基于临空目标RCS预测的相控阵雷达资源自适应分配方法

段毅; 谭贤四; 曲智国; 王红; 谢振华

doi:10.11999/JEIT201061

基于临空目标RCS预测的相控阵雷达资源自适应分配方法

doi: 10.11999/JEIT201061

段毅^{1, 2, ,},
谭贤四¹,
曲智国¹,
王红¹,
谢振华²

1.
空军预警学院武汉 430019
2.
95174部队武汉 430000

基金项目: 国家社会科学基金(2020-SKJJ-C-035)

详细信息

作者简介:
段毅：男，博士，研究方向为临近空间高超声速目标特性、相控阵雷达资源管理

谭贤四：男，博士生导师，教授，研究方向为预警探测体系论证

曲智国：男，博士后，副教授，研究方向为预警监视、图像处理

王红：女，博士生导师，教授，研究方向为陆基预警监视、雷达装备作战运用

谢振华：男，硕士，工程师，研究方向为信息融合、雷达兵力作战使用

通讯作者:
段毅　1074146275@qq.com

中图分类号: TN95
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- HTML全文浏览量: 202
- PDF下载量: 66
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-16
- 修回日期: 2022-08-15
- 录用日期: 2022-08-15
- 网络出版日期: 2022-08-18
- 刊出日期: 2022-12-16

Adaptive Resource Management Method for Phased Array Radar Based on RCS Prediction of Hypersonic Gliding Vehicle

DUAN Yi^{1, 2
, ,},
TAN Xiansi¹,
QU Zhiguo¹,
WANG Hong¹,
XIE Zhenhua²

1.
Air Force Early Warning Academy, Wuhan 430019, China
2.
No.95174 Unit of PLA, Wuhan 430000, China

Funds: The National Social Science Foundation of China (2020-SKJJ-C-035)

摘要

摘要: 针对相控阵雷达(PAR)探测临近空间高超声速目标(HGV)时雷达资源消耗过大、量测精度不高的问题，该文提出一种基于临空目标雷达截面积(RCS)预测的雷达资源自适应分配方法。该方法根据滑窗内目标状态与RCS信息，利用贝叶斯后验概率公式预测下一时刻目标RCS，并针对性地调整发射脉冲驻留时长，实现雷达资源的动态调整，使目标回波信号信噪比保持稳定，提高雷达跟踪性能。仿真实验表明，所提算法能较准确估计出目标RCS，进而自适应分配雷达资源，达到在不增加雷达资源消耗前提下提升跟踪精度的目的。
- 相控阵雷达 /
- 临近空间高超声速目标 /
- RCS预测 /
- 资源分配
Abstract: For the problem of Phased Array Radar (PAR) has excessive resource consumption and low measurement accuracy in the process of detecting Hypersonic Gliding Vehicle (HGV). An adaptive radar power allocation method based Radar Cross-Section (RCS) prediction is presented in this paper. Based on the target state and RCS information in the sliding window, Bayesian posterior probability formula is utilized to predict the target RCS at the next moment. Then, the transmit pulse dwell time is adjusted to achieve dynamic adjustment of radar resources, so that the target echo signal signal-to-noise ratio remains stable and the radar tracking performance is improved. The simulation experiment shows that the method in this paper can make accurate prediction the RCS of next time then adaptive allocates radar power. To achieve the purpose of improving the tracking accuracy under the conditions of radar resources.
- Phased Array Radar (PAR) /
- Hypersonic Gliding Vehicle (HGV) /
- RCS Prediction /
- Resource management

HTML全文

图 1 HTV-2模型

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图 2 HTV-2雷达截面积

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图 3 预测步骤

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图 4 入射角误差示意图

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图 5 仿真场景

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图 6 RCS变化

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图 7 预测误差比较

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图 8 资源分配结果

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表 1 分离点参数

	高度(km)	速度(m/s)	速度倾角(°)	攻角(°)	倾侧角(°)	速度方位角(°)	经度(°)	纬度(°)
目标1	50	5000	0	15	0	300	142	14.4
目标2	50	5000	0	15	0	305	144	13.3

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表 2 雷达参数

发射功率	载频	天线增益	系统损耗	噪声系数	探测距离
50 kW	10 GHz	45 dB	5 dB	2 dB	500 km

带宽	最大占空比	临空目标跟踪资源占比	半功率波束宽度	采样间隔	极化方式
10 MHz	0.1	0.1	1°	0.5 s	VV

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表 3 误差影响分析

	本文方法						HM法
	$ \gamma {\text{ = }}0 $	$ \gamma {\text{ = 0}}{\text{.1}} $	$ \gamma {\text{ = }}0.2 $	$ \gamma {\text{ = }}0.3 $	$ \gamma {\text{ = 0}}{\text{.4}} $	$ \gamma {\text{ = 0}}{\text{.5}} $	HM法
VPE	0.0164	0.0423	0.0783	0.1492	0.1982	0.2398	0.2005
APE	0.0012	0.0086	0.0368	0.0743	0.1464	0.2766	0.1340

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表 4 跟踪精度比较

	本文方法						HM法	固定法
	$ \gamma {\text{ = }}0 $	$ \gamma {\text{ = 0}}{\text{.1}} $	$ \gamma {\text{ = }}0.2 $	$ \gamma {\text{ = }}0.3 $	$ \gamma {\text{ = 0}}{\text{.4}} $	$ \gamma {\text{ = 0}}{\text{.5}} $	HM法	固定法
MEC(10³m)	1.2224	1.3942	1.5852	1.8329	2.1227	2.3893	2.1392	3.5618

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