Sparse Cross-Shaped 3D Imaging Sonar Array Optimization
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Solution Overview
Problem
Existing three-dimensional acoustic imaging sonar systems using planar arrays are bulky, expensive, and consume high power due to the large number of transducers, making them unsuitable for miniaturized portable applications in complex environments.
Innovation Solution
A sparse optimization method for cross-shaped three-dimensional imaging sonar arrays is developed, utilizing a beam pattern construction and energy function optimization with simulated annealing to reduce the number of array elements while maintaining performance, by adjusting weight coefficients and array element positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If planar arrays are used to achieve three-dimensional sonar imaging, then long observation distance and high resolution are obtained, but the number of transducers becomes very large resulting in bulky, heavy, expensive, and high power consumption systems
Solution Approach 1:
The patent extracts only the essential array elements needed for 3D imaging by transitioning from a complete planar array to a sparse cross-shaped array. By removing redundant transducers and retaining only the critical elements along two perpendicular axes, the system achieves the required imaging resolution with dramatically fewer components, directly resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent transforms the traditional two-dimensional planar array into a cross-shaped configuration that effectively utilizes spatial dimensions. By arranging elements along two perpendicular linear arrays intersecting at their centers, the system creates a three-dimensional sensing capability from a sparse two-dimensional structure, maintaining imaging resolution while reducing the total number of elements.
2Device complexity
If the number of array elements is reduced for miniaturization, then portability is improved, but detection performance in complex environments may deteriorate
Solution Approach 1:
The patent applies parameter changes by introducing variable weight coefficients to each array element in the cross-shaped configuration. Through optimized weighting schemes, the system compensates for the reduced number of elements by adjusting the contribution of each transducer, thereby maintaining detection performance and reliability while achieving miniaturization and portability.
3Device complexity
If cross-shaped arrays are used instead of planar arrays, then the number of array elements is reduced, but there is room for further optimization in terms of number of elements and observation field performance
Solution Approach 1:
The patent introduces dynamic optimization through simulated annealing algorithms that adjust array element positions and weight coefficients. This dynamic approach allows the cross-shaped array to be fine-tuned for optimal performance across different observation fields and scenarios, resolving the contradiction by enabling adaptive optimization of both element count and field performance rather than relying on fixed configurations.
Data Source
AI summary
The present invention a sparse optimization method based on cross-shaped three-dimensional imaging sonar array, comprising the following steps: first, constructing a beam pattern simultaneously applicable to a near field and a far field based on a cross-shaped array; then, constructing an energy function required by sparse optimization according to the beam pattern; then, introducing an array element position disturbance into a simulated annealing algorithm to increase the degree of freedom of the sparse process and increase the sparse rate of the sparse array, and using the simulated annealing algorithm to sparse optimization of the energy function; finally, after optimization, a sparse optimization cross-shaped array is obtained. The present invention ensures that the three-dimensional imaging sonar system has the desired performance at any distance, and greatly reduces the hardware complexity of the system. It provides an effective method to achieve high performance and ultra-low complexity 3D imaging sonar system.