Augmented Unscented Kalman Filter Research Articles

An Iterative Augmented Unscented Kalman Particle Filter for Simultaneous State-Parameter-Input Estimation for Structural Systems Subjected to Gamma-Distribution Noise

Simultaneous estimations of the states, parameters and unknown inputs of nonlinear structural systems subjected to non-Gaussian noise are essential, because the inputs are not always available and the system noise is not always Gaussian. However, filters developed for this task require the inter-story drift measurements to be “fused” with the acceleration measurements to avoid the “drift phenomenon”. Because inter-story drifts may be inconvenient to obtain in practice, it is more desirable to use acceleration measurements only. To this end, this study develops a novel iterative augmented unscented Kalman particle filter (IAUKPF) for the simultaneous state-parameter-input estimation for systems subjected to non-Gaussian noise. The iterative strategy proposed in this study effectively corrects the “drift phenomenon” when only acceleration measurements are incorporated, so that the accuracies of the estimated unknown inputs are promoted. The importance density of the particle filter at each time step is provided by the augmented unscented Kalman filter so that the filter takes the best known information into consideration. Numerical examples of nonlinear structural systems affected by non-Gaussian noise are adopted to examine the effectiveness of the proposed IAUKPF.

An iterative augmented unscented Kalman filter for simultaneous state-parameter-input estimation for systems with/without direct feedthrough

A few unscented Kalman filters (UKFs) have been developed for simultaneous state-parameter-input estimation, however, these UKFs often have at least one of these limitations: requiring ad hoc procedures; requiring displacement measurements, which may be inconvenient to obtain, to fuse with acceleration measurements to avoid the “drift phenomenon” in the estimated displacement responses and unknown inputs; requiring a (full-rank) feedthrough matrix; and undesirable computational efficiency. To overcome these limitations, a novel iterative augmented unscented Kalman filter (IAUKF) for simultaneous state-parameter-input estimation for systems either with or without direct feedthrough is developed and examined in this study. The IAUKF closely follows the Bayes’ theorem with no ad hoc procedure, featuring theoretical simplicity. An iterative strategy is introduced to improve the accuracy of the unknown input estimation at each time step, so that the “drift phenomenon” encountered by the existing augmented Kalman filter is avoided. Further, the IAUKF requires no prior models for the time histories of the unknown inputs and applies to systems with/without direct feedthrough. With realistic assumptions, the steps for computationally efficient implementations of the IAUKF are proposed. Numerical examples of nonlinear structural systems with/without direct feedthrough of the unknown inputs are adopted to examine the effectiveness and the computational efficiency of the IAUKF.

A Horizontal Tracking Algorithm Suitable for Airborne Collision Avoidance System

The horizontal tracker is essential for the reliable operation of the Airborne Collision Avoidance System (ACAS). In ACAS target tracking, for the non-linear state estimation problem of using sensor measurement values to track in Cartesian coordinates, this paper proposes a horizontal tracking algorithm based on the Augmented Unscented Kalman Filter (AUKF) to achieve the horizontal of the target. Accurate tracking of direction. Firstly, the tracking model of the relative horizontal state of the target aircraft is established, and then the data is processed by AUKF. In order to verify the effectiveness of the horizontal tracking algorithm, the computer simulation method is used to simulate the track of the local aircraft and the intruder in the horizontal direction, and noise is added to the measured values. The traditional Kalman Filter In Polar Coordinates (KFPC) and AUKF algorithm are used to filter the ACAS horizontal tracking. The simulation results show that AUKF can achieve more accurate target tracking.

A Novel Polar Rapid Transfer Alignment for Shipborne SINS Under Arbitrary Misalignments

The traditional polar rapid transfer alignment (RTA) are generally modeled under small angle misalignment assumption. Those linearized and nonlinear polar RTA error models presented, so far, are not capable of accurately representing the nonlinear properties of the system where the all-orientation large attitude errors happen between the master and slave strap-down inertial navigation systems (SINS). It cannot satisfy the accuracy for the SINS initialization and has a singularity in computation. In this article, the misalignment quaternion in the grid frame was selected as the state and observation. An innovative polar RTA algorithm using the quaternion matching and the augmented unscented Kalman filter (UKF) was then proposed to estimate all-orientation large attitude misalignments in polar regions. Furthermore, the associated error propagation equations were redesigned in the grid frame and with compensations for the lever-arm effect. The augmented UKF model was further adjusted to apply. Simulation and semi-physical experiment results demonstrated that the performance of the innovative polar RTA algorithm proposed here outperformed those via traditional polar RTA techniques, especially with 3-axis large-angle misalignments, and was even robust with the disturbance of lever-arm effect aggravated by the harsh polar environment.

Two-Step Adaptive Augmented Unscented Kalman Filter for Roll Angles of Spinning Missiles Based on Magnetometer Measurements

It is critical to measure the roll angle of a spinning missile quickly and accurately. Magnetometers are commonly used to implement these measurements. At present, the estimation of roll angle parameters is usually performed with the unscented Kalman filter algorithm. In this paper, the two-step adaptive augmented unscented Kalman filter algorithm is proposed to calibrate the biaxial magnetometer and circuit measurements quickly, which allows accurate estimates of the missile roll angle. Unlike the existing algorithms, the state vector of the algorithm is based on the missile roll angle parameters and the error factors caused by the magnetometer and the measurement circuit errors. Next, a two-step fast fitting algorithm is used to fit the initial value. After satisfying the stop rule, the state vector of the filter is configured to estimate the roll angle parameters and the calibration parameters. This method is evaluated by running numerous simulations. In the experiment, the algorithm completes the calibration of the magnetometer and the measurement circuit 1 s after the missile launches, with a sampling rate of 1 ms and an output roll attitude angle with a 0.0015 rad precision. The conventional unscented Kalman filter algorithm requires more time to achieve such a high accuracy. The simulation results demonstrate that the proposed two-step augmented unscented Kalman filter outperforms the conventional unscented Kalman filter in its estimation accuracy and convergence characteristics.

A solution to SLAM problems by simultaneous estimation of kinematic parameters including sensor mounting offset with an augmented UKF

Accurate localization is required for autonomous robots to navigate in cluttered environments safely. Therefore, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), which incorporate probabilistic concepts as localization methods, have been researched up to now. It should be noted, however, that the errors of kinematic parameters such as wheel diameter, tread, and mounting sensor offset are not enough considered in conventional works. We propose an Augmented UKF-SLAM (AUKF-SLAM), which is an extension of the UKF-SLAM and can estimate the kinematic parameters including a sensor mounting offset together with the localization and mapping. The UKF-SLAM and the AUKF-SLAM are compared through some simulations to show that the proposed AUKF-SLAM is more accurate than the UKF-SLAM. Furthermore, localization experiments with only odometry are conducted using a real robot. The experimental results show to demonstrate that the localization using kinematic parameters estimated by the AUKF-SLAM is more accurate than that using values measured by hand in advance. Through some experimental verifications in an elevator hall, cluttered rooms, and a long distance corridor, it is confirmed that the proposed AUKF-SLAM which simultaneously estimates the effective kinematic parameters largely contributes to the total accuracy improvement of SLAM.

Robust predictive augmented unscented Kalman filter

This paper presents a new Unscented Kalman Filtering (UKF) method by using robust model prediction. This method incorporates system driving noise in system state through augmentation of state space dimension to expand the input of system state information. The system model error is constructed by model prediction, and is then used to rectify the UKF process to obtain the estimate of the real system state. The proposed method endows the robustness to the traditional UKF, thus overcoming the limitation that the traditional UKF is sensitive to system model error. Experimental results show that the convergence rate and accuracy of the proposed filtering method is superior to the Extended Kalman Filtering and traditional UKF.

1A2-H08 Augmented UKFを用いた運動学パラメータの同時推定(移動ロボットの自己位置推定と地図構築(1))

Accurate localization is required for autonomous robots to navigate in cluttered environments safely. Therefore, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), which incorporate probabilistic concepts as localization methods, have been researched up to now. It should be noted, however, that the errors of kinematic parameters such as wheel diameter, tread, and mounting sensor position are not considered in the previous works. The present research proposes an Augmented UKF (AUKF), which is an extension of the UKF and can estimate the kinematic parameters together with the localization. The UKF and the AUKF are compared through some simulations to show that the proposed AUKF is much more accurate than the UKF. Additionally, localization experiments with only odometry are conducted using a real robot. It is shown from the experimental results that the localization using kinematic parameters estimated by the AUKF is more accurate than that using values measured by hand in advance.

The Unscented Kalman Filter Estimators for High Dynamic Doppler Shift Environments

The Extend Kalman Filter (EKF) is widely used in the tracking of high dynamic Doppler shift trajectories, but it has some flows when it is used to estimate the state of nonlinear systems. In this paper, we apply the Unscented Transformation (UT) based Unscented Kalman Filter (UKF) to the state estimation in the high dynamic Doppler environments. Two versions of the UKF estimators, augmented UKF estimator and nonaugemented UKF estimator are designed. To compare the performance of them, they are applied to tracking a common high dynamic trajectory, and simulation results declare that given different conditions, the performance of the estimators will be different.

Implicit Fractional Model Order Estimation Using Interacting Multiple Model Kalman Filters

AbstractThe paper deals with implicit and explicit approaches for fractional nonlinear model order estimation using a benchmark model relating applied angular rate and neuron's firing intensity within the vestibular system. The implicit approach is based on an interacting multiple models scheme, where several extended Kalman filters with fixed fractional order nonlinear models are running in parallel. An alternative method based on augmented Unscented Kalman filter is proposed where, where the fractional order of the model is estimated explicitly within the filter state. The performance of the estimators is compared with respect to parameters of estimators and fractional derivative approximation.