Aircraft Navigation State Estimation With Sensor Fault Voting
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Solution Overview
Problem
Conventional inertial navigation systems (INSs) in aircraft are prone to error accumulation due to open system architectures and unreliable sensor measurements, which can impact aircraft control and safety.
Innovation Solution
A vehicle navigation system (VNS) that incorporates a voting module, prediction module, fault detection module, and update module, along with redundant sensors and processors, to enhance state estimation accuracy and resilience to sensor failures, using methods like Kalman filters and bias estimation to improve vehicle state determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional open-loop INS architecture is used, then system simplicity is maintained, but error accumulation occurs due to drift in dead-reckoning calculation
Solution Approach 1:
The patent implements a closed-loop INS architecture where the output of the navigation solution is fed back to correct sensor biases and drift. The system continuously compares the dead-reckoning position with reference positions from GPS or other external sources, and uses this feedback to adjust the inertial sensors' measurements, thereby preventing error accumulation while maintaining system reliability.
Solution Approach 2:
The patent combines multiple navigation systems (inertial navigation, GPS, and other external references) into a unified closed-loop architecture. By merging these different navigation approaches, the system leverages the strengths of each to compensate for their individual weaknesses, achieving both improved accuracy and maintained simplicity through integrated processing.
2Loss of information
If air data sources are included in conventional INS, then navigation information is enhanced, but system reliability decreases due to sensor failures like icing and poor maintenance
Solution Approach 1:
The patent implements redundancy by incorporating multiple independent sensors and navigation sources before failures occur. The system is designed with backup sensors and alternative navigation methods that can take over if primary sensors fail due to icing or maintenance issues, cushioning against the loss of information while maintaining reliability.
Solution Approach 2:
The system dynamically changes operational parameters based on sensor health status. When air data sources are detected to be unreliable (due to icing or maintenance issues), the system automatically adjusts by reducing reliance on those sensors and increasing weight on other navigation sources, thereby maintaining information completeness while preserving reliability.
3Reliability
If redundant sensors and processors are added to improve state estimation accuracy, then measurement reliability is enhanced, but device complexity increases
Solution Approach 1:
The patent uses redundant sensors that are copies of the primary sensors, positioned independently to provide backup measurements. These copied sensors increase reliability by providing alternative data sources without requiring fundamentally different or more complex sensor types, thereby improving state estimation while controlling the type of complexity introduced.
Solution Approach 2:
The redundant sensors and processors are designed to be multi-functional, serving both as primary measurement devices and as backup systems. This universal design allows the same hardware to perform multiple roles, reducing the need for separate dedicated backup systems and thereby limiting the increase in device complexity while maintaining enhanced reliability.
Data Source
AI summary
A system and method that function to generate an updated vehicle state based on a previous vehicle state and a set of sensor measurements. The previous vehicle state can be selected from a set of redundant prior vehicle state candidates. The system and method can optionally detect and correct for sensor measurement faults or failures, prior to updating the vehicle state.