USV Track Fusion Using GPS-Radar Correlation and Dynamic Weights
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Solution Overview
Problem
Unmanned surface vehicles (USVs) face challenges in data preprocessing, data format unification, and data fusion weight allocation, leading to inadequate target sensing capabilities during navigation.
Innovation Solution
A track fusion method and device for USVs that involves obtaining perception information, preprocessing radar data, constructing track correlation and fusion data weight allocation models, and performing data fusion using GPS and radar data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and radar data are collected for target sensing, then target detection capability is improved, but data preprocessing complexity and data format unification difficulty increase
Solution Approach 1:
The patent segments the data preprocessing task into distinct modules: radar data processing module, GPS data processing module, and data fusion module. Each module handles specific data types independently, managing complexity through functional decomposition while maintaining comprehensive target sensing capability.
2Measurement precision
If multiple data sources (GPS and radar) are integrated, then target perception accuracy is improved, but data fusion weight allocation difficulty increases
Solution Approach 1:
The patent implements dynamic weight allocation in the data fusion module, where fusion weights are adjusted based on the reliability and quality of incoming GPS and radar data. This dynamic adaptation enables accurate target perception while automatically managing the complexity of weight allocation through real-time feedback mechanisms.
3Measurement precision
If data correction and track correlation are performed, then target sensing precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data correction and filtering on radar and GPS data before fusion, pre-processing the data to reduce noise and inconsistencies. This preliminary action improves target sensing precision while reducing the computational burden during real-time fusion, thereby mitigating processing time increases.
4Reliability
If comprehensive data processing is performed to eliminate interference, then data reliability is improved, but processing complexity and resource consumption increase
Solution Approach 1:
The patent extracts and removes interfering elements from the raw GPS and radar data through dedicated filtering and correction sub-modules. By isolating and eliminating specific sources of interference (e.g., sea clutter for radar, satellite signal errors for GPS), the system improves data reliability while managing processing complexity through targeted intervention rather than comprehensive reprocessing.
Data Source
AI summary
A track fusion method for an unmanned surface vehicle includes: (a) obtaining perception information of the unmanned surface vehicle, where the perception information includes GPS data information and radar data information; (b) pre-processing the radar data information to obtain target radar information; (c) constructing a track correlation model; and performing track correlation between the GPS data information and the target radar information based on the track correlation model; and (d) constructing a fusion data weight allocation model; and subjecting between the GPS data information and the target radar information correlated therewith to track fusion based on the fusion data weight allocation model. This application further provides a track fusion device for unmanned surface vehicles.