Radar Eigenspace Analysis With DFT for Nearby Target Detection
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Solution Overview
Problem
Current radar detection methods, such as MUSIC and DFT, are computationally inefficient and struggle with detecting nearby objects due to transmitter-to-receiver leakage, limiting their suitability for real-time applications like automotive radar and accuracy in identifying small or angled targets.
Innovation Solution
The proposed solution combines eigenspace analysis with DFT and other spectral analysis techniques, employing pre-processing to identify relevant areas of interest and eliminate non-relevant portions, using a radar apparatus with signal generators, antennas, and processors to calculate covariance matrices and determine eigenvectors for improved target attribute detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MUSIC algorithm is used for radar target detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the eigenspace analysis by identifying and processing only relevant portions corresponding to areas of interest containing potential targets, rather than analyzing the entire eigenspace. This is achieved through pre-processing steps that identify areas of interest before performing eigenspace analysis, thereby reducing computational complexity while maintaining detection accuracy for relevant targets.
Solution Approach 2:
The patent extracts and eliminates non-relevant portions of the eigenspace from consideration by identifying areas of interest through pre-processing. Only the extracted relevant portions corresponding to potential target locations undergo eigenspace analysis, reducing the overall computational burden while preserving measurement precision for detected targets.
2Device complexity
If conventional DFT-based processing is used, then device complexity is reduced, but measurement precision deteriorates for nearby objects
Solution Approach 1:
The patent performs preliminary action by identifying areas of interest through pre-processing steps before applying eigenspace analysis. This pre-processing identifies potential target locations, allowing the system to focus computational resources on relevant regions and improve nearby target detection accuracy without requiring full eigenspace analysis across all possible locations.
Solution Approach 2:
The patent introduces an intermediary approach by combining DFT-based area identification with eigenspace-based target detection. The DFT serves as an intermediary step that identifies areas of interest, which then guide the more computationally intensive eigenspace analysis to specific regions, achieving improved precision for nearby objects without the full computational burden of conventional MUSIC.
3Measurement precision
If full eigenspace analysis is performed, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent segments the eigenspace processing by dividing it into pre-processing identification of areas of interest followed by focused eigenspace analysis only in those identified regions. This segmentation enables real-time processing by avoiding computation in empty or non-relevant regions while maintaining high measurement precision for actual targets.
Solution Approach 2:
The patent applies partial action by performing eigenspace analysis only on identified areas of interest rather than the complete eigenspace. This partial processing approach achieves sufficient measurement precision for detected targets while dramatically improving productivity and enabling real-time automotive radar applications.
4Device complexity
If ultrasonic sensors are used instead of radar, then device complexity is reduced, but measurement precision deteriorates for small or angled targets
Solution Approach 1:
The patent changes the detection parameter from acoustic waves (ultrasonic) to electromagnetic waves (radar), which fundamentally improves measurement precision for small or angled targets. Radar signals maintain directional properties that allow accurate detection of small targets and targets with various surface orientations, unlike ultrasonic sensors which suffer from sound wave deflection.
Data Source
AI summary
Methods, systems, computer-readable media, and apparatuses for determining one or more attributes of at least one target based on eigenspace analysis of radar signals are presented. In some embodiments, a subset of eigenvectors to use for forming a signal or noise subspace is identified based on principal component analysis. In some embodiments, the subset of eigenvectors is identified based on estimating the total number of targets using a discrete Fourier transform (DFT) or other spectral analysis technique. In some embodiments, a DFT is used to identify areas of interest in which to perform eigenspace analysis. In some embodiments, a DFT is used to estimate one attribute of a target, and eigenspace analysis is performed to estimate a different attribute of the target, with the results being combined to generate a multi-dimensional representation of a field of view.