Maximum Likelihood State Estimation of Semi-Markovian Switching System in Non-Gaussian Measurement Noise

2010 ◽  
Vol 46 (1) ◽  
pp. 133-146 ◽  
Author(s):  
Dongliang Huang ◽  
Henry Leung
Keyword(s):  
Maximum Likelihood ◽  
State Estimation ◽  
Markovian Switching ◽  
Measurement Noise ◽  
Switching System ◽  
Non Gaussian

Robust high-degree cubature information filter and its application to trajectory estimation for ballistic missile

2017 ◽  
Vol 232 (12) ◽  
pp. 2364-2377 ◽  
Author(s):  
Xiaogang Wang ◽  
Wutao Qin ◽  
Naigang Cui ◽  
Yu Wang
Keyword(s):  
State Estimation ◽  
Estimation Algorithm ◽  
Ballistic Missile ◽  
Measurement Noise ◽  
Estimation Accuracy ◽  
Trajectory Estimation ◽  
Information Filter ◽  
Non Gaussian ◽  
Time Update ◽  
High Degree

This paper presents a new recursive filter algorithm, the robust high-degree cubature information filter, which can provide reliable state estimation in the presence of non-Gaussian measurement noise. The novel algorithm is developed in the framework of the conventional information filter. The fifth-degree Cubature rule is utilized to improve the estimation accuracy and numerical stability during the time update, while the Huber technique is adopted in the measurements update stage. As the Huber technique is a combined minimum l1 and l2 norm estimation algorithm, the proposed algorithm could exhibit robustness to the non-Gaussian measurement noise, especially the glint noise. In addition, Monte Carlo simulation and the trajectory estimation for ballistic missile experiments demonstrate that the robust high-degree cubature information filter can provide improved state estimation performance over extended information filter and high-degree cubature information filter.

Maximum Likelihood Approach to State Estimation in Online Pipeline Models

2012 ◽  
Author(s):  
Jason P. Modisette
Keyword(s):  
Maximum Likelihood ◽  
State Estimation ◽  
Measurement Noise ◽  
Multiple Models ◽  
Flow Measurements ◽  
Hydraulic Models ◽  
Maximum Likelihood Approach ◽  
True State ◽  
Likelihood Approach ◽  
A New Technique

Hydraulic models of pipelines driven by SCADA usually have many more pressure and flow measurements available that are needed as boundary conditions of the underlying model. These extra measurements can be used to improve the estimate of the true state of the pipeline and to give the hydraulic model some resistance to measurement noise. The process of merging the available SCADA into a form the model can use is called “state estimation.” A new technique for state estimation based on a maximum-likelihood estimate of the state of the pipeline constrained by the underlying physics will be presented. This method, which will be referred to as Maximum Likelihood State Estimation (MLSE), will be justified by comparison with two traditional methods of state estimation in use in the industry, the approach of breaking the pipeline up into multiple models with pressure boundaries and then rectifying the flows, and the Equal Error Fractions (EEF) method of Van der Hoeven [1]. An example of the application of this approach to a real-world pipeline will also be presented, with performance data as well as some of the deficiencies that were found and how they were corrected.

scholarly journals A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 743
Author(s):  
Xi Liu ◽  
Shuhang Chen ◽  
Xiang Shen ◽  
Xiang Zhang ◽  
Yiwen Wang
Keyword(s):  
State Estimation ◽  
Measurement Model ◽  
High Dimensional ◽  
Neural Firing ◽  
The Neural Network ◽  
Information Filter ◽  
Critical Technology ◽  
Dimensional Measurements ◽  
Non Gaussian ◽  
Low Dimensional

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.

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