Centralized Sparse Radar Imaging for Bandwidth-Limited Networks
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Solution Overview
Problem
Conventional radar architectures with edge radar devices face limitations due to limited local signal processing capability, restricted data streaming, and high data volume that exceeds the capacity of local memories and bandwidth-limited networks, leading to reduced radar resolution and increased costs.
Innovation Solution
Implementing a centralized radar system with sparse antennas that utilize virtual aperture imaging and deep sensor fusion, enabling low-volume raw data processing by a centralized processor to generate high-resolution radar data and facilitate continuous data streaming over bandwidth-limited networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If edge radar devices process radar data locally with limited local memory, then processing speed is improved, but radar resolution deteriorates due to limited data cube size
Solution Approach 1:
The patent extracts the memory and processing functions from edge radar devices to a centralized location. Local processors and memories are removed from edge devices, and only minimal processing remains at the edge. The extracted data is transmitted to a centralized memory system that provides unlimited storage capacity for large data cubes, enabling high-resolution radar processing without the speed-resolution tradeoff.
Solution Approach 2:
The patent transitions from a distributed processing architecture to a centralized architecture, adding a new dimensional aspect to data handling. By moving memory and processing to a centralized dimension, the system achieves both fast local data capture and unlimited storage capacity, resolving the contradiction between processing speed and radar resolution.
2Quantity of substance
If raw radar data is transmitted over bandwidth-limited networks, then data volume is reduced, but data loss occurs due to network bandwidth limitations
Solution Approach 1:
The patent extracts only the essential raw radar data from edge devices for transmission, removing unnecessary processed data. By transmitting only the minimum required raw data over the network, the system reduces data volume to fit bandwidth constraints while preserving all information needed for high-resolution reconstruction at the centralized location.
Solution Approach 2:
The system performs preliminary data selection at the edge devices, identifying and extracting only the critical raw radar data that must be transmitted. This preliminary action ensures that network bandwidth is used efficiently for transmitting essential information while preventing data loss by capturing all necessary raw data before transmission.
3Productivity
If each edge radar device has complete RF front-end and local processing unit, then processing capability is improved, but system cost increases
Solution Approach 1:
The patent merges the memory and processing functions of multiple edge radar devices into a single centralized system. Instead of each edge device having its own complete RF front-end and processing unit, the system combines these resources centrally, reducing redundancy and overall system cost while maintaining or enhancing processing capability through shared resources.
Solution Approach 2:
The centralized processing unit serves multiple edge radar devices simultaneously, providing universal processing capability. This multi-functional approach allows a single processing unit to handle data from all edge devices, eliminating the need for dedicated processing units at each edge location and thereby reducing system cost while maintaining high processing capability.
Data Source
AI summary
An apparatus comprising an interface and a processor. The interface may receive radar data from a plurality of radar devices. The radar device may each be implemented at a respective edge site. The radar data may comprise sparsity. The processor may be configured to receive the radar data corresponding to each of the radar devices from the interface, perform virtual aperture imaging in response the radar data, and generate high resolution radar data based on the virtual aperture imaging. The sparsity may be used to capture the radar data may enable the radar data to have a low data volume. The virtual aperture imaging may be configured to enable the high resolution radar data to be generated from the low data volume. The processor may enable high data throughput at a central location for the radar data generated at the respective edge sites.