脉冲噪声中微弱信号稳健检测研究

doi:10.12305/j.issn.1001-506X.2025.11.04

摘要/Abstract

摘要：

针对脉冲噪声中局部最优检测器(locally optimal detector, LOD)稳健性较弱的问题，提出一种稳健局部最优检测方法。首先，给出了基于母函数的微弱信号检测框架，对LOD的母函数和检测性能进行系统分析，指出模型失配会导致LOD检测性能下降；然后，采用高斯?拉普拉斯混合分布模型对脉冲噪声建模，在此基础上构建了稳健LOD，并对其性能进行系统分析。理论和仿真结果都表明，无论脉冲噪声模型是混合高斯分布模型还是对称α稳定分布模型，本文所提检测方法均能取得近似最优检测性能，具有较强的稳健性。所提方法可广泛应用于模型失配的情况中。

关键词: 脉冲噪声, 微弱信号检测, 局部最优检测器, 稳健性

Abstract:

Robust local optimal detection method is proposed to solve the problem of weak robustness of local optimal detector （LOD） in impulsive noise. Firstly, a weak signal detection framework based on generating function is presented, and the generating function and detection performance of LOD are systematically analyzed. This paper pointed out that model mismatch can lead to a decrease in LOD detection performance. Then the Gaussian Laplacian mixed distribution model is used to model the impulse noise, and robust LOD is constructed on this basis, and its performance is systematically analyzed. The theoretical and simulation results show that the detection method proposed in this paper can achieve approximately optimal detection performance regardless of whether the impulse noise model is a mixed Gaussian distribution model or a symmetric α stable distribution model, and has strong robustness, which can be widely used in the case of model mismatch.

Key words: impulse noise, weak signal detection, local optimal detector （LOD）, robustness

中图分类号:

TN 911.7

代振, 韩庆伟, 王平波, 贾文抖. 脉冲噪声中微弱信号稳健检测研究[J]. 系统工程与电子技术, 2025, 47(11): 3551-3558.

Zhen DAI, Qingwei HAN, Pingbo WANG, Wendou JIA. Research on robust detection of weak signals in impulsive noise[J]. Systems Engineering and Electronics, 2025, 47(11): 3551-3558.

图/表 11

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

参考文献 30

1	KAY S, SALISBURY J. Improved active sonar detection using autoregressive prewhiteners[J]. The Journal of the Acoustical Society of America, 1990, 87 (4): 1603- 1611. doi: 10.1121/1.399408
2	王平波, 卫红凯, 娄良轲, 等. 海洋混响数据的SαS分布建模[J]. 哈尔滨工程大学学报, 2021, 42 (1): 55- 60.
	WANG P B, WEI H K, LOU L K, et al. Oceanic reverberation probability density modeling based on SαS distribution[J]. Journal of Harbin Engineering University, 2021, 42 (1): 55- 60.
3	杨喜娟, 孙德春, 刘祖军. 基于广义幂变换的脉冲噪声抑制方法[J]. 电力信息与通信技术, 2024, 22 (9): 78- 83.
	YANG X J, SHUN D C, LIU Z J. Pulse noise suppression method based on generalized power transformation[J]. Electric Power Information and Communication Technology, 2024, 22 (9): 78- 83.
4	罗忠涛, 卢鹏, 张杨勇, 等. 大气噪声幅度分布与抑制处理分析[J]. 系统工程与电子技术, 2018, 40 (7): 1443- 1448.
	LUO Z T, LU P, ZHANG Y Y, et al. Analysis on amplitude distribution and suppression techniques of atmosphere noise[J]. Systems Engineering and Electronics, 2018, 40 (7): 1443- 1448.
5	吕玉娇, 黄海宁, 张扬帆, 等. 冰下非高斯噪声下的范数约束波束形成方法[J]. 声学学报, 2024, 49 (2): 286- 297.
	LU Y J, HUANG H N, ZHANG Y F, et al. Norm-constraining beamforming amid underice non-Gaussian noise[J]. Acta Acustica, 2024, 49 (2): 286- 297.
6	LI N, EISENSTEIN B. Detection of weak signals in non-Gaussian noise[J]. IEEE Trans. on Information Theory, 1981, 27 (6): 755- 771.
7	SALEH T, MARSLAND I, TANANY M E. Suboptimal detectors for alpha-stable noise: simplifying design and improving performance[J]. IEEE Trans. on Communications, 2012, 60 (10): 2982- 2989. doi: 10.1109/TCOMM.2012.071812.100789
8	LI X T, SUN J, WANG S Y, et al. Near-optimal detection with constant false alarm ratio in varying impulsive interference[J]. IET Signal Processing, 2013, 7 (9): 824- 832. doi: 10.1049/iet-spr.2013.0024
9	CHEN Y F, XU F, CHEN J M. Polynomial-approximation-based locally optimum detector for signals with symmetric alpha stable noise[J]. IET Communications Letters, 2014, 16 (8): 2952- 2960.
10	ATALOGLOU V G, NTOUNI G D, KAPINAS V M, et al. A low-complexity detector for BPPM systems under additive Gaussian mixture noise[J]. IEEE Wire Communication Letters, 2017, 6 (1): 50- 53.
11	OH H, NAM H. Design and performance analysis of non-linearity preprocessors in an impulsive noise environment[J]. IEEE Trans. on Vehicular Technology, 2017, 66 (1): 364- 376.
12	XU W C, CHANG R, CHEN J S. Detection of known signals in additive impulsive noise based on Spearman’s rho and Kendall’s tau[J]. Signal Processing, 2019, 16 (1): 165- 179.
13	代振, 王平波, 卫红凯. 非高斯背景下的自适应限幅检测研究[J]. 电子学报, 2020, 48 (3): 426- 430.
	DAI Z, WANG P B, WEI H K. Adaptive limiter detection in non-Gaussian noise background[J]. Acta Electronice Sinica, 2020, 48 (3): 426- 430.
14	LUO Z T, LU P, ZHANG G. Locally optimal detector design in impulsive noise with unknown distribution[J]. EURASIP Journal on Advances in Signal Processing, 2018, 2018 (1): 34- 43. doi: 10.1186/s13634-018-0560-x
15	LUO Z T, ZHANG Y Y. Novel nonlinearity based on gaussiannization and generalized matching for impulsive moise suppression[J]. IEEE Access, 2019, 7, 65163- 65173. doi: 10.1109/ACCESS.2019.2917231
16	LUO Z T, GUO R N, ZHANG X H, et al. Optimal and efficient designs of Gaussian-tailed nonlinearity in symmetric α-stable noise[J]. Electronics Letters, 2019, 55 (6): 353- 355. doi: 10.1049/el.2018.7347
17	DAI Z, WANG P B, WEI H K, et al. Adaptive detection with constant false alarm ratio in a non-Gaussian noise background[J]. IEEE Communications Letters, 2019, 23 (8): 1369- 1372. doi: 10.1109/LCOMM.2019.2918816
18	BEAULIEU N, DENG S, GAO Y. Simple almost-optimal cauchy detector for low-power sensor node LOS receiver applications[J]. Electronics Letters, 2018, 54 (19): 1141- 1143. doi: 10.1049/el.2018.5468
19	DAI Z, WANG P B, CAO W H. Design and performance analysis of parametric suboptimal detectors in SαS noise[J]. IET Communications, 2020, 14 (7): 1169- 1174. doi: 10.1049/iet-com.2019.0606
20	罗忠涛, 詹燕梅, 郭人铭, 等. 脉冲噪声中基于指数函数的可变拖尾非线性变换设计[J]. 电子与信息学报, 2020, 42 (4): 932- 940.
	LUO Z T, ZHAN Y M, GUO R M, et al. Variable tailing nonlinear transformation design based on exponential function in impulsive noise[J]. Journal of Electronics & Information Technology, 2020, 42 (4): 932- 940.
21	代振, 王平波, 娄良轲. SαS分布下的累积分布检测器[J]. 系统工程与电子技术, 2020, 42 (1): 10- 14.
	DAI Z, WANG P B, LOU L K. Cumulative distribution detector under SαS distribution[J]. Systems Engineering and Electronics, 2020, 42 (1): 10- 14.
22	WANG J J, LI J H, SHI W, et al. A novel underwater acoustic signal denoising algorithm for Gaussian/non-Gaussian impulsive noise[J]. IEEE Trans. on Vehicular Technology, 2020, 70 (1): 429- 445.
23	CLAVIER L, PETERS G W, SEPTIER F. Impulsive noise modeling and robust receiver design[J]. EURASIP Journal on Wireless Communications and Networking, 2021, 2021 (1): 13- 43. doi: 10.1186/s13638-020-01868-1
24	CHEN C J, XU W C, PAN Y J, et al. A nonparametric approach to signal detection in non-Gaussian noise[J]. IEEE Signal Processing Letters, 2022, 29, 503- 507.
25	BARKA H, SAHABUl A M, KADDOUM G, et al. An LLR-based receiver for mitigating bursty impulsive noise with unknown distributions[J]. IEEE Open Journal of the Communications Society, 2024, 5, 6166- 6179. doi: 10.1109/OJCOMS.2024.3467385
26	NERI J, DEPALLE P, BADEAU R. Approximate inference and learning of state space models with Laplace noise[J]. IEEE Trans. on Signal Processing, 2021, 69, 3176- 3189. doi: 10.1109/TSP.2021.3075146
27	FU H P, HUAN W , LI Z W, et al. Robust cubature Kalman filter with Gaussian-multivariate Laplacian mixture distribution and partial variati bayesian method[J]. IEEE Trans. on Signal Processing, 2023, 71, 847- 858. doi: 10.1109/TSP.2023.3256041
28	LUIZ G B, DAYAN A. Guimaraes performance of detectors for cooperative spectrum sensing under Laplacian noise[J]. Journal of Communication and Information Systems, 2024, 39 (1): 1- 12.
29	彭飞, 王中, 孟庆旭, 等. EM算法在p范混合模型参数估计中的应用[J]. 武汉大学学报: 信息科学版, 2022, 47 (9): 1432- 1438.
	PENG F, WANG Z, MENG Q X, et al. Application of EM algorithm in parameter estimation of p-Norm mixture model[J]. Geomatics and Information Science of Wuhan University, 2022, 47 (9): 1432- 1438.
30	YANG X, LI Y Z, ZHAO Y, et al. Gaussian mixture model uncertainty modeling for power systems considering mutual assistance of latent variables[J]. IEEE Trans. on Sustainable Energy, 2024, 16 (2): 1483- 1486.

[1]	何从, 刘帅, 闫锋刚, 金铭. 互耦及脉冲噪声下基于稀疏重构的鲁棒DOA估计算法[J]. 系统工程与电子技术, 2025, 47(8): 2444-2453.
[2]	谭钢, 鄢社锋, 毛琳琳, 杨基睿. 稀疏贝叶斯学习水声OFDM系统信道与脉冲噪声联合估计[J]. 系统工程与电子技术, 2025, 47(10): 3482-3491.
[3]	赵忠凯, 关泽越, 李虎. 基于样条插值的非线性自干扰对消技术[J]. 系统工程与电子技术, 2024, 46(9): 2916-2925.
[4]	谭钢, 鄢社锋, 叶子豪, 杨基睿. OFDM系统迭代脉冲噪声抑制与信道估计方法[J]. 系统工程与电子技术, 2024, 46(8): 2841-2849.
[5]	毛毅, 段永胜, 黄中瑞, 张峻宁. 一种在脉冲噪声环境下的最大相关熵目标直接定位算法[J]. 系统工程与电子技术, 2023, 45(9): 2651-2658.
[6]	金艳, 赵大地, 姬红兵. 脉冲噪声下基于NAT函数的LFM信号参数估计[J]. 系统工程与电子技术, 2022, 44(3): 762-770.
[7]	李浩洋, 向建军, 彭芳, 王帅, 李志军. 基于粒子群优化的波束空间广义旁瓣相消算法[J]. 系统工程与电子技术, 2022, 44(10): 3037-3045.
[8]	罗忠涛, 郭人铭, 詹燕梅. 脉冲噪声非线性变换设计的研究综述[J]. 系统工程与电子技术, 2021, 43(7): 1971-1980.
[9]	陈桥, 童宁宁, 胡晓伟, 丁姗姗. 基于多观测向量块稀疏的MIMO雷达非理想正交波形成像[J]. 系统工程与电子技术, 2020, 42(12): 2747-2754.
[10]	纪元法, 朱亮亮, 孙希延, 严素清. 差分进化算法结合正则化解算病态方程[J]. 系统工程与电子技术, 2018, 40(7): 1573-1577.
[11]	李冬霞, 刘国庆, 刘海涛. 基于MCMC的OFDM系统脉冲噪声检测与抑制方法[J]. 系统工程与电子技术, 2018, 40(10): 2348-2353.
[12]	金艳, 李曙光, 姬红兵. 脉冲噪声中基于数据可信度加权的跳频信号检测[J]. 系统工程与电子技术, 2016, 38(9): 2171-2178.
[13]	金艳, 任航, 姬红兵. 脉冲噪声下基于相关熵的OFDM时域参数估计[J]. 系统工程与电子技术, 2015, 37(12): 2701-2706.
[14]	黄宴委，祁冰露. 基于噪声边界检测的规则函数脉冲噪声滤波器[J]. 系统工程与电子技术, 2014, 36(2): 396-402.
[15]	刘也, 余安喜, 张增辉, 朱炬波, 梁甸农. 空间目标跟踪的融合系统设计[J]. Journal of Systems Engineering and Electronics, 2011, 33(9): 1941-1947.