Recursive Filtering with Fading Measurements, Sensor Delays, and Correlated Noises

2014 ◽  
pp. 63-99
Author(s):  
Jun Hu ◽  
Zidong Wang ◽  
Huijun Gao
Keyword(s):  
Recursive Filtering ◽  
Correlated Noises

Recursive filtering with random parameter matrices, multiple fading measurements and correlated noises

Automatica ◽  
2013 ◽  
Vol 49 (11) ◽  
pp. 3440-3448 ◽  
Author(s):  
Jun Hu ◽  
Zidong Wang ◽  
Huijun Gao
Keyword(s):  
Random Parameter ◽  
Recursive Filtering ◽  
Correlated Noises

scholarly journals How to train your differentiable filter

2021 ◽  
Author(s):  
Alina Kloss ◽  
Georg Martius ◽  
Jeannette Bohg
Keyword(s):  
Bayesian Filtering ◽  
Learning Approaches ◽  
Filtering Algorithms ◽  
Recursive Filtering ◽  
Process Dynamics ◽  
Noise Characteristics ◽  
Complex Models ◽  
End To End ◽  
Robotic Applications ◽  
And Training

AbstractIn many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution. Bayesian Filtering algorithms address this state estimation problem, but they require models of process dynamics and sensory observations and the respective noise characteristics of these models. Recently, multiple works have demonstrated that these models can be learned by end-to-end training through differentiable versions of recursive filtering algorithms. In this work, we investigate the advantages of differentiable filters (DFs) over both unstructured learning approaches and manually-tuned filtering algorithms, and provide practical guidance to researchers interested in applying such differentiable filters. For this, we implement DFs with four different underlying filtering algorithms and compare them in extensive experiments. Specifically, we (i) evaluate different implementation choices and training approaches, (ii) investigate how well complex models of uncertainty can be learned in DFs, (iii) evaluate the effect of end-to-end training through DFs and (iv) compare the DFs among each other and to unstructured LSTM models.

scholarly journals Optimal Linear Estimators for Time-Delay Systems with Fading Measurements and Correlated Noises

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Yazhou Li ◽  
Jiayi Li ◽  
Xin Wang
Keyword(s):  
Time Delay ◽  
Minimum Variance ◽  
Delay Systems ◽  
Multiplicative Noises ◽  
Linear Estimators ◽  
Discrete Linear Systems ◽  
Estimation Problems ◽  
Optimal Linear ◽  
State Augmentation ◽  
Correlated Noises

The optimal linear estimation problems are investigated in this paper for a class of discrete linear systems with fading measurements and correlated noises. Firstly, the fading measurements occur in a random way where the fading probabilities are regulated by probability mass functions in a given interval. Furthermore, time-delay exists in the system state and observation simultaneously. Additionally, the multiplicative noises are considered to describe the uncertainty of the state. Based on the projection theory, the linear minimum variance optimal linear estimators, including filter, predictor, and smoother are presented in the paper. Compared with conventional state augmentation, the new algorithm is finite-dimensionally computable and does not increase computational and storage load when the delay is large. A numerical example is provided to illustrate the effectiveness of the proposed algorithms.

Event based estimation with correlated noises

2019 ◽  
Vol 356 (13) ◽  
pp. 7533-7547 ◽  
Author(s):  
Shaikshavali Chitraganti ◽  
Samir Abkerane
Keyword(s):  
Event Based ◽  
Correlated Noises

Two-Way Recursive Filtering

2021 ◽  
pp. 1-1
Author(s):  
Wei-Chih Tu ◽  
Shao-Yi Chien
Keyword(s):  
Recursive Filtering
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