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Alpha beta filter

About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.


Papers
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Proceedings ArticleDOI
01 Dec 2007
TL;DR: This study proposes an adaptive tire-force model that takes variations in road friction into account and compared to real experimental data, in particular wheel force measurements.
Abstract: This paper proposes a new process for the estimation of tire-road forces and vehicle sideslip angle. The method strictly uses measurements from sensors potentially integrable or already integrated in recent car (yaw rate, longitudinal/lateral accelerations, steering angle and angular wheel velocities). The estimation process is based on two blocks in series: the first block contains a sliding-mode observer whose principal role is to calculate tire-road forces, while in the second block an extended Kalman filter estimates sideslip angle and cornering stiffness. More specifically, this study proposes an adaptive tire-force model that takes variations in road friction into account. The paper also presents a study of convergence for the sliding-mode observer. The estimation process was applied and compared to real experimental data, in particular wheel force measurements. Experimental results show the accuracy and potential of the estimation process.

27 citations

Proceedings ArticleDOI
25 Aug 2004
TL;DR: In this paper, the authors explored the application of Kalman-Levy filter to handle maneuvering targets and found that the performance of the Kalman filter in non-maneuvering portion of track is worse than a Kalman Filter's.
Abstract: In target tracking algorithms using Kalman filtering-like approaches, the standard assumptions are Gaussian process and measurement noise models. Based on these assumptions, the Kalman filter is widely used in single or multiple filter versions (e.g., in an Interacting Multiple Model-IMM-estimator). The over-simplification resulting from the above assumptions can cause severe degradation in tracking performance. Of particular concern is the simplistic white noise or Wiener process acceleration models used to handle maneuvering targets. Presence of heavy-tailed noise in the observation process is another concern. In this paper we explore the application of Kalman-Levy filter to handle maneuvering targets. This filter assumes a heavy tailed noise distribution known as the Levy distribution. Unlike in the case of Gaussian distribution, the existence of the covariance is not guaranteed in this case. Due to the heavy tailed nature of the assumed distribution, the Kalman-Levy filter is more effective in the presence of large errors that can occur, for example, due to the onset of acceleration or deceleration. However, for the same reason, the performance of Kalman-Levy filter in non-maneuvering portion of track is worse than a Kalman filter's. This motivates us to develop an IMM estimator incorporating a Kalman filter and a Kalman-Levy filter. The performance of this filter is compared with an IMM estimator with two standard Kalman filters in a scenario from the 4th Navy tracking benchmark problem. It is found that the IMM estimator with a Kalman-Levy filter performs better than the other IMM estimator in both maneuvering and non-maneuvering portion of target flight.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

27 citations

Proceedings ArticleDOI
27 May 2007
TL;DR: A new normalized Kalman based LMS (KLMS) algorithm can be derived that has some advantages to the classical one and is suggested to control the step size, that results in good convergence properties for a large range of input signal powers, that occur in many applications.
Abstract: While the LMS algorithm and its normalized version (NLMS), have been thoroughly used and studied. Connections between the Kalman filter and the RLS algorithm have been established however, the connection between the Kalman filter and the LMS algorithm has not received much attention. By linking these two algorithms, a new normalized Kalman based LMS (KLMS) algorithm can be derived that has some advantages to the classical one. Their stability is guaranteed since they are a special case of the Kalman filter. More, they suggests a new way to control the step size, that results in good convergence properties for a large range of input signal powers, that occur in many applications. They prevent high measurement noise sensitivity that may occur in the NLMS algorithm for low order filters, like the ones used in OFDM equalization systems. In these paper, different algorithms based on the correlation form, information form and simplified versions of these are presented. The simplified form maintain the good convergence properties of the KLMS with slightly lower computational complexity.

27 citations

Journal ArticleDOI
TL;DR: In this article, the authors systematically examine the test statistics in Kalman filter on the ground of the normal, 2χ-, t- and F- distributions, and the strategies for global, regional and local statistical tests as well.
Abstract: Many estimation problems can be modeled using a Kalman filter. One of the key requirements for Kalman filtering is to characterize various error sources, essentially for the quality assurance and quality control of a system. This characterization can be evaluated by applying the principle of multivariate statistics to the system innovations and the measurement residuals. This manuscript will systematically examine the test statistics in Kalman filter on the ground of the normal, 2χ-, t- and F- distributions, and the strategies for global, regional and local statistical tests as well. It is hoped that these test statistics can generally help better understand and perform the statistical analysis in specific applications using a Kalman filter.

27 citations

Proceedings ArticleDOI
21 Mar 1994
TL;DR: In this article, the use of a Kalman filter for a fully active road vehicle suspension system is discussed, where the usual detectability and stabilizability conditions must be satisfied and all the noise signals must be white.
Abstract: Concerns the use of a Kalman filter for a fully active road vehicle suspension system. The usual detectability and stabilizability conditions must be satisfied and all the noise signals must be white. The detectability condition is obeyed by using the filtered white noise road input. The use of this input, as opposed to the frequency limited integrated white noise input, is justified by r.m.s. simulation results. The stabilizability condition is obeyed by having a spring in parallel with the actuator, which is also of practical significance for suspending the static load, hence decreasing actuator power consumption. The description of the noise processes as bandlimited white noise models a worst case scenario as all of the noise signal is present in the operational bandwidth of the closed loop system. The closed loop system using the Kalman filter was simulated and compared to that using full state feedback. Results using the Kalman filter were encouraging, showing a small degradation in performance compared to the nominal system. Interesting results were obtained for road roughnesses different from those for which it was designed. There was a very small degradation in performance, which indicates that there seems to be no need to adapt the Kalman filter gain for different road conditions. Therefore, the potential improvements of using this system, as opposed to the usual LQG method using full state feedback, are enormous. However, it was found that the use of the Kalman filter led to a marked degradation in the stability margins of the system.< >

27 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202331
202277
20211
201910
201836
2017269