Machine Learning Sonar Image Processing
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Solution Overview
Problem
Conventional synthetic aperture sonar (SAS) signal processing techniques require significant memory and processor resources, limiting their efficiency in generating high accuracy sonar images.
Innovation Solution
The integration of machine learning models within autonomous underwater devices to process and generate sonar image data, allowing for cooperative operation among multiple devices to reduce processing demands and enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional signal processing techniques are used to generate sonar images, then image accuracy is improved, but memory and processor resources are significantly increased
Solution Approach 1:
The patent replaces conventional mechanical signal processing algorithms with a machine learning-based system. The machine learning model processes sonar return data to generate sonar images, substituting the traditional computationally intensive signal processing mechanisms with a neural network that can achieve comparable or superior image accuracy with reduced processing resources.
Solution Approach 2:
The patent changes the fundamental parameters of the processing system by transitioning from deterministic signal processing algorithms to probabilistic machine learning models. This parameter change allows the system to achieve high image accuracy while reducing the computational resource requirements through learned optimizations rather than exhaustive algorithmic processing.
2Productivity
If machine learning models are integrated into autonomous underwater devices, then computational resource efficiency is improved, but device complexity is increased
Solution Approach 1:
The patent implements a universal machine learning processing framework that can be applied across multiple autonomous underwater devices. The same machine learning model architecture and training methodology can be deployed on different devices, providing multi-functionality and reducing overall system complexity despite the advanced processing capabilities.
Solution Approach 2:
The patent uses pre-trained machine learning models that can be copied and deployed on multiple autonomous underwater devices. Rather than training separate models for each device, the system uses a centralized training approach where the learned models are replicated across devices, reducing the complexity burden on individual devices while maintaining high processing efficiency.
3Measurement precision
If multiple autonomous underwater devices operate cooperatively, then image quality is enhanced, but coordination complexity is increased
Solution Approach 1:
The patent merges the machine learning processing capabilities of multiple autonomous underwater devices into a coordinated swarm operation. By combining the computational power and sensor data from multiple devices working together, the system enhances image quality through aggregated information while the machine learning models provide standardized processing that simplifies coordination compared to custom algorithms for each device.
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 computational resources needed for sonar image generation, improves image detail, and enables more efficient data processing and object detection in aquatic environments.
Implementation Method 1
each receiver element is configured to generate a signal responsive to detecting sound energy in an aquatic environment
Implementation Method 2
The second autonomous underwater device includes one or more sonar transducers configured to emit one or more pings into the aquatic environment
Implementation Method 3
provide the input data to an on-board machine learning model to generate model output data. The model output data includes sonar image data based on the sound energy
Data Source
AI summary
An autonomous underwater device includes one or more receiver arrays. Each receiver array includes a plurality of receiver elements, and each receiver element is configured to generate a signal responsive to detecting sound energy in an aquatic environment. The autonomous underwater device also includes one or more processors coupled to the one or more receiver arrays and configured to receive signals from the receiver elements, to generate input data based on the received signals, and to provide the input data to an on-board machine learning model to generate model output data. The model output data includes sonar image data based on the sound energy, a label associated with the sonar image data, or both.