Radar Pre-Processing With Attention-Based Range-Bin Selection
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Solution Overview
Problem
Existing radar systems face high computational and memory load due to the constant processing of large data sets in spectrogram-based data pre-processing for automated target recognition, which is not efficiently managed by current methods.
Innovation Solution
Implement an 'attention' mechanism that dynamically adjusts data pre-processing parameters to focus on data of interest, disentangling features and allowing flexible selection of sample data subsets for intelligent processing, reducing memory and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectrogram-based pre-processing is used to generate comprehensive feature representations, then target recognition accuracy is improved, but computational load and memory consumption increase significantly
Solution Approach 1:
The patent segments the continuous data stream into discrete time windows and divides the feature extraction process into separate stages. Instead of processing all data at once to create comprehensive spectrograms, the system processes data in segmented portions, extracting relevant features from each segment independently. This reduces the computational load at any given moment while maintaining recognition accuracy through sequential processing.
Solution Approach 2:
The patent extracts only the most relevant features from the radar data rather than computing all possible spectrogram features. By identifying and extracting only the critical features needed for target recognition, the system reduces computational complexity and memory requirements while preserving the essential information needed for accurate detection.
2Measurement precision
If spectrograms with fixed resolution are generated for all data, then feature representation completeness is improved, but memory consumption remains constant and high
Solution Approach 1:
The patent applies local quality by using variable resolution spectrograms where the time and frequency resolution is adapted locally based on the content of the data. In regions with significant target information, higher resolution is applied, while in regions with less important data, lower resolution is used. This approach maintains feature representation completeness for critical areas while reducing overall memory consumption.
Solution Approach 2:
The patent introduces dynamic adaptation of spectrogram parameters based on the incoming data characteristics. The system dynamically adjusts the number of bins, time window size, and frequency resolution according to the detected signal properties and environmental conditions. This dynamic approach allows the system to maintain high feature representation quality when needed while reducing memory usage during periods of lower complexity.
3Measurement precision
If high-resolution spectrograms are generated continuously, then detection precision is improved, but power consumption increases constantly
Solution Approach 1:
The patent implements periodic processing where high-resolution spectrogram generation is performed only at specific intervals or when triggered by detection events, rather than continuously. The system uses lower-resolution processing for routine monitoring and switches to high-resolution processing periodically or when anomalies are detected, thereby maintaining detection precision while significantly reducing average power consumption.
Solution Approach 2:
The patent dynamically changes processing parameters such as spectrogram resolution, time window size, and update frequency based on environmental conditions and detection requirements. When the environment is stable and low-risk, lower resolution and less frequent processing are used, reducing power consumption. When targets are detected or environmental complexity increases, the system increases resolution and processing frequency to maintain detection precision.
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 solution effectively conserves memory and power by selectively processing only relevant data, optimizing resource usage and enhancing the efficiency of data pre-processing for machine learning models in radar systems.
Implementation Method 1
A radar system consists of a transmitter producing electromagnetic waves in the radio or microwaves domain
Implementation Method 2
Radio waves (pulsed or continuous) from the transmitter reflect off the objects and return to the receiver
Implementation Method 3
applying a Fourier transform to the intermediate frequency signal
Implementation Method 4
The intermediate frequency signal may be generated by mixing the transmitted signal and the received signal
Data Source
AI summary
A system and method for pre-processing data for further processing by a machine learning model. The pre-processing comprises generating sample data from a transmitted signal and a received signal over a time period, allocating the sample data to a plurality of (L) range bins, selecting a first subset of (M) the range bins, generating evaluation data based on an evaluation of the sample data of the first subset of (M) the range bins against one or more criteria, selecting a second subset of (N) range bins based on the evaluation data, generating calculated data based on the sample data of the second subset of (N) range bins, and providing the evaluation data and the calculated data to a machine learning model with temporal dynamics for further processing.