无人驾驶技术——雷达CFAR-代码实现

最新推荐文章于 2025-11-09 18:49:18 发布

原创

最新推荐文章于 2025-11-09 18:49:18 发布 · 3.7k 阅读

20 ·

CC 4.0 BY-SA版权

该博客介绍了如何使用MATLAB实现二维恒虚警率(CFAR)算法，旨在计算雷达的虚警率，用于无人驾驶中的雷达信号处理。文中包含详细步骤及实际效果展示。

文章目录

MATLAB实现二维CFAR
效果图
MATLAB实现二维CFAR步骤如下：
实现代码

无人驾驶技术——雷达CFAR-代码实现

MATLAB实现二维CFAR

本节目标是实现二维CFAR的雷达虚警率计算。CFAR的介绍可参考文章

无人驾驶——雷达Clutter, CFAR,AoA

效果图

在这里插入图片描述

MATLAB实现二维CFAR步骤如下：

# Prerequisites
MATLAB


R = 110; % set DISTANCE OF THE TARGET is 110

v = 30; % set Velocity OF THE TARGET is 30 


# 2D CFAR
The 2d CA-CFAR implementation involves the training cells occupying the cells surround the cell under test with a guard grid in between to prevent the impact of a target signal on the noise estimate.

## STEPS 
1.Determine the number of Training cells for each dimensions. Similarly, pick the number of guard cells.
This number can be picked for try and error. In this project was used, in which gave us a good result.

    * Tr = 10
    * Td = 7 
    * Gr = 5
    * Gd = 3
    * offset = 10.88;

or

    * Tr = 9
    * Td = 5
    * Gr = 5
    * Gd = 3
    * offset = 12;
or 

    * Tr = 80
    * Td = 20 
    * Gr = 7
    * Gd = 7
    * offset =12;

2.Select the grid that includes the training, guard and Cell Under Test (CUT).

3.Measuring the average noise across all the training cells, this gives the threshold.

4.Add a offset to the threshold to keep the false alarm to the minimum. In this project we used 10.88dB.

5.Test the CUT