Simulating the curve of Swerling type target detection probability changing with signal-to-noise ratio based on Matlab
In radar systems, target detection is an important task, which involves accurately identifying and locating targets in complex noise environments. The Swerling model is a commonly used model used to describe the statistical characteristics of radar echo signals. In this article, we will use Matlab to simulate the curve of Swerling-type target detection probability changing with signal-to-noise ratio, and provide the corresponding source code.
First, we need to understand the basic principles of the Swerling model. The Swerling model assumes that the amplitude of the echo signal obeys the Rayleigh distribution, while the noise obeys the zero-mean Gaussian distribution. According to the Swerling model, when the signal-to-noise ratio is high, the target's echo signal is easier to detect than the noise, so the target detection probability is higher; and when the signal-to-noise ratio is low, the target's echo signal is easily detected. Noise masks, so target detection probability is lower.
The following is the source code for using Matlab to simulate the curve of Swerling-type target detection probability changing with signal-to-noise ratio:
% 设置信噪比范围
snr = -10:2:20;
% 初始化目标检测概率向量
pd =