基于NSGA-Ⅲ的机载雷达空空射频隐身探测参数设计

doi:10.12305/j.issn.1001-506X.2024.01.11

摘要/Abstract

摘要：

目标探测任务要求雷达具备较大的发射功率, 但这又会增大其辐射特征, 从而降低射频(radio frequency, RF)隐身性能, 因此如何权衡RF隐身与探测性能成为机载雷达RF隐身探测的难点问题。为此, 本文提出一种用于机载雷达空中运动目标RF隐身探测的多目标优化参数设计方法。所提方法首先建立空中运动目标射频隐身探测多目标优化模型; 然后利用非支配排序遗传算法Ⅲ(non-dominated sorting genetic algorithm Ⅲ, NSGA-Ⅲ)对该模型求解, 获得帕累托最优解集; 最后采用层次分析-逼近理想解排序法对候选解集进行多属性决策获得最终方案。数值实验结果表明所提方法可有效应用于空中运动目标射频隐身探测参数设计。

关键词: 机载雷达, 空中运动目标探测, 射频隐身, 多目标优化

Abstract:

The target detection requires a large radiation power for radar, however, this would enlarge its radiation characteristics and reduce the ability of radio frequency (RF) stealth, hence it is becoming a challenge to balance the performance of RF stealth and detection. Therefore in this paper, a multi-target optimal parameters design method is proposed for airborne radar air moving RF stealth detection mode. The proposed method firstly establishes a multi-target optimization model for RF stealth detection of air moving target, and then non-dominated sorting genetic algorithm Ⅲ (NSGA-Ⅲ) is applied to solve this model to obtain the Pareto optimal solution set. Finally, an optimal solution can be selected by using the analytic hierarchy process and technique for order preference by similarity to ideal solution multi-property decision method. The results of numerical experiments verify the efficacy of the proposed method can be applied to the air moving target RF stealth detection parameters design.

Key words: airborne radar, air moving target detection, radio frequency (RF) stealth, multi-target optimization

中图分类号:

TN958.92

刘俊, 崔宁, 谢佳昕, 行坤. 基于NSGA-Ⅲ的机载雷达空空射频隐身探测参数设计[J]. 系统工程与电子技术, 2023, 46(1): 97-104.

Jun LIU, Ning CUI, Jiaxin XIE, Kun XING. Airborne radar air-to-air RF stealth detection parameter design based on NSGA-Ⅲ[J]. Systems Engineering and Electronics, 2023, 46(1): 97-104.

图/表 11

图1

图2

图3

表1

表2

表3

图4

图5

表4

表5

表6

参考文献 31

1	ZHAO S Y, CHENG T. Research on MIMO radar RF stealth algorithm in searching mode[C]//Proc. of the IEEE International Conference on Signal Processing, Communications and Computing, 2014: 88-93.
2	ZEESHAN M, ARIF M S, UMAIR M, et al. Survey of systems engineering approaches for design and development of AESA radar for fighter aircraft[C]//Proc. of the 16th International Bhurban Conference on Applied Sciences and Technology, 2019: 956-962.
3	张贞凯, 周建江, 汪飞, 等. 机载相控阵雷达射频隐身时最优搜索性能研究[J]. 宇航学报, 2011, 32 (9): 2023- 2028.
	ZHANG Z K , ZHOU J J , WANG F , et al. Research on optimal search performance of airborne phased array radar for radio frequency stealth[J]. Journal of Astronautics, 2011, 32 (9): 2023- 2028.
4	张贞凯, 周建江, 汪飞, 等. 基于射频隐身的相控阵雷达功率控制算法[J]. 系统工程与电子技术, 2012, 34 (11): 2244- 2248. doi: 10.3969/j.issn.1001-506X.2012.11.10
	ZHANG Z K , ZHOU J J , WANG F , et al. Novel algorithm of power control based on radio frequency stealth[J]. Systems Engineering and Electronics, 2012, 34 (11): 2244- 2248. doi: 10.3969/j.issn.1001-506X.2012.11.10
5	PANG C , SHAN G L , MA W N , et al. Sensor radiation interception risk control in target tracking[J]. Defence Technology, 2020, 16 (3): 695- 704. doi: 10.1016/j.dt.2019.10.014
6	LIU H Q , YU L , YANG H Y , et al. Self-adapting radiation control method for RFS in tracking[J]. Journal of Systems Engineering and Electronics, 2018, 29 (2): 244- 255. doi: 10.21629/JSEE.2018.02.04
7	刘宏强, 魏贤智, 李飞, 等. 基于射频隐身的雷达跟踪状态下单次辐射能量实时控制方法[J]. 电子学报, 2015, 43 (10): 2047- 2052. doi: 10.3969/j.issn.0372-2112.2015.10.025
	LIU H Q , WEI X Z , LI F , et al. The real time control method of radar single radiation power based on RF stealth at the tracking[J]. Acta Electronica Sinica, 2015, 43 (10): 2047- 2052. doi: 10.3969/j.issn.0372-2112.2015.10.025
8	杨宇晓, 汪飞, 周建江, 等. 跳频周期和跳频间隔的最大条件熵射频隐身设计方法[J]. 电子与信息学报, 2015, 37 (4): 841- 847.
	YANG Y X , WANG F , ZHOU J J , et al. RF stealth design method for hopping cycle and hopping interval based on conditional maximum entropy[J]. Journal of Electronics & Information Technology, 2015, 37 (4): 841- 847.
9	于伟强, 汪飞, 孙萍, 等. 杂波背景下机载雷达信号参数的射频隐身优化[J]. 系统工程与电子技术, 2021, 43 (11): 3194- 3201. doi: 10.12305/j.issn.1001-506X.2021.11.19
	YU W Q , WANG F , SUN P , et al. RF stealth optimization of airborne radar signal parameters under clutter background[J]. Systems Engineering and Electronics, 2021, 43 (11): 3194- 3201. doi: 10.12305/j.issn.1001-506X.2021.11.19
10	廖俊, 于雷, 周中良, 等. 机载相控阵雷达探测参数优化[J]. 仪器仪表学报, 2012, 33 (11): 2487- 2494. doi: 10.3969/j.issn.0254-3087.2012.11.013
	LIAO J , YU L , ZHOU Z L , et al. Optimizing detection parameters of airborne PAR[J]. Chinese Journal of Scientific Instrument, 2012, 33 (11): 2487- 2494. doi: 10.3969/j.issn.0254-3087.2012.11.013
11	ADDABBO P , HAN S , BIONDI F , et al. Adaptive radar detection in the presence of multiple alternative hypotheses using kullback-leibler information criterion-Part Ⅰ: detector designs[J]. IEEE Trans.on Signal Processing, 2021, 69, 3730- 3741. doi: 10.1109/TSP.2021.3089440
12	ZUK J . Correlated noncoherent radar detection for gamma-fluctuating targets in compound clutter[J]. IEEE Trans.on Aerospace and Electronic Systems, 2022, 58 (2): 1241- 1256. doi: 10.1109/TAES.2021.3113629
13	MAHAFZA B R . Radar systems analysis and design using MATLAB[M]. 4th ed Boca Raton: Chapman and Hall/CRC, 2022.
14	SHI C , WANG F , SELLATHURAI M , et al. Low probability of intercept-based optimal power allocation scheme for an integrated multistatic radar and communication system[J]. IEEE Trans.on Systems Journal, 2020, 14 (1): 983- 994. doi: 10.1109/JSYST.2019.2931754
15	JR D L . An Introduction to RF Stealth[M]. 2nd ed London: Scitech Publishing, 2021.
16	高飞, 谢文冲, 段克清, 等. 相控阵机载PD雷达波形参数系统化设计方法研究[J]. 空军预警学院学报, 2017, 31 (1): 23- 27. doi: 10.3969/j.issn.2095-5839.2017.01.006
	GAO F , XIE W C , DUAN K Q , et al. Systematic design method for phased array airborne PD radar waveform parameters[J]. Journal of Air Force Early Warning Academy, 2017, 31 (1): 23- 27. doi: 10.3969/j.issn.2095-5839.2017.01.006
17	TIAN Y , SI L C , ZHANG X Y , et al. Evolutionary large-scale multi-objective optimization: a survey[J]. ACM Computing Surveys, 2022, doi: 10.1145/3470971
18	DEB K , PRATAP A , AGARWAL S , et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Trans.on Evolutionary Computation, 2002, 6 (2): 182- 197. doi: 10.1109/4235.996017
19	ZITZLER E, LAUMANNS M, THIELE L. SPEA2: improving the strength pareto evolutionary algorithm[R]. Zurich: Computer Engineering and Networks Laboratory, 2001.
20	ZHANG X L , TAN Y J , YANG Z W . Resource allocation optimization of equipment development task based on MOPSO algorithm[J]. Journal of Systems Engineering and Electronics, 2019, 30 (6): 1132- 1143. doi: 10.21629/JSEE.2019.06.09
21	DEB K , JAIN H . An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part Ⅰ: solving problems with box constraints[J]. IEEE Trans.on Evolutionary Computation, 2014, 18 (4): 577- 601. doi: 10.1109/TEVC.2013.2281535
22	CHIU W Y , YEN G G , JUAN T K . Minimum manhattan distance approach to multiple criteria decision making in multiobjective optimization problems[J]. IEEE Trans.on Evolutionary Computation, 2016, 20 (6): 972- 985. doi: 10.1109/TEVC.2016.2564158
23	MATHEW M , CHAKRABORTTY R K , RYAN M J . Selection of an optimal maintenance strategy under uncertain conditions: an interval type-2 fuzzy AHP-TOPSIS method[J]. IEEE Trans.on Engineering Management, 2022, 69 (4): 1121- 1134. doi: 10.1109/TEM.2020.2977141
24	REMADI F D, FRIKHA H M. The triangular intuitionistic fuzzy extension of the CODAS method for solving multi-criteria group decision making[C]//Proc. of the International Multi-Conference on: Organization of Knowledge and Advanced Technologies, 2020.
25	YARAGHI N , TABESH P , GUAN P Q , et al. Comparison of AHP and monte carlo ahp under different levels of uncertainty[J]. IEEE Trans.on Engineering Management, 2015, 62 (1): 122- 132. doi: 10.1109/TEM.2014.2360082
26	TOMPKINS M , IAMMARTINO R , FOSSACECA J . Multiattribute framework for requirements elicitation in phased array radar systems[J]. IEEE Trans.on Engineering Management, 2020, 67 (2): 347- 364. doi: 10.1109/TEM.2018.2878688
27	HAMDANI, WARDOYO R. The complexity calculation for group decision making using TOPSIS algorithm[C]//Proc. of the 12th International Conference on Synchrotron Radiation Instrumentation, 2016.
28	ZHANG H J , WANG G G . Improved NSGA-Ⅲ using transfer learning and centroid distance for dynamic multi-objective optimization[J]. Complex & Intelligent Systems, 2021, 9, 1143- 1164.
29	TIAN Y , CHENG R , ZHANG X , et al. PlatEMO: a MATLAB platform for evolutionary multi-objective optimization[educational forum][J]. IEEE Trans.on Computational Intelligence Magazine, 2017, 12 (4): 73- 87. doi: 10.1109/MCI.2017.2742868
30	DENG J D , ZHANG Q F . Approximating hypervolume and hypervolume contributions using polar coordinate[J]. IEEE Trans.on Evolutionary Computation, 2019, 23 (5): 913- 918. doi: 10.1109/TEVC.2019.2895108
31	SHANG K , ISHIBUCHI H , HE L J , et al. A survey on the hypervolume indicator in evolutionary multiobjective optimization[J]. IEEE Trans.on Evolutionary Computation, 2021, doi: 10.1109/TEVC.2020.3013290

参数	符号	数值
波长/m	λ	0.03
带宽/MHz	B_r	10
脉冲宽度/μs	τ_p	20
天线增益/dB	G_t	35
脉冲重复频率/kHz	f_r	10
虚警概率	p_fa	10^-6
噪声系数/dB	F_n	2
综合损耗/dB	L_r	5.18
最小可检测信噪比/dB	S_min	13

参数	符号	数值
接收门限/W	P_I	5×10^-9
处理器净增益	G_IP	0.5
综合损耗/dB	L_I	3
扫描时间/s	T_I	5
天线增益/dB	G_I	30
噪声系数/dB	F_I	6
截获接收机密度/km^-2	D_I	0.001
带宽/MHz	B_I	25

参数	符号	数值
载机速度/(m/s)	V_a	150
目标径向速度/(m/s)	V_t	300
俯仰角/(°)	φ	3

数值类型	参数	数值
目标值	P_t/W	6 027.3
	T_D/ms	0.054 7
	n_p	548
决策值	p_i	0.035
	p_d	0.937
	SNR_I/dB	71.45
	SNR/dB	32.53

R/km	p_i	p_d	P_t/W	n_p
20	0.001	0.951	38.86	985
40	0.010	0.942	532.67	937
60	0.021	0.935	3 314.95	509
80	0.032	0.922	11 252.14	320