基于多分辨率显著性滤波的微动信号增强方法

doi:10.12305/j.issn.1001-506X.2022.04.10

摘要/Abstract

摘要：

微动信号是典型的非平稳信号, 时频分析能够获得微动信号的联合时间-频率分布图像, 是微动信号分析的主要工具之一, 良好的时频图像质量能保证后续特征提取和参数估计的准确性。然而在实际场景中, 时频图像通常受到噪声污染, 使得微动信号难以分辨, 严重制约了后续特征提取和参数估计。根据显著性检测和图像金字塔的基本原理, 本文在多分辨率表示图像上分别计算显著性并滤波, 最后进行加权融合获得增强的时频图像, 有效抑制了噪声, 提升了低信噪比(signal to noise ratio, SNR)下时频图像的质量和微动信号的显著性。实验结果表明, 对于仿真信号以及暗室测量信号, 在-7~7 dB SNR下, 采用该方法均能显著提升时频图像质量, 且-3 dB以下时能大幅提高周期估计的准确率, 是一种有效的微动信号增强方法。

关键词: 微动, 信号增强, 多分辨率, 显著性

Abstract:

Micro-motion signal is a typical non-stationary signal. Time-frequency analysis can obtain the joint time-frequency distribution image of micro-motion signal, and it is one of the main tools for micro-motion signal analysis. Good time-frequency image quality can ensure the accuracy of the subsequent feature extraction and parameter estimation. However, in the actual scene, time-frequency images are usually polluted by noise, which makes it difficult to distinguish micro-motion signals, which seriously restricts the subsequent feature extraction and parameter estimation. According to the basic principles of saliency detection and image pyramid, we compute the saliency on the multi-resolution images separately, and then perform the filtering process. Finally, we perform weighted fusion of them to obtain the enhanced time-frequency image. This method effectively suppresses noise, improves the quality of time-frequency images and the saliency of micro-motion signals under low signal to noise ratio (SNR). Experimental results show that for simulated signals as well as darkroom measured signals, the proposed method can significantly improve the time-frequency image quality when the SNR is from -7 dB to 7 dB, and the accuracy of period estimation can be greatly improved when the SNR is below -3 dB. Therefore, it is an effective method for micro-motion signal enhancement.

Key words: micro-motion, signal enhancement, multi-resolution, saliency

中图分类号:

TN95

唐明磊, 张文鹏, 姜卫东, 高勋章. 基于多分辨率显著性滤波的微动信号增强方法[J]. 系统工程与电子技术, 2022, 44(4): 1148-1157.

Minglei TANG, Wenpeng ZHANG, Weidong JIANG, Xunzhang GAO. Micro-motion signal enhancement method based on multi-resolution saliency filtering[J]. Systems Engineering and Electronics, 2022, 44(4): 1148-1157.

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参考文献 19

1	CHEN V C , LI F , HO S S , et al. Micro-Doppler effect in radar: phenomenon, model, and simulation study[J]. IEEE Trans.on Aerospace & Electronic Systems, 2006, 42 (1): 2- 21.
2	李康乐. 雷达目标微动特征提取与估计技术研究[D]. 长沙: 国防科学技术大学, 2010.
	LI K L. Research on feature extraction and parameters estimation for radar targets with micro-motions[D]. Changsha: National University of Defense Technology, 2010.
3	ZHANG W P , LI K L , JIANG W D . Parameter estimation of radar targets with macro-motion and micro-motion based on circular correlation coefficients[J]. IEEE Signal Processing Letters, 2015, 22 (5): 633- 637. doi: 10.1109/LSP.2014.2365547
4	赵若冰. 雷达目标的微多普勒特征建模与分析技术研究[D]. 南京: 南京理工大学, 2017.
	ZHAO R B. Research on micro-Doppler feature modeling and analysis technology of radar target[D]. Nanjing: Nanjing University of Science and Technology, 2017.
5	林襄. 雷达目标多分量微动信号参数估计与分离技术研究[D]. 长沙: 国防科学技术大学, 2016.
	LIN X. Research on radar multi-component micro-Doppler signal decomposition and parameter estimation[D]. Changsha: National University of Defense Technology, 2016.
6	李昆, 朱卫纲. 利用生成对抗网络的时频图像去噪和增强技术[J]. 电讯技术, 2020, 60 (5): 517- 523.
	LI K , ZHU W G . Time-frequency image denoising and enhancement processing based on generative adversarial network[J]. Telecommunication Engineering, 2020, 60 (5): 517- 523.
7	TORRES L, FRERY A C. SAR image despeckling algorithms using stochastic distances and nonlocal means[EB/OL]. [2021-02-01]. https//arxiv. org/abs/1308.4338v1.
8	HEO Y C , KIM K , LEE Y . Image denoising using non-local means (NLM) approach in magnetic resonance (MR) imaging: a systematic review[J]. Applied Sciences-Basel, 2020, 10 (20): 7028. doi: 10.3390/app10207028
9	CHEN H O , KONG N S P , IBRAHIM H . Bi-histogram equalization with a plateau limit for digital image enhancement[J]. IEEE Trans.on Consumer Electronics, 2009, 55 (4): 2072- 2080. doi: 10.1109/TCE.2009.5373771
10	RODRIGUES C , PEIXOTO Z M A , FERREIRA F M F . Ultrasound image denoising using wavelet thresholding methods in association with the bilateral filter[J]. IEEE Latin America Transactions, 2019, 17 (11): 1800- 1807. doi: 10.1109/TLA.2019.8986417
11	PURANIKMATH S S , KALIYAPERUMAL V . Enhancement of SAR images using curvelet with controlled shrinking technique[J]. Remote Sensing Letters, 2016, 7 (1): 21- 30.
12	LIU S Q , SHI M Z , HU S H , et al. Synthetic aperture radar image denoising based on Shearlet transform using the context-based model[J]. Physical Communication, 2014, 13 (Part C): 221- 229.
13	LI Y . A multifeature extraction method using deep residual network for MR image denoising[J]. Computational & Mathematical Methods in Medicine, 2020, doi: 10.1155/2020/8823861
14	ZHANG Z H , LIU Y P , LIU J N , et al. AMP-net: denoising-based deep unfolding for compressive image sensing[J]. IEEE Trans.on Image Processing, 2021, 30 (1): 1487- 1500.
15	ZHANG Q , WU Y , ZHAO W , et al. Multiple-scale salient-region detection of SAR image based on Gamma distribution and local intensity variation[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11 (8): 1370- 1374. doi: 10.1109/LGRS.2013.2293508
16	TU S , SU Y . Fast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images[J]. IEEE Trans.on Geoscience & Remote Sensing, 2016, 54 (10): 5729- 5744.
17	WANG H P , XU F , CHEN S S . Saliency detector for SAR images based on pattern recurrence[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 9 (7): 2891- 2900.
18	WANG S G , WANG M , YANG S Y , et al. New hierarchical saliency filtering for fast ship detection in high-resolution SAR images[J]. IEEE Trans.on Geoscience & Remote Sensing, 2017, 55 (1): 351- 362.
19	NI W P , MA L , YAN W D , et al. Background context-aware-based SAR image saliency detection[J]. IEEE Geoscience & Remote Sensing Letters, 2018, 15 (9): 1392- 1396.

算法	运行时间
均值滤波	0.002 8
中值滤波	0.029
高斯滤波	0.029
非局部均值滤波	158.2
本文所提算法	0.24

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