Compressive Multiplexing for Radar Data Cube Processing
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Solution Overview
Problem
Current radar systems face challenges in processing-intensive target detection algorithms, which demand increased data processing capacity, leading to a significant burden on radar System on Chip (SoC) processors, and require efficient data compression techniques to reduce storage and transmission bandwidth.
Innovation Solution
The implementation of compressive multiplexing techniques that uniquely code data vectors in radar data cubes, combining them to generate compressed data vectors that require less storage, using methods like Doppler Division Multiplexing (DDM) and Costas coding, allowing for efficient data compression and decompression during processing and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar systems process complete radar data cubes using target detection algorithms, then target detection accuracy is maintained, but processing burden on radar SoC processors increases significantly
Solution Approach 1:
The patent extracts and transmits only selected portions of the radar data cube (such as range-Doppler maps or specific angular sectors) to the SoC processor, rather than transmitting the complete data cube. This extraction reduces the data volume requiring processing while maintaining the essential information needed for accurate target detection, thereby resolving the contradiction between detection accuracy and processing burden.
Solution Approach 2:
The patent segments the radar data cube into multiple portions or regions of interest before processing or transmission. By dividing the data cube into manageable segments (e.g., different range zones, Doppler bins, or angular sectors), the system can prioritize processing of critical segments while reducing the overall processing burden on the SoC processor, thus maintaining detection accuracy for important targets while reducing computational load.
2Reliability
If radar systems store and transmit complete radar data cubes, then data integrity is maintained, but storage and transmission bandwidth requirements increase
Solution Approach 1:
The patent extracts only the essential or most informative portions of the radar data cube for storage and transmission. By identifying and extracting key data elements (such as detected targets, critical reflections, or significant spectral components), the system maintains data integrity for these essential elements while significantly reducing the total quantity of data requiring storage and transmission bandwidth.
Solution Approach 2:
The patent applies partial processing or compression to the radar data cube, where not all data elements are processed or transmitted with equal fidelity. Critical data elements receive full processing and are transmitted with high fidelity, while less critical elements are compressed or summarized, achieving acceptable data integrity for the overall system while reducing total storage and transmission requirements.
Data Source
AI summary
Systems, methods, and circuitries are disclosed for compressing radar data. In one example, a method includes storing radar data in a memory, the radar data being stored in a data cube having a slow-time dimension, a fast-time dimension, and a channel dimension. The data cube is divided into one or more zones. For each zone a number of data matrices is selected based on a compression factor. Sets of data matrices containing the number of data matrices are formed and, for each set of data matrices, for each data matrix, the data vectors are coded to generate a coded data matrix. A coding for data vectors in a data matrix is the same and a coding for different data matrices is different. The coded data matrices are combined to generate a compressed data matrix for the zone and the compressed data matrices for the one or more zones are stored.