Unmanned Surface Vessel Parameter Estimation Under Outlier Noise
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing parameter estimation methods for unmanned surface vessels face challenges in accurately estimating model parameters, especially when dealing with nonlinear dynamics and outlier noises, and fail to effectively consider process noise characteristics.
Innovation Solution
An adaptive robust estimation method using an augmented state estimation problem and an adaptive robust unscented Kalman filtering approach with process noise covariance matrix constraints, combining maximum cross entropy and minimum mean square error criteria, to improve estimation accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional Kalman filtering is used for parameter estimation, then the estimation process is simple, but the estimation accuracy deteriorates due to outlier noises and unknown noise characteristics
Solution Approach 1:
The patent introduces an intermediary mechanism (robust estimation criterion and adaptive noise characterization) between the standard Kalman filter and the parameter estimation problem. This intermediary layer filters out the harmful effects of outlier noises and unknown noise characteristics while preserving the simplicity of the Kalman filtering framework, thereby improving estimation accuracy without significantly complicating the process.
Solution Approach 2:
The patent dynamically adjusts the noise covariance parameters based on the actual system behavior and outlier detection. By changing the noise characteristics parameters adaptively rather than assuming fixed values, the estimator can maintain high accuracy even when faced with unknown noise characteristics and outlier contaminations.
2Reliability
If robust estimation methods are applied to handle outlier noises, then the robustness improves, but the computational complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the estimation algorithm continuously monitors the residual errors and adjusts the noise covariance parameters accordingly. This feedback loop enables the system to maintain robustness against outlier noises by adaptively responding to their presence, while avoiding the need for complex pre-processing or post-processing steps that would increase computational burden.
3Adaptability or versatility
If the process noise covariance matrix is estimated without constraints, then the estimation flexibility is high, but the estimation accuracy deteriorates due to high dimensionality
Solution Approach 1:
The patent segments the high-dimensional process noise covariance matrix into multiple lower-dimensional blocks or sub-matrices. This segmentation reduces the computational complexity and improves estimation accuracy by making the estimation problem more manageable, while still maintaining the overall flexibility through the block structure that can capture different noise characteristics in different state dimensions.
Data Source
AI summary
An adaptive robust estimation method and system for parameters of an unmanned surface vessel belongs to the field of automatic control. First, an augmented state estimation problem considering an external disturbance for the unmanned surface vessel is constructed; augmented state vectors include a state vector of the unmanned surface vessel, a parameter vector, and an unknown input vector; and then, depending on a real-time input vector and a measurement vector of a system, a designed adaptive robust unscented Kalman filtering method is employed to obtain model parameters of the unmanned surface vessel.