Radar Sensor Angular Spectrum for Precipitation False-Alarm Suppression
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Solution Overview
Problem
Radar sensors face challenges in distinguishing between actual targets and false detections, particularly in precipitation conditions, leading to increased false alarm rates due to weak reflections from rain or snow, which are not localized and require complex hardware or processing solutions.
Innovation Solution
The method forms an angular spectrum for a partial data set of reflected signals, determines a noise level based on signal strength, and sets a detection threshold to differentiate between noise and actual targets by comparing the angular spectrum with this threshold, adapting it to ambient conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the detection threshold is lowered to detect runners with different movement speeds, then detection sensitivity is improved, but false alarm rate increases in heavy rain
Solution Approach 1:
The patent segments the detection space into multiple speed bins to separately analyze reflections from different movement speeds. This allows the system to detect runners whose body parts move at different speeds while distinguishing them from uniform precipitation patterns that appear across all speed bins.
Solution Approach 2:
The patent introduces an angular spectrum dimension to the detection process. By analyzing the angular distribution of reflections and comparing it against a detection threshold, the system can distinguish between localized target objects (runners) and distributed precipitation, thereby reducing false alarms while maintaining detection sensitivity.
2Reliability
If a higher detection threshold is used to suppress rain detection, then false alarm rate decreases, but detection of runners is also suppressed
Solution Approach 1:
The patent applies local quality by setting detection thresholds based on angular spectrum characteristics. Instead of using a uniform threshold, the system adapts the threshold locally according to the angular distribution pattern, allowing sensitive detection in directions where runners are present while suppressing uniform precipitation noise.
Solution Approach 2:
The patent changes the detection parameter from a fixed threshold to an adaptive threshold based on angular spectrum analysis. This allows the detection threshold to dynamically adjust to environmental conditions, maintaining high sensitivity for runners while suppressing rain-induced false alarms.
3Reliability
If polarimetric measures are used to suppress precipitation detection, then false alarm rate decreases, but hardware complexity increases
Solution Approach 1:
The patent replaces complex hardware-based polarimetric measures with a software-based angular spectrum analysis approach. By using signal processing in the angular domain, the system achieves precipitation suppression without requiring additional polarimetric hardware components.
Solution Approach 2:
The patent creates a virtual representation of the detection space through angular spectrum analysis. This computational model allows the system to simulate and analyze reflection patterns without requiring physical hardware modifications, achieving complex detection functionality through software processing.
4Measurement precision
If detection is evaluated over multiple measurement cycles, then detection accuracy improves, but processing complexity and time increase
Solution Approach 1:
The patent performs preliminary angular spectrum analysis on each individual measurement cycle, pre-processing the data to extract angular distribution characteristics. This allows the system to make detection decisions based on single-cycle data with high accuracy, eliminating the need for complex multi-cycle evaluation while maintaining detection reliability.
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 effectively suppresses false detections from interference like rain or snow while reliably identifying actual obstacles, ensuring consistent detection performance regardless of environmental conditions with minimal effort and cost.
Implementation Method 1
receives secondary signals reflected from target objects
Data Source
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AI summary
A method for suppressing false detection, in particular precipitation detection, in a radar sensor which transmits primary signals in measuring cycles and receives secondary signals reflected by target objects, comprises the following steps: - forming an angular spectrum for a partial data set of the reflected signals, wherein the angular spectrum indicates the signal strength curve of the corresponding reflected signals over an angle relative to the radar sensor, - determining a noise level for the angular spectrum as a function of the signal strength curve and determining a detection threshold on the basis of the noise level and - detecting target objects by comparing the angular spectrum with the detection threshold.