Radar Object Recognition Using Cadence Spectrum Analysis
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Solution Overview
Problem
Current radar-based object recognition systems face limitations in accuracy and recognition speed due to reliance on size and velocity measurements, which are not sufficient for precise identification in various environments.
Innovation Solution
The method involves generating Doppler spectrogram data from echo signals, transforming time domain data into cadence spectrum data, and combining these to acquire 1D/2D cadence spectrum data, which are then used to extract cadence features for object recognition, employing a radar and processor system for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar-based object recognition relies on size and velocity measurements, then the system can operate in various environmental conditions, but the recognition accuracy and speed are limited
Solution Approach 1:
The patent transforms the recognition parameters from traditional size and velocity measurements to cadence spectrum features. By changing the measurement parameters to cadence characteristics (frequency domain representation of motion patterns), the system achieves higher recognition accuracy while maintaining operational capability in various environmental conditions
Solution Approach 2:
The patent replaces traditional mechanical measurement approaches (direct size and velocity measurement) with signal processing-based cadence analysis. By substituting mechanical measurement systems with spectral analysis methods, the system achieves improved precision without proportionally increasing device complexity
2Speed
If radar uses Doppler effect to measure velocity, then the system can function in night and harsh environments, but the recognition speed is insufficient for precise identification
Solution Approach 1:
The patent transitions from time-domain velocity measurement to frequency-domain cadence spectrum analysis. By adding the frequency dimension to the analysis, the system can simultaneously achieve fast recognition speed through spectral features and high precision through detailed cadence pattern identification
Solution Approach 2:
The patent performs preliminary transformation of echo signals into Doppler spectrogram data and cadence spectrum data before final object identification. This pre-processing in the frequency domain prepares the data for faster and more precise recognition, reducing the computational burden during the actual identification phase
3Measurement precision
If the system transforms N sets of time domain data into N sets of cadence spectrum data, then the object recognition accuracy is enhanced, but the processing complexity increases
Solution Approach 1:
The patent transforms N sets of time domain data into N sets of cadence spectrum data, which is more processing than traditional single-velocity analysis but enables comprehensive cadence feature extraction. This partial transformation approach focuses computational resources on the most discriminative frequency components, achieving high accuracy without excessive processing time
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 enhances object recognition accuracy and speed by leveraging cadence features, allowing for the differentiation of objects based on their movement patterns, independent of environmental conditions.
Implementation Method 1
The radar is used to receive an echo signal, the echo signal being associated with an object
Implementation Method 2
use Doppler effect to measure the velocity of the target object according to the frequency shift between the transmitted wave and the reflected wave
Data Source
AI summary
An object recognition method includes generating Doppler spectrogram data according to an echo signal, the echo signal being relating to an object; transforming N sets of time-domain data of the Doppler spectrogram data corresponding to N velocities into N sets of cadence spectrogram data, respectively; combining the N sets of spectrogram data to obtain 1D/2D cadence spectrum data, and acquiring a series of cadence feature from the 1D/2D cadence spectrum data to recognize the object.