Adaptive CFAR Object Detection System for Radar
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object detection systems face challenges in maximizing signal-to-noise ratio (SNR) and setting appropriate detection thresholds, leading to false alarms and poor spatial resolution, especially when detecting objects at varying distances with different SNR levels.
Innovation Solution
An object detection system that employs a receiver with an analog-to-digital converter, a pre-processor for noise reduction, a parameter extractor to calculate the number of reference cells and a multiplication factor, and a CFAR processor to analyze the cell-under-test and reference cells, dynamically adjusting the number of reference cells based on probability of detection and false alarm to optimize threshold setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of reference cells is increased to detect distant objects with low SNR, then the detection capability for low SNR objects is improved, but the spatial resolution deteriorates
Solution Approach 1:
The patent applies dynamics by making the number of reference cells variable rather than fixed. The CFAR processor dynamically adjusts the number of reference cells based on the SNR of the current detection scenario. For distant objects with low SNR, more reference cells are used to improve detection reliability. For close objects with high SNR, fewer reference cells are used to maintain spatial resolution. This dynamic adaptation resolves the contradiction between detection capability and spatial resolution.
Solution Approach 2:
The patent changes the parameter of reference cell count based on SNR conditions. By calculating the SNR of the received signal and comparing it with a threshold, the system selects different numbers of reference cells (e.g., first number for low SNR, second number for high SNR). This parameter change allows the system to optimize both detection capability and spatial resolution for different operating conditions.
2Reliability
If the number of reference cells is set to be large to detect distant objects, then the probability of detection is improved, but the false alarm rate increases due to poor spatial resolution
Solution Approach 1:
The system dynamically adjusts the number of reference cells based on SNR conditions to balance detection probability and false alarm rate. When SNR is low (distant objects), a larger number of reference cells increases detection probability. When SNR is high (close objects), a smaller number of reference cells maintains spatial resolution and reduces false alarms from clutter. This dynamic adjustment prevents the false alarm rate from increasing unnecessarily.
Solution Approach 2:
The patent changes the reference cell count parameter based on SNR threshold comparison. For signals with SNR below the threshold, the system uses a first number of reference cells optimized for detection. For signals with SNR above the threshold, it uses a second number of reference cells that maintains spatial resolution. This parameter change strategy controls the false alarm rate while maintaining high detection probability.
3Device complexity
If a fixed number of reference cells is used for CFAR processing, then the system complexity is reduced, but the detection performance deteriorates for objects with varying SNR levels
Solution Approach 1:
The patent implements a dynamic reference cell selection mechanism that adapts to varying SNR conditions. The SNR calculator computes the signal-to-noise ratio, and based on this computation, the CFAR processor selects the appropriate number of reference cells. This dynamic approach maintains relatively simple system architecture while significantly improving detection performance for objects at different distances with varying SNR levels.
Solution Approach 2:
The system changes the reference cell count parameter based on computed SNR values. By comparing SNR with a threshold, the system selects between different reference cell configurations. This parameter change approach allows the system to maintain good detection performance across varying SNR conditions without requiring a completely complex adaptive structure, as the parameter selection is based on a simple threshold comparison.
Data Source
AI summary
The disclosed systems, structures, and methods are directed to an object detection system, employing a receiver configured to receive a signal reflected from an object, an analog-to-digital converter (ADC) configured to convert the received signal into a digital signal, a pre-processor configured to improve a signal-to-noise (SNR) of the digital signal and to generate a pre-processed signal corresponding to the digital signal, a parameter extractor configured to calculate a number of reference cells M and a multiplication factor K0, and a Constant False Alarm Rate (CFAR) processor configured to analyze a cell-under-test (CUT) and M reference cells in accordance with the number of reference cells M and the multiplication factor K0 to detect the presence of the object.