【摘要】：對于主動聲吶目標(biāo)檢測與識別這一水聲領(lǐng)域研究熱點(diǎn),如何從接收信號中檢測到目標(biāo)回波,并從回波中提取目標(biāo)特征信息是其基礎(chǔ),也是其關(guān)鍵技術(shù)。根據(jù)目標(biāo)回波與背景噪聲在時(shí)頻域上的分布特點(diǎn)以及目標(biāo)回波亮點(diǎn)間的時(shí)頻差異,人們更傾向于使用時(shí)頻方法對該非平穩(wěn)信號進(jìn)行處理。隨著人們對于高階譜的興趣的提升,高階譜己經(jīng)成為一種分析非高斯過程,提取信號更多譜信息的重要工具,但是它并不適用于非平穩(wěn)過程。由此本文提出使用時(shí)變高階譜——Wigner高階譜來進(jìn)行目標(biāo)回波檢測與亮點(diǎn)提取。本文根據(jù)Wigner高階譜的定義,推導(dǎo)了 CW信號的Wigner雙譜和Wigner三譜的基本表達(dá)式,仿真研究了 Wigner高階譜在高斯白噪聲背景下對CW信號和LFM信號的檢測能力,并對其進(jìn)行簡要分析。研究了多分量CW信號和LFM信號的Wigner雙譜和Wigner三譜的交叉項(xiàng)問題,給出不同信號形式下交叉項(xiàng)的不同特點(diǎn),指出模糊域核函數(shù)交叉項(xiàng)抑制方法存在的不足。針對該缺點(diǎn),根據(jù)形態(tài)學(xué)處理的基本算法及其作用,結(jié)合多分量Wigner雙譜和Wigner三譜交叉項(xiàng)特點(diǎn),本文提出了基于形態(tài)學(xué)處理的多分量信號Wigner高階譜交叉項(xiàng)處理方法,并且仿真驗(yàn)證了該方法的有效性。此外,根據(jù)噪聲與信號在Wigner高階譜域的分布特點(diǎn),指出該方法同樣可以用于噪聲的抑制處理。基于目標(biāo)回波亮點(diǎn)模型,本文給出了典型水下目標(biāo)模型回波在不同入射角度下的幾何亮點(diǎn)的個(gè)數(shù)及相互間的時(shí)延差,并對其進(jìn)行高階時(shí)頻特性分析。采用基于形態(tài)學(xué)交叉項(xiàng)處理方法,使用Wigner高階譜進(jìn)行目標(biāo)回波亮點(diǎn)處理,通過仿真分析,給出了該方法的適用范圍。最后根據(jù)實(shí)驗(yàn)數(shù)據(jù)處理結(jié)果,驗(yàn)證了目標(biāo)回波亮點(diǎn)模型的正確性,驗(yàn)證了 Wigner高階譜與形態(tài)學(xué)相結(jié)合的幾何亮點(diǎn)提取方法的有效性,驗(yàn)證了上述方法在目標(biāo)回波檢測中的有效性。
[Abstract]:For active sonar target detection and recognition, which is a research hotspot in underwater acoustic field, how to detect the target echo from the received signal and extract the target characteristic information from the echo is the foundation and the key technology. According to the distribution characteristics of target echo and background noise in time-frequency domain and the time-frequency difference between the bright spots of target echo, people prefer to use time-frequency method to process the non-stationary signal. With the increasing interest in higher-order spectra, high-order spectrum has become an important tool for analyzing non-Gao Si processes and extracting more spectral information from signals, but it is not suitable for non-stationary processes. In this paper, a time-varying high-order spectrum, Wigner high-order spectrum, is proposed for target echo detection and bright spot extraction. Based on the definition of Wigner high-order spectrum, the basic expressions of Wigner bispectrum and Wigner trispectrum of CW signal are derived. The ability of Wigner high-order spectrum to detect CW signal and LFM signal under the background of Gao Si white noise is simulated and analyzed briefly. In this paper, the crossover of Wigner bispectrum and Wigner trispectrum of multicomponent CW signal and LFM signal is studied. The different characteristics of crossover terms in different signal forms are given, and the shortcomings of the method of suppressing crossover terms in fuzzy domain kernel function are pointed out. In view of this shortcoming, according to the basic algorithm of morphological processing and its function, combined with the characteristics of multi-component Wigner bispectrum and Wigner trispectral crossover, this paper proposes a multi-component signal Wigner high-order spectral crossover processing method based on morphological processing. The effectiveness of the method is verified by simulation. In addition, according to the distribution of noise and signal in Wigner higher-order spectral domain, it is pointed out that this method can also be used for noise suppression. Based on the target echo bright spot model, this paper presents the number of geometric highlights and the time delay difference between them under different incident angles of typical underwater target model, and analyzes their higher-order time-frequency characteristics. Based on morphological cross term processing method, Wigner high order spectrum is used to deal with the bright spot of target echo. Through simulation analysis, the application range of this method is given. Finally, according to the experimental data processing results, the correctness of the target echo bright spot model is verified, and the validity of the geometric bright spot extraction method combined with Wigner high order spectrum and morphology is verified. The effectiveness of the above method in target echo detection is verified.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TB56

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