Radar Data Compression by Wiping Off Leakage and Strong Targets
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Solution Overview
Problem
Vehicular radars generate large amounts of data, which poses a challenge for efficient processing and transmission, particularly due to dominant leakage signals and strong targets close to the vehicle, leading to increased data size and complexity.
Innovation Solution
A radar data compression scheme that identifies and subtracts leakage and strong target signals based on distance thresholds, using a combination of time-domain differentiation and statistical encoding to reduce data size, allowing for lossless compression and efficient data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all radar signals are processed and transmitted, then complete radar information is maintained, but data size and transmission burden increase significantly
Solution Approach 1:
The patent extracts and removes dominant leakage signals and strong target signals from the radar data before compression. By identifying signals that exceed certain amplitude thresholds and removing them, the system reduces the data quantity while preserving the essential information about weaker, more distant targets. This extraction principle directly addresses the contradiction by eliminating redundant data that would otherwise increase transmission burden without adding valuable information.
Solution Approach 2:
The patent applies different processing qualities to different portions of the radar data. Strong signals near the vehicle are handled differently (removed or specially marked) compared to weaker signals from distant targets. This local differentiation allows the system to optimize data representation based on the specific characteristics of each signal region, maintaining information accuracy where needed while reducing overall data size through selective processing.
2Productivity
If compression is applied to reduce data size, then transmission efficiency improves, but data complexity and processing requirements increase
Solution Approach 1:
The patent performs preliminary processing by removing dominant leakage and strong target signals before applying compression algorithms. This preliminary action simplifies the subsequent compression task by eliminating the most significant variations in the data, allowing standard compression algorithms to work more efficiently on the remaining weaker signals. The complexity is front-loaded into a simple threshold-based removal process rather than requiring complex compression algorithms.
Solution Approach 2:
The patent changes the parameter representation of the radar data by removing dominant signals and representing the remaining data in a transformed space. This parameter change enables more efficient compression by focusing on the residual variations that contain the most valuable information about distant targets, rather than attempting to compress the full dynamic range of the original signals.
3Quantity of substance
If dominant leakage signals are removed, then data size reduces, but signal processing complexity increases
Solution Approach 1:
The patent replaces complex mechanical signal processing methods with a simpler computational approach. Instead of using sophisticated filtering or transformation methods to remove leakage signals, the system uses direct amplitude thresholding and comparison. This substitution of a simpler detection mechanism for a more complex one reduces the overall processing difficulty while achieving the same goal of removing dominant signals.
Solution Approach 2:
The leakage signal removal process is designed to be self-identifying through the use of amplitude thresholds. The system automatically identifies and removes signals that exceed certain amplitude levels without requiring external intervention or complex analysis. This self-service approach simplifies the processing by making the removal operation automatic and based on intrinsic signal properties rather than requiring complex external control.
Data Source
AI summary
For example, a radar data compressor may include an input to receive input digital raw data comprising digital samples of received radar signals at a plurality of receive (Rx) antennas; a raw data compressor configured to compress the input digital raw data into compressed digital data, for example, by wiping off from the input digital raw data one or more wiped-off signals, e.g., based on a wipe-off criterion applied to the input digital raw data; and a compressor output to provide compressed data including the compressed digital data, and signal parameter information defining the one or more wiped-off signals.