Echolocation Range-Bin Compression for Memory-Efficient Target Detection
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Solution Overview
Problem
Echolocation devices generate large amounts of data that require significant computer memory and processor bandwidth for storage and processing, posing challenges in efficient data management and target detection.
Innovation Solution
The system partitions the range bins of echolocation data into multiple sections and combines data from certain sections using coherent addition, storing only the combined data in memory, which reduces storage requirements and improves processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all range bin data is stored in memory for complete target detection, then target detection accuracy is improved, but memory storage requirements increase
Solution Approach 1:
The patent divides the range bins into multiple sections (first section, second section, third section, fourth section) and processes each section separately. Only selected sections (second and fourth sections) are stored in memory, while other sections (first and third sections) are processed and discarded. This segmentation allows the system to maintain target detection accuracy for critical sections while reducing overall memory storage requirements by approximately 50%.
2Reliability
If all range bin data is processed to ensure complete target detection, then detection reliability is improved, but processor bandwidth requirements increase
Solution Approach 1:
The patent extracts and processes only the necessary sections (second and fourth sections) for storage and further processing, while the first and third sections are processed and discarded. This extraction approach maintains detection reliability for the stored sections while significantly reducing the total processor bandwidth requirements by avoiding redundant processing of all range bin data.
3Productivity
If data from multiple sections is combined using coherent addition, then storage efficiency is improved, but signal-to-noise ratio may be affected
Solution Approach 1:
The patent combines data from the second section and fourth section using coherent addition to create composite data structures that are stored in memory. This merging approach achieves storage efficiency by representing multiple range bins more compactly while maintaining the signal-to-noise ratio through coherent processing that preserves signal characteristics. The coherent addition ensures that signal components add constructively while noise components do not perfectly correlate, maintaining detection quality.
Data Source
AI summary
Systems and methods for compressing data are provided. An example method includes generating range data based on digital data of received signals resulting from transmitted radar chirps being reflected, in which the range data is distributed among range bins; partitioning the range bins into multiple sections, each of which includes a respective set of range bins, in which a first section includes range data spanning a closest range and a last section includes range data spanning a farthest range; merging range data in a section with range data in two adjacent sections that are between the first and last sections; storing merged range data in the two other sections in respective regions of a memory; processing the merged range data to generate range-velocity data and/or range-angle data; and analyzing such data to determine whether a target is present in the section.