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The application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non‐linear filter problems, when prediction equations are continuous‐time and the update equations are discrete‐time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete‐time non‐linear filter problems, when prediction equations and update equations are discrete‐time. In order to assess the performance of the filters, the authors consider the non‐linear dynamics for a re‐entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re‐entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re‐entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins‐Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter. [ABSTRACT FROM AUTHOR]

Adaptive Kalman filtering method is based on generating a separable Variational model for estimating joint posterior distribution of states in dynamical system and noise parameters on each time step separately. In this article we present a Gaussian approximation based framework for optimal smoothing of non-linear stochastic state space models, and also time-varying noisy measurements of the system are obtained at discrete instances of time. It is also shown how the method can be applied to a class of models. The result is a recursive algorithm, where on each step the state is estimated with Kalman filter and the sufficient statistics of the noise variances are estimated. We also numerically compare accuracies and error performance of the algorithm with different simulated data. [ABSTRACT FROM AUTHOR]

In this study, full adaptive Kalman filters are designed for continuous-time nonlinear fractional-order systems (FOSs) containing unknown parameters and fractional-orders. First, the estimated FOS is discretized using the Grünwald–Letnikov difference method to transform the fractional-order differential equation into a difference equation. Then, in terms of the nonlinear function contained in the investigated system, the Taylor expansion formula is adopted to linearize the discretized equation. Based on the method of augmented vector, an augmented state equation is established by state equations, unknown parameter equations, and fractional-order equation to achieve the state estimation. Besides, the sigmoid function is brought to ensure that the estimation of the fractional-order is performed in a suitable range. Because the covariance matrices of noises are difficult to be measured in physical systems, we also concern the problems on the state estimation, parameter estimation, and fractional-order estimation under the cases that the covariance matrix of process noise is unknown or the covariance matrix of measurement noise is unknown. Considering that the initial value can produce an error of state estimation and parameter identification for the FOS defined under the Caputo sense, the augmented vector method is used to achieve the initial value compensation. Finally, the effectiveness of the proposed algorithms is validated by four examples. [ABSTRACT FROM AUTHOR]

Abstract This study considers an asynchronous multirate data integration problem in the linear state space model with unknown and time-varying statistical parameters of the measurement noises. To improve performance of the multirate adaptive Kalman filter algorithm, a multi-sensor adaptive Kalman filtering algorithm based on variational Bayesian approximations has been developed in an asynchronous multirate multi-sensor integrated navigation system. The proposed filtering algorithm estimates measurement noise variances of the sensors adaptively and also it is robust to anomalous measurements of sensors and however, multirate adaptive Kalman filter is required to use an appropriate algorithm for outlier rejection to achieve a reliable and optimal estimation of position, velocity, and orientation. A navigation system composed of a strapdown inertial navigation system along with Doppler velocity log, inclinometer and depth meter with different sampling rates is designed to evaluate performance of multirate error state Kalman filter (MESKF) and multirate adaptive error state Kalman filter (MAESKF) algorithms and the proposed algorithm. Results of two experimental tests show that the average relative root mean square error (RMSE) of the position estimated by the proposed filtering algorithm can be decreased approximately 57% and 36% when compared to that of MESKF and MAESKF algorithms, respectively. Highlights • A multisensor adaptive Kalman filtering based on variational Bayesian is developed. • Asynchronous multirate multisensor integrated navigation improves performance of MAKF. • Proposed algorithm estimates measurement noise variances of the sensors adaptively. • Experimental results showed that the proposed algorithm is robust to outliers. [ABSTRACT FROM AUTHOR]

This paper presents the development of a Kalman Filter with Neuro-Fuzzy adaptation (KF-NFA) which is applied in attitude estimation, relying on information derived from triaxial accelerometer and gyroscope sensors contained in an inertial measurement unit (IMU). The adaptation process is performed on the filter statistical information matrices R or Q, which are tuned using an Adaptive Neuro Fuzzy Inference System (ANFIS) based on the filter innovation sequence through a covariance-matching technique. The test results show a better performance of the KF-NFA when it is compared with a traditional Kalman Filter (T-KF). This work is being developed in the context of a Pedestrian Dead Reckoning (PDR) algorithm for localization based services (LBS), currently in progress. [ABSTRACT FROM AUTHOR]

This paper presents a new method for adaptive estimation of kinematic model error in dynamic aircraft navigation. This method combines the concepts of random weighting and Sage windowing to online monitor predicted and observation residuals to control the influence of the kinematic model?s systematic error on system state estimation. Based on the Sage windowing, random weighting estimations are constructed within a moving time window for the systematic error of the kinematic model as well as the covariance matrices of the observation noise vector, the predicted residual vector, and the predicted state vector. Experimental results and comparison analysis demonstrate that the proposed method not only adjusts the covariance matrices of the observation noise vector and the predicted residual vector, but also effectively controls the influence of the kinematic model error on state parameter estimation, thus improving the navigation accuracy. [ABSTRACT FROM AUTHOR]

Ultracapacitors (UCs) are the focus of increasing attention in electric vehicle and renewable energy system applications due to their excellent performance in terms of power density, efficiency, and lifespan. Modeling and parameterization of UCs play an important role in model-based regulation and management for a reliable and safe operation. In this paper, an equivalent circuit model template composed of a bulk capacitor, a second-order capacitance-resistance network, and a series resistance, is employed to represent the dynamics of UCs. The extended Kalman Filter is then used to recursively estimate the model parameters in the Dynamic Stress Test (DST) on a specially established test rig. The DST loading profile is able to emulate the practical power sinking and sourcing of UCs in electric vehicles. In order to examine the accuracy of the identified model, a Hybrid Pulse Power Characterization test is carried out. The validation result demonstrates that the recursively calibrated model can precisely delineate the dynamic voltage behavior of UCs under the discrepant loading condition, and the online identification approach is thus capable of extracting the model parameters in a credible and robust manner. [ABSTRACT FROM AUTHOR]

Abstract: In Interferometric Fiber Optic Gyroscope (IFOG), the diminution of random noise and drift error is a critical task. These errors degrade the performance of IFOG. In this paper, a modified adaptive Kalman gain correction (AKFG) algorithm is proposed to denoise IFOG signal. The covariance matrix of innovation sequence is estimated using weighted average window method in which the weights are randomly generated in the range [0, 1]. Innovation based random weighted estimation (IRWE)-AKFG is applied to denoise the IFOG drift signal. The Kalman gain is adaptively updated using the covariance matrix of innovation sequence. The proposed algorithm is applied for denoising IFOG signal under static and dynamic environment. Allan variance method is used to analyze and quantify the stochastic errors in IFOG sensor. The performance of the proposed algorithm is compared with Conventional Kalman filter (CKF) and the simulation results reveal that the proposed algorithm is an efficient algorithm for denoising the IFOG signal. [Copyright &y& Elsevier]

We present an attitude estimation method for self-guided ballistic munitions from onboard magnetometers. An adaptive feedback filter is used to remove actuator-induced magnetic disturbances whose spectrum overlaps the signal of interest. A state-augmented extended Kalman filter (EKF) is used to estimate roll rate and sensor bias from the filtered data. Experimental results demonstrate that this method is able to accurately estimate the roll without phase delay inherent in other methods. [ABSTRACT FROM AUTHOR]

Our protocol adapts the error-control code and spreading factor on the links of a direct-sequence (DS) spread-spectrum packet radio network. The radios employ half-duplex transmission, and they operate with fixed power and bandwidth. There are no base stations, access points, or other infrastructure, and the protocol requires no power measurements or channel estimates. We compare the protocol with hypothetical ideal protocols that have perfect channel state information and maximize the throughput for each packet transmission. [ABSTRACT FROM AUTHOR]

The application of kalman filter in tracking the maneuver target is not available as it is used in tracking the target of uniform motion. Therefore, a improved method for tracking a maneuver target is proposed. In the proposed approach, the maneuver detector provides the estimate of time instant at which a target starts to maneuver, when a target maneuver is determined, the kalman filter model will be adjusted with varied target motion state. The maneuver, modeled as an acceleration, is estimated recursively. Finally, the performance of the proposed approach is shown to be superior to kalman filter by simulation. [ABSTRACT FROM AUTHOR]

The objective of this paper is concerned with the estimation problem for linear discrete-time stochastic systems with mixed uncertainties involving random one-step sensor delay, stochastic-bias measurements, and missing measurements. Three Bernoulli distributed random variables are employed to describe the uncertainties. All the three uncertainties in the measurement have certain probability of occurrence in the target tracking system. And then, an adaptive Kalman estimation is proposed to deal with this problem. The adaptive filter gains can be obtained in terms of solutions to a set of recursive discrete-time Riccati equations. Examples in three scenarios of target tracking are exploited to show the effectiveness of the proposed design approach. [ABSTRACT FROM AUTHOR]