Radar Information Compression Using Bin Normalization and Quantization
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Solution Overview
Problem
Conventional light-based sensors, such as cameras and LIDAR, perform poorly in adverse weather conditions, limiting their effectiveness in autonomous and robotic systems.
Innovation Solution
Implementing radar systems with advanced signal processing techniques to enhance perception and navigation capabilities, including MIMO radar and FMCW radar, which utilize frequency-modulated continuous wave signals for improved range and speed estimation, and angle determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If light-based sensors (cameras and LIDAR) are used for autonomous perception, then the system can achieve good performance in normal conditions, but the reliability deteriorates under adverse weather conditions such as rain, snow, hail, or poor visibility
Solution Approach 1:
The patent transitions from light-based sensing to radio wave-based radar sensing, fundamentally changing the electromagnetic parameter (wavelength/frequency) to operate in a regime that penetrates adverse weather conditions. Radar waves with longer wavelengths can pass through rain, snow, and fog that block visible light and infrared wavelengths used by cameras and LIDAR.
Solution Approach 2:
The patent replaces optical/mechanical sensing systems (cameras and LIDAR) with electromagnetic wave-based radar systems. This substitution enables operation in adverse weather by using radio waves instead of visible light or infrared, which are blocked by precipitation and poor visibility conditions.
2Reliability
If radar systems with advanced signal processing are implemented, then the reliability and accuracy of environmental perception improve, but the complexity of the system increases
Solution Approach 1:
The patent segments the radar signal processing into distinct functional modules: frequency modulation for range estimation, Doppler processing for velocity measurement, and angle determination algorithms. This modular segmentation manages complexity by organizing processing tasks into separate, manageable stages rather than monolithic processing.
Solution Approach 2:
The patent applies frequency modulation to the transmitted radar signal before emission, encoding range information in advance. The FMCW (Frequency Modulated Continuous Wave) technique pre-structures the signal with known frequency variations, allowing range calculation from the frequency difference between transmitted and received signals without complex real-time processing.
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
Enhances the reliability of autonomous and robotic systems by providing accurate environmental perception in various weather conditions, ensuring safe and efficient operation.
Implementation Method 1
radar processor configured to process radar signals received from the plurality of receive antennas
Implementation Method 2
Frequency-Modulated Continuous Wave (FMCW) radar apparatus
Implementation Method 3
improved range and speed estimation
Data Source
AI summary
For example, a processor may be configured to generate compressed radar information by compressing radar values in a plurality of data bins of at least one radar processing dimension, the at least one radar processing dimension including a range dimension. For example, the processor may be configured to generate the compressed radar information by quantizing a plurality of normalized values corresponding to the radar values in the plurality of data bins. For example, a normalized value corresponding to a radar value in a data bin may be based on a normalization of the radar value with respect to a plurality of radar values in the data bin. For example, the processor may be configured to store the compressed radar information in a memory.