Sonar Data Compression Using Sparse Signal Extraction
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Solution Overview
Problem
Existing sonar technologies face challenges in efficiently transmitting and storing sparse data from sonar signals scattered from surfaces, particularly in scenarios where data transmission bandwidth is limited, such as in remotely operating vehicles, due to the high volume of raw data generated by sonar arrays.
Innovation Solution
The method involves reducing the amount of raw data sent to a sonar beamformer or storage system by converting analog signals from a sonar array into a greatly reduced set of digital signals, using techniques like compressing phase and intensity information, and employing cheaper comparator circuits instead of expensive ADCs to record and compress data, thereby minimizing data transmission while maintaining significant image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw data from sonar arrays is transmitted to beamformers or stored, then complete sonar imaging capability is maintained, but data transmission bandwidth and storage requirements become excessively high
Solution Approach 1:
The patent extracts only the essential features from raw sonar data by implementing sparsity-based compression. It identifies and retains only the significant signal components that contribute to image quality, discarding redundant information. This extraction approach maintains measurement precision while dramatically reducing data volume for transmission and storage.
Solution Approach 2:
The patent segments the sonar data processing into distinct stages: raw data acquisition, sparsity-based compression, and reconstructed image formation. By segmenting the data stream and applying compression algorithms to identify and retain only significant components, it reduces overall data volume while preserving essential imaging information.
2Productivity
If data compression is applied to reduce transmission bandwidth, then data transmission efficiency improves, but sonar image quality may degrade
Solution Approach 1:
The patent changes the parameter representation of sonar data by transforming raw data into a sparse domain representation. It applies compression algorithms that identify significant signal components and represents the data using fewer parameters, thereby improving transmission efficiency while maintaining image quality through intelligent parameter selection and retention.
3Measurement precision
If expensive ADCs are used to record high-resolution sonar data, then measurement precision is maintained, but system cost increases
Solution Approach 1:
The patent replaces expensive, high-resolution ADCs with cheaper, lower-resolution ADCs. By using sparsity-based compression techniques, it compensates for the reduced ADC resolution and reconstructs high-quality sonar images from the lower-resolution data, thereby reducing system cost while maintaining measurement precision through intelligent signal processing.
Data Source
AI summary
In a sonar system using a large array multielement sonar detector, the raw phase and intensity data is reduced to less than three bits per channel per slice for each of the detectors in the multielement array before the raw data is transmitted to a beamformer for transforming the data to information about the spatial positions of objects reflecting the sonar signals.