scholarly journals Carrier recovery for M-QAM signals based on a block estimation process with Kalman filter

2014 ◽  
Vol 22 (13) ◽  
pp. 15376 ◽  
Author(s):  
Takashi Inoue ◽  
Shu Namiki
Keyword(s):  
Kalman Filter ◽  
Carrier Recovery ◽  
Estimation Process

scholarly journals EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4174 ◽  
Author(s):  
Rodrigo Munguía ◽  
Sarquis Urzua ◽  
Antoni Grau
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Dynamic Models ◽  
Model Parameters ◽  
State Variables ◽  
Test Bed ◽  
Estimation Process ◽  
Observability Analysis ◽  
Aerial Vehicles ◽  
Aerial Vehicle

This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.

scholarly journals State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 971
Author(s):  
Yogendra Rao Musunuri ◽  
Oh-Seol Kwon
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
The State ◽  
Experimental Results ◽  
Estimation Process ◽  
Temporal Noise ◽  
Nonlinear Noise ◽  
Conventional Methods ◽  
3D Skeleton

In this study, we propose a method for minimizing the noise of Kinect sensors for 3D skeleton estimation. Notably, it is difficult to effectively remove nonlinear noise when estimating 3D skeleton posture; however, the proposed randomized unscented Kalman filter reduces the nonlinear temporal noise effectively through the state estimation process. The 3D skeleton data can then be estimated at each step by iteratively passing the posterior state during the propagation and updating process. Ultimately, the performance of the proposed method for 3D skeleton estimation is observed to be superior to that of conventional methods based on experimental results.

scholarly journals Kalman Filter Adaptation to Disturbances of the Observer’s Parameters

Inventions ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 80
Author(s):  
Alexander A. Manin ◽  
Sergey V. Sokolov ◽  
Arthur I. Novikov ◽  
Marianna V. Polyakova ◽  
Dmitriy N. Demidov ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Vector ◽  
Stochastic Systems ◽  
Adaptive Estimation ◽  
Measuring System ◽  
Time Intervals ◽  
Estimation Process ◽  
Nonlinear Programming Methods ◽  
Interval Observers ◽  
Accuracy And Stability

Currently, one of the most effective algorithms for state estimation of stochastic systems is a Kalman filter. This filter provides an optimal root-mean-square error in state vector estimation only when the parameters of the dynamic system and its observer are precisely known. In real conditions, the observer’s parameters are often inaccurately known; moreover, they change randomly over time. This in turn leads to the divergence of the Kalman estimation process. The problem is currently being solved in a variety of ways. They include the use of interval observers, the use of an extended Kalman filter, the introduction of an additional evaluating observer by nonlinear programming methods, robust scaling of the observer’s transmission coefficient, etc. At the same time, it should be borne in mind that, firstly, all of the above ways are focused on application in specific technical systems and complexes, and secondly, they fundamentally do not allow estimating errors in determining the parameters of the observer themselves in order to compensate them for further improving the accuracy and stability of the filtration process of the state vector. To solve this problem, this paper proposes the use of accurate observations that are irregularly received in a complex measuring system (for example, navigation) for adaptive evaluation of the observer’s true parameters of the stochastic system state vector. The development of the proposed algorithm is based on the analytical dependence of the Kalman estimate variation on the observer’s parameters disturbances obtained using the mathematical apparatus for the study of perturbed multidimensional dynamical systems. The developed algorithm for observer’s parameters adaptive estimation makes it possible to significantly increase the accuracy and stability of the stochastic estimation process as a whole in the time intervals between accurate observations, which is illustrated by the corresponding numerical example.

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