Aircraft Navigation Data Blending for Low-Uncertainty Flight Control
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Solution Overview
Problem
Existing vehicle navigation systems face challenges in accurately determining navigational parameters such as position, speed, and direction due to measurement errors and uncertainties, which can lead to inaccuracies in flight path adjustments and traffic control.
Innovation Solution
A method that collects navigational parameters from sensors, GPS, and inertial reference systems, applies a Kalman filter to correct errors, determines statistical uncertainties, and assigns statistical weights to form a blended navigational solution that minimizes overall uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources are used for determining navigational parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple independent navigational data sources (GPS, inertial reference system, barometric altimeter, radar altimeter) into a single integrated navigation system. This merging allows the system to leverage the strengths of each individual source while compensating for their respective weaknesses, thereby improving overall measurement precision without requiring separate independent systems for each data source.
Solution Approach 2:
The navigation system is designed to universally process and integrate multiple types of navigational parameters (position, velocity, altitude) from diverse sources through a common filtering and weighting mechanism. This multi-functional approach allows the same system architecture to handle different data sources and parameter types, reducing overall system complexity while maintaining high precision.
2Reliability
If statistical filtering and weighting are applied to navigational data, then reliability is improved, but computational requirements increase
Solution Approach 1:
The system pre-calculates and stores statistical properties (variance, covariance matrices) of each data source during system initialization and operational phases. By preparing these statistical characteristics in advance, the real-time navigation solution can quickly apply pre-computed weighting factors without performing complex iterative optimization, thereby improving reliability while minimizing real-time computational energy consumption.
3Productivity
If real-time navigational parameter determination is performed, then productivity is improved, but measurement precision may deteriorate due to processing errors
Solution Approach 1:
The system implements continuous feedback through real-time monitoring of data quality metrics and uncertainty estimates. The Kalman filter dynamically adjusts its processing based on feedback about the reliability of incoming data from each source, allowing the system to maintain high update rates while preserving precision by adaptively weighting data based on current conditions rather than using fixed processing algorithms.
Data Source
AI summary
An aircraft includes at least one source collecting a set of navigational parameters of the aircraft, the at least one source obtaining flight data for the aircraft and including at least one of a global positioning system, an inertial reference system, or a sensor. The aircraft further includes a flight control computer communicatively coupled to the source and including a first processor and a first memory having a machine-readable medium, as well as a flight management system communicatively coupled to the flight control computer.