ACKF.

Attitude determination using gyros and vector measurements aided with adaptive kinematics modeling

High-precision attitude estimation in real time plays a vital role in spacecraft flight. In this work, the attitude filtering using gyros and vector measurements is studied in the framework of the traditional multiplicative extended Kalman filtering (MEKF). An adaptive online kinematic modeling is proposed to aid the filtering, resulting in the so called adaptive kinematics-aided Kalman filtering (AKKF). Meanwhile, the state estimate is reset to be zeros and the covariance resetting is implemented accordingly at each step. Due to time-varying features of the attitude motion, a predetermined statistical model for the employed kinematic model cannot generally reflect the reality. So, the best invariant quadratic unbiased estimates (BIQUE) is adopted to adaptively tune the variance components in the employed kinematic model, to better model the realistic kinematic characteristics and hence to further improve the accuracy. Numerical simulation study indicates the reliability and accuracy of the proposed AKKF method.

Position Information-Based NOMA for Downlink and Uplink Transmission in Mobile Scenarios

In this article, a non-orthogonal multiple access (NOMA) system with partial channel state information (CSI) for downlink and uplink transmission in mobile scenarios is considered, i.e., users are deployed randomly and will move casually around the base station (BS). In this case, the channel gain of each user varies over time, which has an influence on the performance of conventional NOMA. An analytical framework is developed to evaluate the impact of position estimation deviation in terms of decoding order error probability, average sum rate and outage probability. Based on the framework, dynamic power allocation (DPA) for downlink NOMA and dynamic power control (DPC) for uplink NOMA are put forward to optimize the outage performance with user distance information. It has been shown that the performance of NOMA relies on accurate user position information. To this end, two algorithms based on position filtering are proposed to improve the accuracy of user position. Monte Carlo simulation is presented to demonstrate the improvement of spectrum efficiency and outage performance. Simulation results verify the accuracy of the proposed analytical framework.

Nonlinear Stochastic Attitude Filters on the Special Orthogonal Group 3: Ito and Stratonovich

Two nonlinear stochastic complimentary filters are developed on SO(3). They guarantee that errors in the Rodriguez vector and estimates are semi-globally uniformly ultimately bounded in mean square, and they converge to a small neighborhood of the origin. Simulation results are presented to illustrate the effectiveness of the proposed filters considering high level of uncertainties in angular velocity as well as body-frame vector measurements. Keywords: Attitude estimate, Attitude estimator, Attitude observer, Attitude filter, Nonlinear stochastic filter, stochastic differential equations, Brownian motion process, Ito, Stratonovich, Wong Zakai, Rodriguez vector, unit-quaternion, special orthogonal group 3, Euclidean, Euler angles, Angle-axis, Mapping, Parameterization, Representation, Robust, Invariant, Kalman Filter, Extended Kalman Filter, Multiplicative Extended Kalman Filter, Unscented Kalman Filter, Particle Filter, KF, EKF, MEKF, IEKF, first, second, Partial derivative, operator, probability, small, error, dynamics, kinematics, equilibrium, asymptotic, covariance, mean square, expected value, zero, unknown, time-varying, global, semi-global, stable, stability, uncertain, white noise, Gaussian noise, colored noise, bias, vectorial, vector measurement, angular velocity, singular value decomposition, bounded, rotational matrix, identity, deterministic, orientation, body frame, comparison, inertial frame, rigid body, three dimensional, 3D, space, Attitude Control, Lie algebra, Lie group, projection, Gyroscope, Inertial measurement units, micro electromechanical systems, sensor, IMUs, MEMS, Roll, Pitch, Yaw, UAVs, QUAV, SVD, Fixed, Moving, Vehicles, Robot, Robotic System, Spacecraft, submarine, Underwater vehicle, Problem, advantage, integral, integration, passive complementary filter, Disadvantage, autonomous, Review, Overview, Survey, comparative study, pose, SDEs, SE(3), SO(3).

The Clinical Rationale for the Sentry Bioconvertible Inferior Vena Cava Filter for the Prevention of Pulmonary Embolism.

The Sentry inferior vena cava (IVC) filter is designed to provide temporary protection against pulmonary embolism (PE) during transient high-risk periods and then to bioconvert after 60 days after implantation. At the time of bioconversion, the device's nitinol arms retract from the filtering position into the caval wall. Subsequently, the stable stent-like nitinol frame is endothelialized. The Sentry bioconvertible IVC filter has been evaluated in a multicenter investigational-device-exemption pivotal trial (NCT01975090) of 129 patients with documented deep vein thrombosis (DVT) or PE, or at temporary risk of developing DVT or PE, and with contraindications to anticoagulation. Successful filter conversion was observed in 95.7% of patients at 6 months (110/115) and 96.4% at 12 months (106/110). Through 12 months, there were no cases of symptomatic PE. The rationale for development of the Sentry bioconvertible device includes the following considerations: (1) the period of highest risk of PE for the vast majority of patients occurs within the first 60 days after an index event, with most of the PEs occurring in the first 30 days; (2) the design of retrievable IVC filters to support their removal after a transitory high-PE-risk period has, in practice, been associated with insecure filter dynamics and time-dependent complications including tilting, fracture, embolization, migration, and IVC perforation; (3) most retrievable IVC filters are placed for temporary protection, but for a variety of reasons they are not removed in any more than half of implanted patients, and when removal is attempted, the procedure is not always successful even with advanced techniques; and (4) analysis of Medicare hospital data suggests that payment for the retrieval procedure does not routinely compensate for expense. The Sentry device is not intended for removal after bioconversion. In initial clinical use, complications have been limited. Long-term results for the Sentry bioconvertible IVC filter are anticipated soon.

Nontraditional Attitude Filtering with Simultaneous Process and Measurement Covariance Adaptation

AbstractThis study discusses simultaneous adaptation of the process and measurement noise covariance matrixes for a nontraditional attitude filtering algorithm. The nontraditional attitude filterin...