Showing papers on "Alpha beta filter published in 2019"
TL;DR: A novel solution for improving the accuracy of indoor navigation using a learning to perdition model-based artificial neural network to improve the prediction accuracy of the prediction algorithm is presented.
Abstract: The navigation system has been around for the last several years. Recently, the emergence of miniaturized sensors has made it easy to navigate the object in an indoor environment. These sensors give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user’s context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization, whereas interest in location-based services is also rising. While numerous inertial measurement unit-based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. In this article, we present a novel solution for improving the accuracy of indoor navigation using a learning to perdition model. The design system tracks the location of the object in an indoor environment where the global positioning system and other satellites will not work properly. Moreover, in order to improve the accuracy of indoor navigation, we proposed a learning to prediction model-based artificial neural network to improve the prediction accuracy of the prediction algorithm. For experimental analysis, we use the next generation inertial measurement unit (IMU) in order to acquired sensing data. The next generation IMU is a compact IMU and data acquisition platform that combines onboard triple-axis sensors like accelerometers, gyroscopes, and magnetometers. Furthermore, we consider a scenario where the prediction algorithm is used to predict the actual sensor reading from the noisy sensor reading. Additionally, we have developed an artificial neural network-based learning module to tune the parameter of alpha and beta in the alpha–beta filter algorithm to minimize the amount of error in the current sensor readings. In order to evaluate the accuracy of the system, we carried out a number of experiments through which we observed that the alpha–beta filter with a learning module performed better than the traditional alpha–beta filter algorithm in terms of RMSE.
30 citations
TL;DR: A new fractional error back-propagation learning algorithm is derived to adapt weights of the artificial neural network, by taking advantage of the Lyapunov stability strategy of fractional-order systems which is called Miattag–Leffler stability.
Abstract: In this paper, a novel observer structure for nonlinear fractional-order systems is presented to estimate the states of fractional-order nonlinear chaotic system with unknown dynamical model. A new fractional error back-propagation learning algorithm is derived to adapt weights of the artificial neural network, by taking advantage of the Lyapunov stability strategy of fractional-order systems which is called Miattag–Leffler stability. The main contribution is the extension of neural observer for fractional dynamics in a way that satisfies Miattag–Leffler conditions. Observer design procedure guarantees the convergence of observer error to the neighborhood of zero. Furthermore, the robustness of the proposed estimator against uncertainties and external disturbances are the main benefits of the proposed method. Simulation results show the effectiveness and capabilities of the proposed observer.
18 citations
TL;DR: In this paper, a constrained integrated total Kalman filter algorithm was developed for integrated direct geo-referencing in which a quadratic constraint may appear in some problems of integrated direct Geo-reference.
Abstract: A constrained integrated total Kalman filter algorithm is developed. It considers a quadratic constraint which may appear in some problems of integrated direct geo-referencing in particular when IN...
11 citations
TL;DR: This study applies the recently developed iterative ensemble Kalman filter in the context of well-test analysis to infer reservoir parameters from the noisy recorded data to verify the robustness of the developed algorithm even in dealing with complex heterogeneous models.
Abstract: Accurate estimation of the reservoir parameters is crucial to predict the future reservoir behavior. Well testing is a dynamic method used to estimate the petro-physical reservoir parameters through imposing a rate disturbance at the wellhead and recording the pressure data in the wellbore. However, an accurate estimation of the reservoir parameters from well-test data is vulnerable to the noise at the recorded data, the non-uniqueness of the obtained match, and the accuracy of the optimization algorithm. Different stochastic optimization methods have been applied to this address problem in the literature. In this study, we apply the recently developed iterative ensemble Kalman filter in the context of well-test analysis to infer reservoir parameters from the noisy recorded data. Since the introduction of the ensemble Kalman filter (EnKF) by Evensen in 1994 as a novel method for data assimilation, it has had enormous impact in many application domains because of its robustness and ease of implementation, and numerical evidence of its accuracy. While the objective of the standard EnKF approaches is to approximate the statistical properties of geological parameters conditioned to observation, via an ensemble, the objective of the iterative ensemble Kalman methods is to approximate the solution of inverse problems using a deterministic derivative-free iterative scheme. We conducted three case studies of the application of the iterative ensemble Kalman methods for a well-test analysis of a homogenous reservoir model, a dual-porosity heterogeneous system, and a faulted discontinuous reservoir. We demonstrated that the convergence occurs very rapidly almost at the first iterations contrary to the well-known particle swarm optimization algorithm. The maximum relative error for the simulated cases is below 15%, which belongs to the skin factor. Low relative error, narrowed uncertainty range over time, and excellent graphical match obtained between the simulated derivative data and the generated curve by using the iterative EnKF verify the robustness of the developed algorithm even in dealing with complex heterogeneous models.
10 citations
TL;DR: Employing the novel interactive mechanism, the numerical study and experiments indicate that the proposed method has remarkable improvement on average performance in the uncertain and complex environment.
Abstract: Target tracking is popular in computer vision field. Although the classic BPNN completes targets tracking, its computation is complex and tracking accuracy is low when the tracking scene is uncertain or complex. To deal with the difficulties above, in this paper, we propose an innovative target tracking method combined adaptive $\alpha $ – $\beta $ filter with robust BPNN. First, we utilize the adaptive $\alpha $ – $\beta $ filter to compute the location region on optimal filtering parameters in the prediction stage. Of course, the novel filter reduces the region and gives effective image information to the robust BPNN that has the optimal number and weight of neurons as well as the improved learning rate. Subsequently, the network makes an accurate recognition and sends back the updated positions of targets to the filter for the next cycle. Employing the novel interactive mechanism, the numerical study and experiments indicate that the proposed method has remarkable improvement on average performance in the uncertain and complex environment.
4 citations
TL;DR: A linear-quadratic-Gaussian/loop transfer recovery (LQG/LTR) procedure using reduced-order Kalman filters by extending known exact-recovery result to provide a systematic method for directly designing reduced- order LQG controllers without additional coordinate transformations.
Abstract: This paper discusses a linear-quadratic-Gaussian/loop transfer recovery (LQG/LTR) procedure using reduced-order Kalman filters by extending known exact-recovery result. The state-space realisation commonly used for reduced-order observer design is employed. The zero structure intrinsic to the realisation is revealed. Asymptotic recovery is achieved using a non-singular reduced-order Kalman filter with a parameterised set of covariance matrices. The proposed procedure provides a systematic method for directly designing reduced-order LQG controllers without additional coordinate transformations. A numerical design example for a simple multivariable plant is presented to compare the proposed design with the standard LQG/LTR design using a full-order Kalman filter.
3 citations
TL;DR: In this paper, the standard Kalman filter cannot handle inequality constraints imposed on the state variables, as state truncation induces a nonlinear and non-Gaussian model, and a Rao-Blackwellized partic...
Abstract: The standard Kalman filter cannot handle inequality constraints imposed on the state variables, as state truncation induces a nonlinear and non-Gaussian model. We propose a Rao-Blackwellized partic...
2 citations
01 Apr 2019
TL;DR: In this study, a novel target tracking algorithm is proposed with a better tracking performance for the case where the measurement noise and target arrange are high and target maneuvers are intense.
Abstract: In this study, a novel target tracking algorithm is proposed with a better tracking performance for the case where the measurement noise and target arrange are high and target maneuvers are intense. The proposed FastIMM-FM algorithm has been shown to have a better position estimation than the FastIMM algorithm and the FastIMM-imp algorithm. Also, FastIMM-FM algorithm has less computational cost than the other two algorithms.
1 citations
Patent•
19 Mar 2019
TL;DR: In this article, an alpha-beta filter-based star vector measurement error online estimation method is proposed to help in real-time calculate star vector weight, optimize attitude estimation algorithms and improve the attitude measurement precision.
Abstract: The invention discloses an alpha-beta filter-based star vector measurement error online estimation method. The alpha-beta filter-based star vector measurement error online estimation method comprisesthe following steps of S1, according to the observed star vector and the reference star vector of an attitude determining star, calculating an attitude quaternion q through an equally weighed method;S2, processing the attitude quaternion q through an alpha-beta filter to obtain a reference attitude quaternion q; S3, computing a reference attitude matrix A according to the reference attitude quaternion q; S4, according to the reference attitude matrix A, calculating the error of the observed star vector. The alpha-beta filter-based star vector measurement error online estimation method can help in real time calculate star vector weight, optimize attitude estimation algorithms and improve the attitude measurement precision.
Patent•
26 Feb 2019
TL;DR: In this article, the authors proposed a radar target tracking system based on a self-tuning alpha-beta filter, which is used to filter the unknown targets effectively and ensure the filtering accuracy.
Abstract: The invention relates to a disturbing target filtering method of a radar target tracking system based on an alpha-beta filter. Compared with the prior art, the invention solves the defect that the filtering accuracy cannot be guaranteed when a radar tracks an unknown target. The method comprises the following steps: setting of a radar target tracking system; establishing of a virtual noise model;estimation calculation; filtering of a self-tuning alpha-beta filter. The method establishes a virtual noise model by using a method for fitting the virtual noise, and the system with parameter disturbance is transformed into a filtering problem with stable unknown parameters and known noise by using the virtual noise instead of disturbance and dynamic noise of the system itself. Therefore, a self-tuning alpha-beta filter is generated, which is used to filter the unknown targets effectively and ensure the filtering accuracy.