基于先驗(yàn)估計(jì)的自適應(yīng)Chirplet信號(hào)展開(kāi)
Adaptive Chirplet Signal Expansion Based on Transcendental Estimation
-
摘要: 該文提出一種新的時(shí)頻表示方法--自適應(yīng)線性調(diào)頻小波(Chirplet)信號(hào)展開(kāi)算法。算法基于信號(hào)本征空間,融參數(shù)的初值估計(jì)和精確估計(jì)于一體,利用匹配追蹤算法將信號(hào)自適應(yīng)地展開(kāi)在高斯線性調(diào)頻小波基函數(shù)集上。通過(guò)展開(kāi)系數(shù)和基函數(shù)參數(shù)獲得信號(hào)的時(shí)頻分布,其時(shí)頻聚集性、抗噪性和時(shí)頻分辨率不僅優(yōu)于一般的時(shí)頻分布而且優(yōu)于已有的自適應(yīng)時(shí)頻分布,可以更好地刻畫(huà)信號(hào)的本質(zhì)。應(yīng)用數(shù)值仿真檢驗(yàn)了算法的有效性和時(shí)頻分布的優(yōu)良性能。Abstract: In this paper, a new time-frequency representation method, adaptive signal expansion algorithm! is presented. The algorithm is based on that essential character of signal space, initial value estimation and precise resolution are obtained simultaneously. Signal is adaptively expanded to a sum of chirplet elementary functions by using match pursuit algorithm. Then, according to expansion coefficients and elementary function parameters, adaptive time frequency distribution is obtained. Its time frequency congregate, noise-reduction and time frequency resolution are not only better than the general time frequency distribution but also better than adaptive time frequency distribution reported and it is able to characterize the signals nature exactly. The validity of the algorithm and the performance of adaptive time frequency distribution are tested by numerical simulations.
-
Mihovilovic D, Bracewell R N. Adaptive chirplet representation of signal on time-frequency plane[J].Electronics Letters.1991,27(13):1159-[2]Mann S, Haykin S. The chirplet transform: physical considerations[J].IEEE Trans. on Signal Processing.1995, 43 (11):2745-2761[3]Qian Shie, Chen Dapang, Chen Kebo. Signal approximation via data-adaptive normalized Gaussian functions and its application for speech processing[J]. IEEE Trans. on Acoustics, Speech, and SignalProcessing, 1992, 1(1): 141 - 144.[4]Mallat S G, Zhang Zhifeng. Matching pursuits with timefrequency dictionaries[J].IEEE Trans. on Signal Processing.1993, 41(12):3397-[5]Bultan A. A four-parameter atomic decomposition of chirplets[J].IEEE Trans. on Signal Processing.1999, 47(3):731-[6]殷勤業(yè),倪志芳,錢(qián)世鍔,等.自適應(yīng)旋轉(zhuǎn)投影分解法[J].電子學(xué)報(bào),1997,25(4):52-58.[7]Qian Shie, Chen Dapang, Yin Qinye. Adaptive chirplet based signal approximation[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1998, 3(5): 1781 - 1784.[8]鄒虹,保錚.一種有效的基于Chirplet自適應(yīng)信號(hào)分解算法[J].電子學(xué)報(bào), 2001, 29(4): 515 - 517.[9]Yin Qinye, Qian Shie, Feng Aigang. A fast refinement for adaptive Gaussian chirplet decomposition[J].IEEE Trans. on Signal Processing.2002, 50(6):1298- -
計(jì)量
- 文章訪問(wèn)數(shù): 2362
- HTML全文瀏覽量: 128
- PDF下載量: 762
- 被引次數(shù): 0
下載:
