Split-Aperture Sonar Beamforming for 3D Position Accuracy
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Solution Overview
Problem
Current sonar imaging techniques produce 'fuzzy' output due to errors in data point positions, especially when multiple overlapping beams intersect a surface at high incidence angles, making it difficult to accurately resolve the exact position of objects in 3D space.
Innovation Solution
A method involving a sonar computing device that subdivides raw data into slices and uses split-aperture processing in two-dimensional space to correct beam positions by comparing phase data from multiple subarrays, interpolating the corrected positions onto an output grid, thereby improving the accuracy of beamformed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple overlapping beams are used to cover a larger viewing area, then the area of stationary object is improved, but the measurement precision deteriorates due to high incidence angle errors
Solution Approach 1:
The detector array is divided into multiple subarrays, each processing a specific angular sector. By segmenting the array and assigning different subarrays to different angular ranges, the system can maintain high measurement precision within each sector while collectively covering a broader viewing area through the combination of all subarray outputs.
Solution Approach 2:
Different subarrays are optimized for different angular regions, with each subarray having tailored beamforming parameters and processing characteristics suited to its specific angular sector. This local optimization ensures high precision measurements in each region while the aggregate coverage provides extensive viewing area.
2Productivity
If beamforming is performed on the entire detector array, then the productivity is improved, but the measurement precision deteriorates due to angular errors in overlapping beams
Solution Approach 1:
The beamforming process is segmented across multiple subarrays that can be processed independently and in parallel. This segmentation maintains high productivity through parallel processing while improving precision by dedicating each subarray to specific angular ranges, thereby eliminating the high incidence angle errors that plague full-array beamforming at extreme angles.
3Measurement precision
If the detector array is divided into multiple subarrays for angular correction, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The detector array is divided into multiple subarrays, each handling a specific angular sector. This segmentation improves angular position accuracy by ensuring that each subarray operates within its optimal angular range, avoiding the high incidence angle errors that occur in full-array beamforming. The modular subarray structure also allows for independent processing and optimization of each segment.
Solution Approach 2:
The system dynamically adjusts beamforming parameters such as phase shifts and amplitude weights for each subarray based on its specific angular sector. By changing these parameters locally for each subarray rather than using uniform parameters across the entire array, the system achieves higher angular precision while managing complexity through parameterization rather than structural complexity.
4Measurement precision
If phase data from multiple subarrays is used for correction, then the measurement precision is improved, but the loss of information increases due to the need to process and compare multiple data sets
Solution Approach 1:
Phase data from multiple subarrays are processed independently for their respective angular sectors and then combined. This segmentation approach improves precision by ensuring each subarray's phase data is optimized for its specific angular range, while the modular combination process minimizes information loss by preserving the unique phase information from each subarray rather than requiring complete data redundancy.
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 provides a 2D angular correction vector for each voxel, enhancing the precision of beamformed data and ensuring that only one data point represents the true position of a surface, reducing errors and improving image clarity.
Implementation Method 1
sending one or more sonar signal pulses into a volume of fluid, also known as insonifying the volume of fluid. Doing so causes objects within the insonified volume to reflect sound energy. One or more detector elements of a detector array may record the reflected sound energy.
Implementation Method 2
Beamforming generally relates to techniques for generating, from the raw data, a 3D array of values (e.g., magnitude and phase) corresponding to measurements within an insonified volume for a given ping.
Data Source
AI summary
Technologies for correcting beamformed data are disclosed. A sonar computing device receives, at two or more sets of two-dimensional (2D) subarrays of a multi-element detector array, raw data representing a three-dimensional (3D) volumetric view of a space. A first set of subarrays of the two or more sets of subarrays includes elements of the detector array along a first direction. A second set of subarrays of the two or more sets of subarrays includes elements of the detector array along a second direction. The raw data is subdivided into slices and beamformed. The beamformed data is corrected by, per slice, obtaining first phase data from the first set, obtaining second phase data from the second set, correcting a beam position of each beam in the first and second directions per voxel based on the first and second phase data, and interpolating the corrected beam positions to an output grid.