基于杂波拖尾分布的雷达无人机检测性能分析

doi:10.12305/j.issn.1001-506X.2024.01.13

Abstract

Abstract:

Detection of the fixed-wing unmanned aerial vehicle (UAV) is an important issue for low-altitude radar surveillance. Analyzing the radar detection performance for fixed-wing UAV provides important reference for the evaluation and technical upgrade of radar UAV detection capability. In this paper, based on the experimental data of radar detection of low-altitude fixed-wing UAV, the statistical distribution of fixed-wing UAV radar received signal amplitude is analyzed at first. Then the false alarm probability caused by the clutter tail is modeled by polynomial. Then, according to the distribution of the tail of false alarm probability and the distribution of amplitude of UAV echo plus clutter, the detection threshold and radar detection performance are obtained by theoretical derivation. Finally, the theoretical radar detection performance is compared with traditional performance analysis results and actual radar detection performance. The comparison results show that radar detection threshold owns higher accuracy by using the polynomial to fit the tail of the false alarm probability, which makes the prediction of radar UAV detection performance has higher accuracy than the traditional methods.

Key words: radar detection, clutter tail, unmanned aerial vehicle (UAV), performance analysis, false alarm probability

CLC Number:

TN95

Yong YANG, Xuesong WANG. Analysis of radar detection performance for UAV based on clutter tail distribution[J]. Systems Engineering and Electronics, 2023, 46(1): 113-120.

Figures/Tables 10

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References 30

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分布	概率密度函数	关键参数估计
瑞利分布	$ f(x)=\frac{x}{\sigma^2} \mathrm{e}^{-\frac{x^2}{2 \sigma^2}}$	$ \hat{\sigma}^2=\frac{1}{2} m(2)$
对数正态分布	$f(x)=\frac{1}{\sqrt{2 \pi \alpha^2 x^2}} \mathrm{e}^{-\frac{(\ln x-\ln \mu)^2}{2 \alpha^2}} $	$\hat{\alpha}^2=\ln \left[m(2) / m^2(1)\right], \mu=m(1) / \mathrm{e}^{\hat{\sigma}^2 / 2} $
韦布尔分布	$f(x)=\frac{c}{b^c} x^{c-1} e^{-}\left(\frac{x}{b}\right)^{c} $	$\hat{c}=\operatorname{interp}\left[\varOmega, c, m(2) / m^2(1)\right], \hat{b}=\frac{m(1)}{\varGamma\left(\frac{1}{\hat{c}}+1\right)} $
伽马分布	$ f(x)=\frac{x^{\gamma-1}}{\varGamma(\gamma) \beta^\gamma} \mathrm{e}^{-\frac{x}{\beta}}$	$ \hat{\beta}=\frac{m(2)-m^2(1)}{m(1)}, \hat{\gamma}=\frac{m(1)}{\hat{\beta}}$
K分布	$f(x)=\frac{2}{\chi \varGamma(\varsigma)}\left(\frac{x}{2 \chi}\right)^{\varsigma} K_{\varsigma-1}\left(\frac{x}{\chi}\right) $	$ \begin{array}{l}\;\;\;\;\;\;\;\varsigma=\operatorname{interp}\left(x_0, y_0, x_d\right), \chi=\sqrt{\frac{m(2)}{4 \zeta^2}} \\y_0=[0.1, 100], x_0=\frac{\varGamma\left(y_0+1\right) \varGamma\left(y_0\right)}{\varGamma^2\left(y_0+\frac{1}{2}\right) \varGamma^2\left(\frac{3}{2}\right)}\end{array}$

分布	N=50		N=100		N=500
分布	拟合度	最大拟合误差	拟合度	最大拟合误差	拟合度	最大拟合误差
瑞利分布	0.51	0.16	0.34	0.13	3e-7	0.18
对数正态分布	0.24	0.2	0.02	0.21	1e-10	0.22
韦布尔分布	0.99	0.06	0.79	0.09	3e-7	0.18
伽马分布	0.68	0.14	0.34	0.13	3e-7	0.18
K分布	0.95	0.1	0.79	0.09	3e-7	0.18

分布	N=50		N=100		N=500
分布	拟合度	最大拟合误差	拟合度	最大拟合误差	拟合度	最大拟合误差
瑞利分布	0.68	0.14	0.14	0.16	5.4e-14	0.25
对数正态分布	0.84	0.12	0.14	0.16	5.4e-14	0.25
韦布尔分布	0.99	0.08	0.68	0.1	5.4e-14	0.25
伽马分布	0.95	0.1	0.56	0.11	5.4e-14	0.25
K分布	0.99	0.08	0.68	0.1	5.4e-14	0.25

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Analysis of radar detection performance for UAV based on clutter tail distribution

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