Topic
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 published on a yearly basis
Papers
More filters
TL;DR: A novel adaptive H-infinity filtering algorithm is presented, which integrates the adaptive Kalman filter and the H-Infinity filter in order to perform a comprehensive filtering algorithm and has multiple advantages compared to the other filtering algorithms.
Abstract: The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.
35 citations
TL;DR: In this article, a constrained Kalman filtering method is proposed that estimates the parameters under the physical constraints using a general constrained optimization technique, and the accuracy is also improved through the use of a nonapproximated Kalman filter design.
Abstract: The odometry information used in mobile robot localization can contain a significant number of errors when robot experiences slippage. To offset the presence of these errors, the use of a low-cost gyroscope in conjunction with Kalman filtering methods has been considered by many researchers. However, results from conventional Kalman filtering methods that use a gyroscope with odometry can unfeasible because the parameters are estimated regardless of the physical constraints of the robot. In this paper, a novel constrained Kalman filtering method is proposed that estimates the parameters under the physical constraints using a general constrained optimization technique. The state observability is improved by additional state variables and the accuracy is also improved through the use of a nonapproximated Kalman filter design. Experimental results show that the proposed method effectively offsets the localization error while yielding feasible parameter estimation.
35 citations
TL;DR: In this paper, an adaptive unscented Kalman filter (AUKF) formulation for orientation estimation of aircraft and UAV utilizing low-cost attitude and heading reference systems (AHRS) was developed.
Abstract: Purpose – This paper aims to develop an adaptive unscented Kalman filter (AUKF) formulation for orientation estimation of aircraft and UAV utilizing low‐cost attitude and heading reference systems (AHRS).Design/methodology/approach – A recursive least‐square algorithm with exponential age weighting in time is utilized for estimation of the unknown inputs. The proposed AUKF tunes its measurement covariance to yield optimal performance. Owing to nonlinear nature of the dynamic model as well as the measurement equations, an unscented Kalman filter (UKF) is chosen against the extended Kalman filter, due to its better performance characteristics. The unscented transformation of the UKF is shown to equivalently capture the effect of nonlinearities up to second order without the need for explicit calculations of the Jacobians.Findings – In most conventional AHRS filters, severe problems can occur once the system suddenly experiences additional acceleration, resulting in erroneous orientation angles. On the contr...
35 citations
TL;DR: A static gain observer for linear continuous plants with intrinsic pulse-modulated feedback is analyzed to asymptotically drive the state estimation error to zero and synchronize the sequence of pulse modulation instants estimated by the observer with that of the plant.
35 citations
01 Jan 2006
TL;DR: A new proof for high-gain observer is given, which explains a direct relation between observer gain and observer error, and it is proved the stability of the closed-loop system if the weights of RBF neural networks have certain learning rules and the observer is fast enough.
Abstract: Normal industrial PD control of Robot has two drawbacks: it needs joint velocity sensors, and it cannot guarantee zero steady-state error. In this paper we make two modifications to overcome these problems. High-gain observer is applied to estimate the joint velocities, and an RBF neural network is used to compensate gravity and friction. We give a new proof for high-gain observer, which explains a direct relation between observer gain and observer error. Based on Lyapunov-like analysis, we also prove the stability of the closed-loop system if the weights of RBF neural networks have certain learning rules and the observer is fast enough.
35 citations