Cloud Obstacle Detection Using Multi-Aircraft Sensor Fusion
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Solution Overview
Problem
Existing aircraft threat detection systems struggle to accurately identify obstacles in areas such as runways, especially in adverse weather conditions, and higher frequency radars are susceptible to attenuation, complicating safe aircraft operations.
Innovation Solution
A cloud-based system that combines sensor data from multiple aircraft to compare historical and new data, using deep learning and image analysis to identify differences indicative of threats, and updates a threat database for real-time alerting and path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher frequency radars are used to improve resolution for obstacle detection, then measurement precision is improved, but the radar becomes susceptible to severe attenuation in rain, worsening reliability
Solution Approach 1:
The patent combines data from multiple radar systems operating at different frequencies to achieve both high resolution and reliability. By merging observations from different radar bands, the system overcomes the limitations of individual high-frequency radars that suffer from rain attenuation, while maintaining the ability to detect obstacles with high precision through multi-frequency synthesis
Solution Approach 2:
The patent introduces cloud-based processing as an intermediary that aggregates and analyzes radar data from multiple aircraft and ground stations. This intermediary system processes raw radar returns, applies algorithms to distinguish obstacles from weather phenomena, and produces reliable detection results that compensate for the attenuation problems of individual high-frequency radar systems
2Device complexity
If traditional single-aircraft radar systems are used for threat detection, then device complexity is low, but measurement precision and reliability of obstacle identification deteriorate
Solution Approach 1:
The patent merges radar data from multiple aircraft and ground-based sensors into a unified detection system. By combining observations from different spatial locations and sensor types, the system achieves superior threat detection accuracy that no single aircraft could attain alone, while the cloud-based architecture manages the complexity through centralized processing
Solution Approach 2:
The patent adds spatial and temporal dimensions to threat detection by incorporating data from multiple aircraft at different positions and times. This multi-dimensional approach allows the system to distinguish real threats from false alarms by analyzing patterns across multiple observations, significantly improving detection precision without requiring each individual sensor to be overly complex
3Reliability
If real-time obstacle detection is implemented in adverse weather conditions, then safety is improved, but measurement precision deteriorates due to fog and rain interference
Solution Approach 1:
The patent combines data from multiple radar frequencies and multiple observation points to penetrate adverse weather conditions. By merging signals that have traveled through fog and rain from different angles and frequencies, the system reconstructs accurate obstacle images that maintain high precision even when individual signals are degraded by weather interference
Solution Approach 2:
The system employs feedback mechanisms where detection results from multiple aircraft and previous time steps are continuously refined. The cloud-based system analyzes patterns in the data, learns from false positives and negatives, and adjusts processing parameters to maintain high measurement precision in adverse weather, thereby ensuring continuous safety improvements
Data Source
AI summary
A system for detecting threats at an area is disclosed. The system may include a controller including one or more processors configured to execute a set of program instructions stored in a memory. The set of program instructions may be configured to cause the one or more processors to receive safe historical data of an area configured to be representative of a lack of threats, receive new data of the area from one or more nodes, compare the new data and the safe historical data to identify a difference between the new data and the safe historical data, and update a database based on the difference.