Ship Position Estimation Using Iterative Model Parameter Updates
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Solution Overview
Problem
Current position prediction methods for ships, such as those using AIS, suffer from inaccuracies due to delayed satellite signals, missing or abnormal data, and random errors, leading to insufficient positioning accuracy near cross-sea bridges, which can result in ship-bridge collision risks.
Innovation Solution
A position estimation and prediction method that updates a model parameter using a position data sequence to determine accurate position prediction data, combining it with observation data for estimation, and iteratively refining predictions until an iteration ending condition is met, ensuring real-time and accurate ship positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rate-based prediction method or other conventional prediction methods are used, then prediction can be performed, but position calculation accuracy is insufficient
Solution Approach 1:
The patent implements feedback by continuously updating model parameters using the latest position observation data and prediction results. The position estimation at each time step feeds back into the model to refine parameters, creating a closed-loop system that adapts to changing ship motion patterns and improves accuracy over time.
Solution Approach 2:
The patent dynamically changes model parameters based on observed position data sequences. By adapting parameters to reflect current ship motion characteristics and environmental conditions, the system maintains high prediction accuracy despite varying operational conditions, directly addressing the accuracy-reliability contradiction.
2Measurement precision
If model parameters are continuously updated using position data sequences, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent employs dynamic model parameter updating where the complexity of computation adapts to the quality and quantity of available data. The system adjusts its computational effort based on data sequences length and variability, maintaining high accuracy while avoiding unnecessary computational overhead when data is limited or patterns are stable.
3Measurement precision
If position prediction data is determined using historical position data sequences, then prediction capability is enhanced, but response time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing position data sequences and maintaining updated model parameters ready for rapid prediction. The system prepares prediction models in advance with learned patterns from historical data, enabling fast real-time predictions without extensive computation during critical decision-making moments.
Data Source
AI summary
Embodiments of the application provide a position estimation and prediction method for a moving object. The method includes: updating a model parameter of a position prediction model according to a position data sequence of the moving object at a current sampling moment; determining position prediction data at the current sampling moment according to the position data sequence and the position prediction model after the model parameter is updated; determining position estimation data at the current sampling moment according to the position prediction data and the position observation data at the current sampling moment; and determining position prediction data of a next sampling moment according to the position estimation data at the current sampling moment, the position data sequence and the position prediction model after the model parameter is updated, and entering an iteration operation of the next sampling moment.