MIMO Radar Signal Processing for Noise-Resilient Detection
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Solution Overview
Problem
MIMO radar systems face challenges in detecting small objects and distinguishing close objects due to noise and false detections, which are exacerbated by the spatially varying noise levels in different environments.
Innovation Solution
The system processes radar return data using a method that accounts for the spatial distribution of noise, identifying candidate detection RDE bins and confirmed detection azimuth bins through adaptive threshold adjustments based on noise levels at specific ranges and directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple antennas are used to enhance resolution, then detection capability improves, but system cost increases prohibitively
Solution Approach 1:
The patent segments the detection process into multiple stages: initial detection with a first set of antennas, followed by confirmation detection with a second set of antennas for candidate objects. This segmentation allows the system to achieve high detection resolution for confirmed objects while reducing overall system cost by not requiring all antennas to operate at full capacity simultaneously.
Solution Approach 2:
The patent applies local quality by allocating different antenna resources to different detection needs. The first set of antennas performs initial scanning with lower resource allocation, while the second set provides enhanced resolution for specific candidate objects. This localized resource allocation optimizes detection capability where needed while controlling overall system cost.
2Ease of operation
If a constant noise threshold is used, then processing simplicity is maintained, but detection accuracy deteriorates due to spatially varying noise levels
Solution Approach 1:
The patent implements dynamic noise threshold adjustment based on spatial location. Instead of using a constant threshold, the system adapts the noise threshold according to the specific spatial region being analyzed, accounting for varying noise levels across different areas. This dynamic approach maintains detection accuracy while managing processing complexity through structured adaptation.
Solution Approach 2:
The patent changes the noise threshold parameter based on spatial coordinates and environmental conditions. By adjusting this critical parameter dynamically across different regions of the detection space, the system achieves accurate detection despite varying noise levels, moving from a static to a adaptive parameter regime.
Data Source
AI summary
The present disclosure generally relates to methods, systems, apparatuses, and non-transitory computer readable media for processing radar signals of a MIMO radar system. By dynamically accounting for environmental noise both spatially and temporally, systems of the present disclosure can make radar-based systems more accurate and robust against noised-induced false detections, especially with respect to the use of sparse-receivers. More precisely, systems of the present disclosure may first process MIMO radar return data to identify one or more range-Doppler-elevation (RDE) bins exceeding a candidate detection criteria. Each identified candidate detection RDE bin may then be processed by a second search procedure to identify any azimuth bins (of the particular candidate detection RDE bin) exceeding a confirmed detection criteria. Each identified azimuth bin indicates a confirmed detection of a scatterer (e.g., an object) whose azimuth, elevation, range, and Doppler-velocity indicated by the identified azimuth bin and its associated candidate detection RDE bin.