AUV Seabed Image Correlation for GPS-Free Return Navigation
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Solution Overview
Problem
Autonomous underwater vehicles face navigation challenges in deep or hostile waters where periodic geographic fixes are not feasible, as existing inertial navigation systems drift due to noisy sensor data, and reliance on seabed features is ineffective in featureless areas.
Innovation Solution
A coherence-based navigation system that generates complex images with amplitude and phase data, using normalized cross-correlation coefficients to monitor and maintain the vehicle's position relative to an ingress path, allowing for precise re-navigation without external fixes by exploiting the static nature of the seabed's specular reflections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If inertial navigation systems are used for underwater vehicles, then navigation capability is provided, but position drift occurs due to noisy sensor data integration over time
Solution Approach 1:
The system continuously compares current complex sonar images with stored reference complex images and uses the calculated NCCC values as feedback to adjust vehicle position, maintaining alignment with the ingress path throughout the egress cycle
Solution Approach 2:
The system performs preliminary mapping during the ingress cycle by storing complex sonar images and terrain data before egress, enabling subsequent navigation without requiring external fixes during the actual return journey
2Measurement precision
If periodic geographic fixes are obtained by surfacing for GPS fixes, then position accuracy is improved, but operational convenience deteriorates in deep or hostile water
Solution Approach 1:
The vehicle navigates autonomously using its own stored complex images as a reference map, comparing current sonar imagery against its self-generated ingress path data without requiring external GPS infrastructure or surfacing for fixes
3Loss of information
If traditional sonar imagery is used for terrain recognition, then navigation data is obtained, but aspect dependence reduces recognition accuracy
Solution Approach 1:
The system transitions from traditional 2D amplitude-only sonar imagery to 3D complex images that incorporate both amplitude and phase information, creating a more complete representation of the seabed that is invariant to viewing angle changes
4Measurement precision
If complex image processing with NCCC calculation is performed, then navigation precision is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the essential coherence information through NCCC calculation between corresponding image regions, discarding redundant data while retaining the critical navigation signal
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
Enables precise re-navigation in 3D space without external fixes, reducing vulnerability and energy consumption, even in featureless seabed environments, by maintaining maximum coherence between ingress and egress paths.
Implementation Method 1
exploiting the static nature of the seabed's specular reflections
Implementation Method 2
comparing successive local complex egress images to one or more reference complex ingress images to obtain successive normalized cross-correlation coefficients 'NCCCs'
Data Source
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AI summary
A method and system for facilitating navigation of an autonomous underwater vehicle (AUV) about an egress path that mirrors an ingress path. Complex return data during an ingress cycle are obtained and a corresponding complex image of the seabed along the ingress cycle is generated. Complex return data during an egress cycle are also obtained and a plurality of corresponding complex local images of the seabed along the egress cycle can be generated. The complex local images are compared to the complex ingress image to identify a normalized cross-correlation coefficient (NCCC). A maximum NCCC indicates that a position of the AUV in the along-track direction has been found. Successive local complex images from the egress cycle can be compared against the complex image from the ingress cycle as the AUV moves along the egress path to identify successive NCCCs, and monitored overtime to determine if the successive NCCCs are increasing or decreasing as the AUV moves along the egress path. The path of the AUV can be corrected to mirror the egress path to the ingress path based on the change of the NCCCs as compared to maximum NCCC.