Predictive Aircraft Surface State Event Track System
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Solution Overview
Problem
Current air traffic control systems face inefficiencies and inaccuracies in predicting aircraft departure times due to manual estimation and lack of reliable surveillance systems, particularly in non-movement areas of airports, leading to missed slots and suboptimal runway use.
Innovation Solution
An autonomous and automatic predictive aircraft surface state event track (ASSET) system that uses onboard sensors and cloud-based processing to detect aircraft events and operational states, providing confidence intervals for meeting scheduled events without the need for expensive surveillance systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual ready time prediction and verbal release time coordination are used, then the system is simple to operate, but the prediction accuracy and reliability deteriorate
Solution Approach 1:
The patent replaces manual mechanical processes (verbal communication, manual estimation) with automated electronic systems. The mobile device automatically collects sensor data, processes it through algorithms, and transmits structured messages, eliminating the need for manual ready time prediction and verbal coordination between Tower and Center.
Solution Approach 2:
The system enables the aircraft to self-report its status and position through automated sensor data collection and message transmission. The mobile device on the aircraft autonomously monitors operational parameters and communicates them to flight management personnel, removing the need for manual reporting.
2Measurement precision
If expensive surveillance systems are installed to improve aircraft tracking, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
Instead of using ground-based surveillance systems to track aircraft (traditional approach), the patent inverts the approach by having the aircraft itself carry sensors and actively report its status. The mobile device on the aircraft collects sensor data and transmits it to ground systems, reversing the traditional surveillance paradigm.
Solution Approach 2:
The mobile device with sensors acts as an intermediary between the aircraft's operational state and the ground-based flight management system. It collects data from various aircraft sensors and transmits processed information to personnel, serving as a bridge that eliminates the need for expensive dedicated surveillance infrastructure.
3Ease of operation
If release time windows are used to manage departures, then coordination is simplified, but uncertainty increases and slots are missed
Solution Approach 1:
The system implements continuous feedback by automatically monitoring aircraft operational parameters (engine status, brake status, door status) and transmitting real-time updates to flight management personnel. This continuous feedback loop allows for more accurate and timely coordination compared to static release time windows, improving the reliability of meeting scheduled slots.
Solution Approach 2:
The system performs preliminary assessment of aircraft readiness by monitoring sensor data before departure. By determining aircraft ready status in advance through automated sensor monitoring and message transmission, the system enables more accurate release time coordination and reduces uncertainty about whether the aircraft will meet its scheduled departure slot.
Data Source
AI summary
An automatic, autonomous predictive aircraft surface state event track (ASSET) system, includes a mobile device onboard an aircraft and a remote service in communication with the mobile device. The mobile device includes a processor, and an application that in turn includes machine instructions encoded on a non-transitory computer-readable storage medium. The processor executes the machine instructions to receive sensor data from aircraft onboard sensors, the sensor data indicating an operational state of the aircraft; and transmit the sensor data. The remote service receives the sensor data and includes a remote processor that executes machine instructions to compute an operational state of the aircraft; identify an aircraft event associated the aircraft; and using the aircraft operational data, the sensor data, and the event, predict that the aircraft will meet a next scheduled aircraft event within a specified time window.