Radar Sensor Data Encoding With High-Base Compression
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Solution Overview
Problem
Conventional radar sensors face increased memory requirements and chip size due to high data volumes from improved distance and angular resolution, necessitating efficient data compression methods to reduce memory needs while maintaining accuracy.
Innovation Solution
The method employs an exponential representation with a base greater than 2, such as 4 or 8, to increase resolution for stored values, allowing for reduced mantissa and exponent lengths, thereby saving memory space, and dynamically adjusts the function selection based on value distribution for optimal resolution without additional memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of reception channels and vector dimensions are increased to improve distance and angular resolution, then measurement precision is improved, but memory requirements and chip size increase
Solution Approach 1:
The patent changes the base parameter in the exponential representation from b=2 to b>2 (such as b=4 or b=8). This parameter change allows achieving the same dynamic range with shorter mantissa and exponent lengths, thereby reducing memory requirements while maintaining measurement precision.
Solution Approach 2:
The patent uses a simplified exponential representation format that captures the essential information needed for radar signal processing. By representing real values in the form r=m·b−k with b>2, the system creates a compressed copy of the data that retains sufficient precision for distance and angular resolution while occupying less memory space.
2Measurement precision
If the mantissa and exponent lengths are increased to achieve higher resolution, then measurement precision is improved, but memory requirements increase
Solution Approach 1:
The patent changes the base parameter from 2 to a larger value (b>2), which fundamentally alters the relationship between mantissa/exponent lengths and achievable resolution. This allows achieving higher resolution with shorter representations, resolving the contradiction between precision and memory space.
Data Source
AI summary
A method for encoding and storing digital data, which include a plurality of real values, in a signal processing unit of a radar sensor in which at least one real value r in an exponential representation in the form r=m·b−k is stored, where m is a digital mantissa having a length p, b is a base, and k is a positive number that is encoded as a digital number having a length q. An exponential representation with b>2 is used for the compressed storage of the values r.