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Research into a Multi-Variate Surveillance Data Fusion Processing Algorithm.

Yi Mao¹, Yi Yang¹, Yuxin Hu¹

¹State Key Laboratory of Air Traffic Management System and Technology, Nanjing 210000, China.

Sensors (Basel, Switzerland)

|November 17, 2019

Summary

This summary is machine-generated.

This study introduces advanced data integration algorithms for air traffic management (ATM) systems, enhancing surveillance accuracy using radar and Automatic Dependent Surveillance (ADS). The methods improve data fusion efficiency for critical mid-and-high altitude and approaching airspace surveillance.

Keywords:

ADS data integration radar wave filtering

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Area of Science:

Aerospace Engineering
Computer Science
Signal Processing

Background:

Civil Air Traffic Management (ATM) systems rely on radar and Automatic Dependent Surveillance (ADS).
Satellite navigation offers global coverage but requires integration with existing surveillance systems.
Surveillance challenges exist in mid-to-high altitude and approaching airspace.

Purpose of the Study:

To enhance the efficiency and accuracy of data integration in multi-sensor ATM surveillance systems.
To address the surveillance problem in critical airspace regions.
To develop a multigeneration surveillance data integration system.

Main Methods:

Proposed filter-based covariance matrix weighting method.
Proposed measurement variance weighting method.
Proposed measurement-first weighted fusion method for data integration.

Main Results:

Improved efficiency of data integration calculations under fixed accuracy.
Successful integration of multi-radar and ADS systems.
Established sensor and target track models for data fusion.

Conclusions:

The developed weighted integration algorithms effectively improve multi-radar and ADS data fusion for ATM.
The proposed methods provide a robust solution for surveillance in complex airspace.
The study lays the groundwork for advanced multigeneration surveillance data integration systems.