State Space Estimation for UAV Relative Navigation
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Solution Overview
Problem
Existing relative navigation systems face challenges in accurately determining the relative position and attitude between a UAV and a platform, especially in GNSS-denied environments, due to growing unknowns with the number of antennas, inability to consider Doppler measurements, and systematic errors from asynchronous range measurements, as well as the difficulty in integrating additional sensor data like IMU and radar or laser altimeters.
Innovation Solution
A method using a state space estimation algorithm, such as a Kalman filter, to directly estimate the relative position and attitude of the UAV body frame with regard to the ship, allowing for asynchronous range measurements and integration of Doppler and additional sensor data, thereby reducing systematic errors and facilitating the use of off-the-shelf distance measurement systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the non-linear least squares approach is used to estimate relative position of each antenna, then the relative position and attitude information can be obtained, but the number of unknowns to be estimated grows with the number of antennas
Solution Approach 1:
The patent combines the estimation of relative position and attitude into a unified state vector in a state space model. Instead of estimating antenna positions and attitudes separately (which would create growing unknowns), the system estimates the relative state of the second platform directly, merging multiple estimation tasks into a single integrated framework that reduces the total number of unknowns.
Solution Approach 2:
The patent transitions from estimating individual antenna positions in 3D space (growing complexity with each antenna) to estimating the relative pose (position and attitude) of the entire second platform as a single entity. This dimensional transformation reduces the problem from multiple independent estimations to one unified estimation task.
2Measurement precision
If the non-linear least squares approach is used, then relative navigation can be achieved, but Doppler measurements cannot be considered
Solution Approach 1:
The state space estimation framework is designed to be universal and can process multiple types of measurements simultaneously. The measurement model in the state space formulation can accommodate range measurements, Doppler measurements, and other sensor data types, making the system versatile rather than limited to a single measurement type as in the non-linear least squares approach.
3Ease of manufacture
If range measurements are made sequentially with different antennas, then measurements can be performed with standard systems, but systematic errors are introduced due to different time of validity
Solution Approach 1:
The state space model incorporates time dynamics explicitly through the state transition model. Instead of requiring all measurements to be simultaneous (which would introduce synchronization complexity), the system dynamically models how the relative state evolves over time, allowing sequential measurements at different timestamps to be properly weighted and integrated according to their respective times of validity.
4Measurement precision
If additional sensors like IMU and radar altimeter are integrated, then navigation accuracy can be improved, but the complexity of processing increases significantly
Solution Approach 1:
The state space estimation framework provides a universal processing architecture that can handle multiple sensor types through a unified measurement model. Each sensor (IMU, radar altimeter, range/Doppler measurements) is integrated through its own measurement equation that maps sensor readings to the state vector, providing a systematic and manageable approach rather than ad-hoc processing for each sensor type.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the number of unknowns to estimate, enables consideration of Doppler measurements, and allows for the use of off-the-shelf systems, improving navigation accuracy and ease of integration with additional sensors, facilitating autonomous landing and navigation in GNSS-denied environments.
Implementation Method 1
four or more transmitters of positioning signals are located on a platform of a first object and a second object approaching the platform comprises three or more receivers for receiving the positioning signals
Implementation Method 2
The Doppler shift of the received signal can also be measured, which can be converted to a measurement of the relative velocity between antenna and transponder
Data Source
AI summary
A method for platform relative navigation using range measurements involves four or more transmitters of positioning signals located on and/or near a platform of a first object and a second object approaching the platform that includes three or more receivers for receiving the positioning signals. For each received positioning signal, a range measurement between the transmitter of the positioning signal and the receiver of the positioning signal is performed. The relative position and relative attitude of a body frame of the second object is estimated with regard to the first object by processing the range measurements with a state space estimation algorithm implementing a model of the system of the first and second object.