3D Feature Extraction from FMCW Radar Signals
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Solution Overview
Problem
Current FMCW radar systems face challenges in effectively extracting three-dimensional features from radar signals, particularly in detecting objects at short to medium ranges and identifying complex motion patterns, which limits their accuracy and efficiency in automated analysis.
Innovation Solution
The system employs a phased array FMCW radar system that processes sequential radar signal data frames to determine amplitude and phase changes, using a trained machine learning model, specifically a deep 3D convolutional neural network and long short-term memory network, to extract three-dimensional features from the radar signal data frames, enabling the detection of directional and volumetric changes over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional FMCW radar systems process sequential radar signal data frames to detect objects at short to medium ranges, then the system can identify objects within the specified range, but the extraction of three-dimensional features and detection of complex motion patterns remains inaccurate and inefficient
Solution Approach 1:
The patent replaces traditional signal processing methods with a deep learning-based machine learning model that processes sequential radar signal data frames. The model extracts three-dimensional features by analyzing amplitude and phase changes across multiple frames, enabling accurate detection of objects and complex motion patterns at short to medium ranges while improving computational efficiency through automated feature extraction.
Solution Approach 2:
The patent transforms radar signal parameters from raw amplitude and phase measurements to three-dimensional feature representations. By computing phase changes between sequential frames and feeding these transformed parameters into a deep learning model, the system achieves improved measurement precision for three-dimensional feature extraction while maintaining processing efficiency.
2Reliability
If the system processes sequential radar signal data frames to determine amplitude and phase changes, then the system can extract three-dimensional features, but the complexity of the processing system increases
Solution Approach 1:
The patent introduces a deep learning model as an intermediary component between the radar signal processing pipeline and the detection output. This model receives sequential data frames with amplitude and phase information, processes them through multiple layers of computation, and outputs extracted three-dimensional features. The intermediary model encapsulates the complexity of feature extraction, improving reliability while keeping the overall system architecture manageable through modular design.
Data Source
AI summary
Motion-related 3D feature extraction by receiving a set of sequential radar signal data frames associated with a subject, determining radar signal amplitude and phase for each data frame, determining radar signal phase changes between sequential data frames, and extracting, by a trained machine learning model, one or more three-dimensional features from the sequential radar signal data frames according to the radar signal amplitude and the radar signal phase changes between sequential data frames.