Radar Signal Processor Target Identification via Feature Extraction
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Solution Overview
Problem
Conventional signal processors face challenges in accurately identifying target objects from echo signals due to the requirement of a probability model expressing amplitude transitions, leading to erroneous identification when low output probability transitions are observed.
Innovation Solution
A signal processor is designed with an extracting module to extract partial sample sequences from echo signals, a characteristic amount calculating module to calculate features like rising and falling sample counts and amplitude values, and an identifying module to compare these features with stored type-based data to identify target objects, while also accounting for deterioration degrees and unnecessary object identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a probability model expressing amplitude transitions is used to identify target objects, then the identification process can be automated, but erroneous identification occurs when low output probability transitions are observed
Solution Approach 1:
The patent extracts specific characteristic amounts (rising sample number, falling sample number, peak amplitude, integral value) from the echo signal waveforms and uses these extracted features for identification instead of relying on the probability model's automated but unreliable amplitude transition analysis. This extraction of key features resolves the contradiction by providing automated identification through feature comparison while maintaining reliability through physically meaningful characteristics.
2Ease of operation
If conventional probability model-based identification is used, then target identification can be performed, but the system requires complex probability models and stored data
Solution Approach 1:
The patent extracts essential characteristic amounts from echo signals and stores pre-calculated type-based data for comparison. This approach maintains identification capability while reducing complexity by focusing on key waveform features (rising/falling samples, peak amplitude, integral value) rather than requiring complex probability models and extensive stored probability distributions.
Solution Approach 2:
The patent changes the identification approach from probability-based amplitude transition analysis to direct comparison of extracted characteristic amounts with type-based reference data. This parameter change simplifies the system by using deterministic feature comparison instead of probabilistic modeling, reducing device complexity while maintaining ease of operation.
3Measurement precision
If characteristic amounts are extracted and compared with type-based data, then identification accuracy is improved, but additional processing steps are required
Solution Approach 1:
The patent segments the echo signal waveform into distinct characteristic components (rising portion, falling portion, peak portion) and extracts specific measurements from each segment. This segmentation improves identification accuracy by focusing on meaningful waveform features while organizing the processing into manageable, systematic steps that reduce overall processing complexity.
Data Source
AI summary
In order to accurately identify a target object, a signal processor is provided, which includes an extracting module configured to extract, from echo sample sequences, a plurality of samples caused by the target object as a partial sample sequence, a characteristic amount calculating module configured to calculate a characteristic of the partial sample sequence as a characteristic amount, a memory configured to store a plurality of type-based data that are data as comparison targets of the characteristic amount and correspond to types from which the target object is identified, and an identifying module configured to compare the characteristic amount with each of the plurality of type-based data and, based on the comparison result, identify the target object corresponding to the partial sample sequence for which the characteristic amount is calculated.