Celestial Navigation Fusion for GNSS-Denied Positioning
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Solution Overview
Problem
Existing navigation systems rely heavily on Global Navigation Satellite Systems (GNSS) which can be obstructed or jammed, leading to unreliable positioning in certain scenarios.
Innovation Solution
A system that utilizes celestial measurement data, such as the azimuth and elevation of the Sun or astronomical quaternions, in conjunction with inertial measurement data, to determine a vehicle's location using a Kalman filter, enabling navigation without GNSS reliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNSS is used for navigation, then position fixation can be achieved under normal conditions, but reliability deteriorates when satellites are obstructed or jammed
Solution Approach 1:
The system transitions between different measurement parameters and sources (GNSS signals, celestial body observations, inertial sensor data) based on environmental conditions. When GNSS is obstructed or jammed, the system switches to using celestial body azimuth and elevation angles combined with inertial navigation parameters to maintain reliable position fixation.
Solution Approach 2:
The navigation system is divided into multiple independent subsystems: GNSS receiver, celestial measurement system, and inertial measurement unit. Each subsystem can operate independently, allowing the system to segment the navigation function across different technologies to ensure reliability when one subsystem fails or is blocked.
2Adaptability or versatility
If celestial measurement system is used, then navigation without GNSS is enabled, but device complexity increases
Solution Approach 1:
The celestial measurement system is merged with the inertial measurement unit and GNSS receiver into an integrated navigation system. The processor combines data from all three sources using a unified algorithm framework, reducing overall system complexity compared to operating separate systems independently.
Solution Approach 2:
The measurement system is designed to perform multiple functions: it can track GNSS satellites, observe celestial bodies (sun, moon, stars), and provide data for both position fixation and attitude determination. This multi-functionality reduces the need for separate dedicated systems.
3Measurement precision
If Kalman filter is used to integrate measurements, then accuracy is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary calculations by predicting celestial body positions based on time and estimated location before actual measurements are taken. This preliminary action reduces the computational burden during real-time filtering by pre-computing expected measurement values and comparison criteria.
Solution Approach 2:
The Kalman filter implementation is dynamically adjusted based on measurement quality and system state. The processor adapts the filter parameters and computation frequency to balance accuracy requirements with available computational resources, reducing power consumption when full precision is not critical.
Data Source
AI summary
Methods and systems are provided for determining navigational information for an entity such as a vehicle using celestial measurements. One method involves determining a Kalman gain based at least in part on inertial measurement data at or before a current point in time associated with the celestial measurement data, determining a celestial measurement error based at least in part on a relationship between the celestial measurement data and a prior estimate for the celestial measurement data, determining a current estimated error associated with the location of the entity based at least in part on a prior estimated error associated with the location, the Kalman gain and the celestial measurement error, and determining an updated estimate of the location of the entity based at least in part on the current estimated error and a prior estimate of the location of the entity.