Dynamic Radar Detection Thresholds for Clutter and Weak Target Handling
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Solution Overview
Problem
Radar systems face challenges with false alarms and missed detections due to clutter reflections and weak signal power from distant targets, which existing detection threshold designs fail to adequately address.
Innovation Solution
The method involves setting detection threshold values by adding different offset values to reference threshold values, allowing for adjustable threshold adjustments based on object characteristics such as range, velocity, or angle, using a segment-based or function-based scheme to optimize detection in various segments or conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single detection threshold value is used for all detection scenarios, then the device complexity is reduced, but the reliability of detection deteriorates due to inability to handle both clutter reflections and weak distant targets effectively
Solution Approach 1:
The patent applies parameter changes by adjusting detection threshold values based on different object characteristics. Specifically, it modifies the threshold parameter dynamically according to range, velocity, or angle parameters of detected objects, allowing the system to adapt to varying detection conditions without increasing structural complexity
Solution Approach 2:
The patent implements dynamics by transitioning from a static single threshold value to a dynamic multi-threshold system. The detection threshold is no longer fixed but varies according to object characteristics such as range segments, velocity ranges, or angular positions, enabling the system to respond adaptively to different detection scenarios
2Reliability
If the detection threshold is set to reduce false alarms from clutter reflections, then the reliability improves, but the ability to detect weak signal power from distant targets deteriorates
Solution Approach 1:
The patent applies local quality by setting different detection threshold levels for different local conditions or segments. Instead of using a uniform threshold, it creates localized threshold values tailored to specific range segments, velocity ranges, or angular regions, allowing optimal detection performance in each local context
Solution Approach 2:
The patent implements segmentation by dividing the detection space into multiple segments based on object characteristics. It segments the range into multiple intervals, velocity into different ranges, or angle into distinct zones, and applies specific threshold values to each segment, enabling differentiated handling of clutter and weak targets
3Measurement precision
If the detection threshold is set to detect weak signal power from distant targets, then the measurement precision improves, but false alarms from clutter reflections increase
Solution Approach 1:
The patent applies local quality by setting different detection threshold levels for different local conditions or segments. Instead of using a uniform threshold, it creates localized threshold values tailored to specific range segments, velocity ranges, or angular regions, allowing optimal detection performance in each local context
Solution Approach 2:
The patent applies parameter changes by adjusting detection threshold values based on different object characteristics. Specifically, it modifies the threshold parameter dynamically according to range, velocity, or angle parameters of detected objects, allowing the system to adapt to varying detection conditions
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 reduces false alarms from clutter reflections and enhances the detection of weak signal power from distant targets, improving the overall detection accuracy and reliability of radar systems.
Implementation Method 1
A Radio Detection and Ranging (radar) system refers to electronic equipment that detects the presence of objects by using reflected electromagnetic energy
Data Source
AI summary
An object detection method includes: obtaining a first offset value and a second offset value, setting a first detection threshold value by adding the first offset value to a first reference threshold value, setting a second detection threshold value by adding the second offset value to a second reference threshold value, obtaining a detection input, and performing target detection upon the detection input according to at least the first detection threshold value and the second detection threshold value. The first offset value is different from the second offset value. The first reference threshold value is determined for detecting if at least one object with a first value of an object characteristic exists. The second reference threshold value is determined for detecting if at least one object with a second value of the object characteristic exists. The second value is different from the first value.