Radar Object Recognition Using Frequency Domain Signal Classification
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Solution Overview
Problem
Existing object recognition systems using radar struggle with accuracy in environments with varying light conditions, such as darkness or complex lighting, due to the reliance on image recognition methods that require clear images.
Innovation Solution
An object recognition method and system that utilizes radar echo signals to generate frequency domain signals, updates a primary classifier using these signals and auxiliary data, and generates object classification data to recognize objects independently of light conditions, employing a combination of radar equipment, an auxiliary sensor, and a controller to perform real-time training and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image recognition is used to identify objects, then recognition accuracy can be high in clear light conditions, but recognition reliability deteriorates in dark or complicated light environments
Solution Approach 1:
The patent replaces the optical-based image recognition system with a radar-based detection system. The radar system transmits electromagnetic waves and processes echo signals to detect objects, completely eliminating dependence on ambient light conditions. This substitution of detection mechanism (from optical to electromagnetic wave reflection) resolves the contradiction by maintaining both high accuracy and reliability across all lighting environments.
Solution Approach 2:
The patent changes the detection parameter from optical intensity (light-dependent) to radar echo signal characteristics (light-independent). By transforming the input data from images to radar signals and processing them through frequency domain analysis and classification algorithms, the system achieves consistent performance regardless of illumination conditions, thereby resolving the reliability issue in varying light environments.
2Reliability
If radar is used to detect objects, then recognition can be performed regardless of light conditions, but recognition accuracy remains low
Solution Approach 1:
The patent implements a preliminary training phase where the classification model is trained using radar echo signals paired with corresponding image data and labels. This pre-training establishes a robust mapping between radar signals and object identities. During actual detection, the pre-trained model processes radar echoes to achieve high accuracy, thus resolving the accuracy issue while maintaining the light-independent reliability advantage of radar.
Solution Approach 2:
The patent introduces an intermediary training process that bridges radar signals and object identification. By using images and labels as intermediate training data to teach the classifier to interpret radar echoes, the system transforms the raw radar signals into accurate object recognition results, thereby improving accuracy without compromising the inherent reliability of radar in various lighting conditions.
3Measurement precision
If real-time object recognition is performed using radar and machine learning, then accurate classification can be achieved, but system complexity increases
Solution Approach 1:
The patent segments the object recognition task into distinct functional modules: radar signal transmission, echo signal reception, frequency domain transformation, classification processing, and result output. Each module performs a specific function, making the overall complex system manageable and maintainable. This modular segmentation achieves accurate real-time recognition while controlling system complexity through clear functional division.
Solution Approach 2:
The patent designs a multi-functional controller that integrates radar signal processing, frequency domain transformation, and machine learning classification capabilities in a single device. This universal controller performs multiple functions (signal reception, transformation, classification, and output) that would otherwise require separate systems, thereby achieving accurate real-time recognition while minimizing overall system complexity through functional integration.
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
The system achieves accurate object recognition without being affected by ambient light, providing reliable results in various environments by leveraging radar technology and machine learning algorithms to classify objects based on Doppler spectrogram data.
Implementation Method 1
the radar can be used to detect objects regardless of day or night
Implementation Method 2
receive a first echo signal upon detecting a first object
Implementation Method 3
generating a frequency domain signal according to the first echo signal
Data Source
AI summary
An object recognition method includes generating a first frequency domain signal according to a first echo signal, updating at least one parameter of a primary classifier according to the first frequency domain signal and a training target corresponding to the first frequency domain signal, generating a second frequency domain signal according to a second echo signal, and generating object classification data corresponding to the second frequency domain signal according to the second frequency domain signal and the at least one parameter of the primary classifier. The object classification data is associated with presence of a second object.