Radar Video Compression Using Per-Pixel Doppler Prediction
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Solution Overview
Problem
Current data compression techniques for radar video data require significant computational resources and are inefficient, especially in vehicle navigation systems, where accurate prediction of pixel motion across sequential frames is challenging, leading to high computational costs and potential errors.
Innovation Solution
The proposed method uses per-pixel Doppler measurements for partial frame prediction, generating a compressed radar data file by calculating differences between predicted and actual radar representations, which reduces the data size and computational requirements, allowing efficient storage and transmission of radar data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data compression techniques are used for radar video data, then data size is reduced, but computational resources and processing time increase significantly
Solution Approach 1:
The patent performs preliminary motion estimation using Doppler measurements before compression. By calculating range rates and predicting pixel positions in advance, the system prepares motion compensation data that simplifies subsequent compression operations, reducing the computational burden during actual compression while maintaining high compression ratios
Solution Approach 2:
The patent replaces conventional mechanical/image-based motion estimation with Doppler-based velocity measurement. Instead of analyzing pixel intensity changes to estimate motion, the system uses Doppler frequency shifts to directly measure radial velocity, substituting a more efficient physical measurement approach that reduces computational complexity
2Measurement precision
If accurate motion prediction is achieved using conventional methods, then prediction precision improves, but computational cost increases
Solution Approach 1:
The patent makes the radar signal itself serve the dual purpose of both detection and motion measurement. The Doppler information, already inherent in the radar return signal, is utilized directly for velocity estimation without requiring separate motion estimation algorithms, allowing the system to achieve accurate motion prediction using the same data that detects objects
Solution Approach 2:
The patent changes the parameter used for motion estimation from spatial pixel intensity variations to temporal Doppler frequency shifts. By measuring velocity through Doppler frequency rather than analyzing changes in image pixel values, the system achieves accurate motion prediction with significantly reduced computational requirements
3Quantity of substance
If more computational resources are allocated to compression, then compression ratio improves, but processing speed decreases
Solution Approach 1:
The patent performs preliminary organization of radar data into range-Doppler maps and calculates range rates before compression. By pre-processing the data to extract motion information and structure it according to predicted pixel positions, the system enables faster compression operations with improved compression ratios, as the actual compression needs to process only the residual differences rather than raw data
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient compression and decompression of radar data, reducing computational resources needed for processing and storage, while maintaining accurate motion prediction, thus improving radar data management and vehicle navigation performance.
Implementation Method 1
receiving first radar data from a radar unit coupled to a vehicle operating in an environment
Implementation Method 2
Each first pixel includes a Doppler score and a backscatter value. For each first pixel, determining a range rate based on a Doppler score for the first pixel. The range rate indicates a radial direction motion for a surface represented by the first pixel.
Data Source
AI summary
Example embodiments relate to radar image video compression techniques using per-pixel Doppler measurements, which can involve initially receiving radar data from a radar unit to generate a radar representation that represents surfaces in the environment. Based on Doppler scores in the radar representation, a range rate can be determined for each pixel that indicates a radial direction motion for a surface represented by the pixel. The range rates and backscatter values can then be used to estimate a radar representation prediction for subsequent radar data received from the radar unit, which enables a generation of a compressed radar data file that represents the difference between the radar representation prediction and the actual representation determined for the subsequent radar data. The compressed radar data file can be stored in memory, transmitted to other devices, and decompressed and used to train models via machine learning.