基于變參數(shù)隨機(jī)共振和歸一化變換的時(shí)變信號(hào)檢測(cè)與恢復(fù)
doi: 10.11999/JEIT141618 cstr: 32379.14.JEIT141618
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(51475441, 11274300)
Time-varying Signal Detection and Recovery Method Based on Varying Parameter Stochastic Resonance and Normalization Transformation
-
摘要: 非線性隨機(jī)共振系統(tǒng)具有利用噪聲增強(qiáng)微弱信號(hào)的能力，為強(qiáng)噪聲背景下的信號(hào)檢測(cè)開(kāi)辟了新的途徑。該文提出一種變參數(shù)隨機(jī)共振(VPSR)模型，實(shí)現(xiàn)對(duì)非周期信號(hào)的有效檢測(cè)、噪聲去除和信號(hào)恢復(fù)。通過(guò)以恢復(fù)信號(hào)的擬合決定系數(shù)和互相關(guān)系數(shù)作為評(píng)判標(biāo)準(zhǔn)，研究分析了不同參數(shù)變化對(duì)系統(tǒng)輸出的影響，分析結(jié)果表明該模型能有效地從噪聲背景中恢復(fù)時(shí)變信號(hào)。該方法拓展了隨機(jī)共振用于時(shí)變信號(hào)檢測(cè)技術(shù)的領(lǐng)域，在時(shí)變信號(hào)檢測(cè)和處理以及雷達(dá)通訊等方向有著一定的潛在應(yīng)用價(jià)值。
-
關(guān)鍵詞:
- 信號(hào)處理 /
- 變參數(shù)隨機(jī)共振 /
- 時(shí)變信號(hào) /
- 微弱信號(hào)探測(cè) /
- 信號(hào)恢復(fù)
Abstract: The nonlinear stochastic resonance system has the ability to take advantage of background noise to enhance the weak signal among it. It provides the new approach to detect the weak signal embedded with heavy noise. This study proposes a new Varying Parameter Stochastic Resonance (VPSR) model. The model performs well in the detection of a time-varying signal with noise as well as the denoising and signal recovery. This study takes the determination coefficient and cross correlation coefficient as the criteria and analyzes the influence of different parameters variation on the system output. The simulation results indicate the model performs better in the time-varying signal recovery than the traditional one. The proposed method develops the area of time-varying signal detection with stochastic resonance which can be hoped to be widely used in the aperiodic signal processing, radar communication, etc. due to its superiority. -
Benzi R, Sutera A, and Vulpiani A. The mechanism of stochastic resonance[J]. Journal of Physics A: Mathematical and General, 1981, 14(11): L453-L457. Gammaitoni L, Hanggi P, Jung P, et al.. Stochastic resonance[J]. Reviews of Modern Physics, 1998, 70(1): 223-287. 鄭仕譜. 基于隨機(jī)共振的弱信號(hào)提取方法研究[D]. [碩士論文], 浙江大學(xué), 2014. Zheng Shi-pu. Study on weak signal extraction technology using stochastic resonance[D]. [Master dissertation], Zhejiang University, 2014. 楊祥龍, 汪樂(lè)宇. 一種強(qiáng)噪聲背景下弱信號(hào)檢測(cè)的非線性方法[J]. 電子與信息學(xué)報(bào), 2002, 24(6): 811-815. Yang Xiang-long and Wang Le-yu. A new detection method of weak signal in strong background-noise level[J]. Journal of Electronics Information Technology, 2002, 24(6): 811-815. 梁軍利, 楊樹(shù)元, 唐志峰. 基于隨機(jī)共振的微弱信號(hào)檢測(cè)[J]. 電子與信息學(xué)報(bào), 2006, 28(6): 1068-1072. Liang Jun-li, Yang Shu-yuan, and Tang Zhi-feng. Weak signal detection based on stochastic resonance[J]. Journal of Electronics Information Technology, 2006, 28(6): 1068-1072. 王強(qiáng). 基于變步長(zhǎng)隨機(jī)共振的弱信號(hào)檢測(cè)技術(shù)[J]. 信息通信, 2014, 12(8): 25. Wang Qiang. Weak signal detection based on step-change stochastic resonance[J]. Information Communications, 2014, 12(8): 25. Li Ji-meng, Chen Xue-feng, and He Zheng-jia. Multi-stable stochastic resonance and its application research on mechanical fault diagnosis[J]. Journal of Sound and Vibration, 2013, 332(22): 5999-6015. 冷永剛, 田祥友. 一階線性系統(tǒng)隨機(jī)共振在轉(zhuǎn)子軸故障診斷中的應(yīng)用研究[J]. 振動(dòng)與沖擊, 2014, 62(17): 1-5. Leng Yong-gang and Tian Xiang-you. Application of a first-order linear system's stochastic resonance in fault diagnosis of rotor shaft[J]. Journal of Vibration and Shock, 2014, 62(17): 1-5. Lu Si-liang, He Qing-bo, and Kong Fan-rang. Stochastic resonance with Woods-Saxon potential for rolling element bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2014, 45(2): 488-503. Xu Jia-wen, Shao Wei-wei, Kong Fan-rang, et al.. Right-angle piezoelectric cantilever with improved energy harvesting efficiency[J]. Applied Physics Letters, 2010, 96(15): 152904(1-3). Harne R L and Wang K W. A review of the recent research on vibration energy harvesting via bistable systems[J]. Smart Materials and Structures, 2013, 22(2): 023001(1-12). 朱文濤, 蘇濤, 楊濤, 等. 線性調(diào)頻連續(xù)波信號(hào)檢測(cè)與參數(shù)估計(jì)算法[J]. 電子與信息學(xué)報(bào), 2014, 36(3): 552-558. Zhu Wen-tao, Su Tao, Yang Tao, et al.. Detection and parameter estimation of linear frequency modulation continuous wave signal[J]. Journal of Electronics Information Technology, 2014, 36(3): 552-558. 朱健東, 趙擁軍, 唐江. 線性調(diào)頻連續(xù)波信號(hào)的周期分?jǐn)?shù)階Fourier變換檢測(cè)與估計(jì)[J]. 電子與信息學(xué)報(bào), 2013, 35(8): 1827-1833. Zhu Jian-dong, Zhao Yong-jun, and Tang Jiang. Periodic FRFT based detection and estimation for LFMCW signal[J]. Journal of Electronics Information Technology, 2013, 35(8): 1827-1833. Collins J J, Chow C C, Capela A C, et al.. Aperiodic stochastic resonance[J]. Physical Review E, 1996, 54(5): 5575-5584. 段江海, 宋愛(ài)國(guó). 雙穩(wěn)系統(tǒng)中非周期隨機(jī)共振的數(shù)值仿真[J]. 電路與系統(tǒng)學(xué)報(bào), 2004, 9(5): 149-152. Duan Jiang-hai and Song Ai-guo. Numerical simulations of aperiodic stochastic resonance in bistable systems[J]. Journal of Circuits and Systems, 2004, 9(5): 149-152. Wang Xiao-min and Wen Chuan. Detection and parameter estimation of LFM signal based on stochastic resonance[C]. Proceedings of the 2011 2nd International Conference on Networking and Information Technology, Singapore, 2011, 112-119. Peng Hao, Zhong Su-chuan, Tu Zhe, et al.. Stochastic resonance of over-damped bistable system driven by chirp signal and Gaussian white noise[J]. Acta Physica Sinica, 2013, 62(8): 080501(1-6). Yang Ding-xin, Hu Zheng, and Yang Yong-min. The analysis of stochastic resonance of periodic signal with large parameters[J]. Acta Physica Sinica, 2012, 61(8): 080501(1-10). Leng Yong-gang, Wang Tai-yong, Qin Xu-da, et al.. Power spectrum research of twice sampling stochastic resonance response in a bistable system[J]. Acta Physica Sinica, 2004, 53(3): 717-723. -
計(jì)量
- 文章訪問(wèn)數(shù): 1316
- HTML全文瀏覽量: 159
- PDF下載量: 548
- 被引次數(shù): 0
下載:
