RMCA-CFAR Radar Detector Eliminates Sorting for Fast Target Detection
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Solution Overview
Problem
Vehicle radar systems face challenges in detecting multiple targets in cluttered environments due to excessive operating time caused by sorting processes, particularly in environments like tunnels and buildings, where existing OS-CFAR detectors are inefficient.
Innovation Solution
The implementation of a recursive modified cell average-constant false alarm rate (RMCA-CFAR) detector that eliminates the sorting process, allowing for rapid target detection by comparing reference data with adjacent data and calculating cell averages using a sliding window, enabling detection of multiple targets in cluttered environments without the need for sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If OS-CFAR detector is used to detect multiple targets in cluttered environments, then target detection capability is improved, but operating time increases excessively due to sorting process
Solution Approach 1:
The patent extracts and removes the sorting process from the OS-CFAR detector, replacing it with a selection process that chooses specific data points without requiring full sorting. This eliminates the time-consuming sorting operation while maintaining the ability to detect multiple targets in cluttered environments.
Solution Approach 2:
The patent changes the operational parameters of the CFAR detector by modifying the data selection mechanism. Instead of sorting all data points to determine threshold values, the system selects specific data points based on modified criteria, fundamentally changing how the detector operates to reduce computational time.
2Measurement precision
If sorting process is implemented in CFAR detector, then multiple target detection accuracy is improved, but computation complexity increases
Solution Approach 1:
The patent extracts the sorting operation from the detection algorithm, replacing it with a simpler selection process. This reduces computation complexity by eliminating the need to sort all data points while still achieving accurate multiple target detection through selective data point evaluation.
Solution Approach 2:
The patent segments the data processing into distinct phases: selecting specific data points, calculating cell averages from selected points, and comparing with thresholds. This segmentation allows the system to achieve accurate detection without the computational burden of sorting the entire data set.
3Speed
If recursive modified cell average-CFAR detector is used to eliminate sorting process, then operating speed is improved, but detection reliability in cluttered environments may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the detector iteratively refines its target identification based on initial detections. The recursive nature allows the system to adjust and improve detection accuracy by using previous detection results to inform subsequent analysis, maintaining reliability while achieving high operating speed.
Solution Approach 2:
The patent introduces dynamic adaptation in the detection process, where the system adjusts its parameters and selection criteria based on the detected environment. This dynamic approach allows the detector to maintain high reliability in varying cluttered conditions while preserving the speed benefits of eliminating the sorting process.
Data Source
AI summary
The present invention suggests a target detecting apparatus and method using a radar which detect a target using a recursive modified cell average-constant false alarm rate (RMCA-CFAR) detector without having a sorting process. The present invention provides a target detecting apparatus using a radar, the apparatus including: a data selecting unit which compares reference data with at least one of previous data and subsequent data which are located at both sides of the reference data, from a received signal including information on a distance and a speed for multiple targets, to select specific data; a cell average calculating unit which calculates an average of cells extracted using a sliding window including the specific data; a CFAR data detecting unit which detects CFAR data based on the average of the extracted cells; and a target detecting unit which detects the target based on the CFAR data.