【摘要】：通信信號的調(diào)制方式識別和參數(shù)估計(jì)對于軍用電子對抗以及民用頻譜監(jiān)管都具有極其重要的意義。由于無線通信環(huán)境的復(fù)雜性和不可預(yù)知性,在低信噪比條件下,傳統(tǒng)的參數(shù)估計(jì)和調(diào)制方式識別方法難以達(dá)到預(yù)期的效果�；谘h(huán)譜分析的信號處理具有較強(qiáng)的抑制噪聲能力,已被廣泛應(yīng)用到參數(shù)估計(jì)和特征參數(shù)提取等方面,但由于循環(huán)譜理論分析方法還存在很多問題,本文在閱讀國內(nèi)外最新的思想和研究后,研究用新的一些方法對信號進(jìn)行參數(shù)估計(jì)和調(diào)制方式識別。本文首先分析了幾種常用數(shù)字和模擬調(diào)制信號的循環(huán)譜周期圖,并通過頻域平滑算法分析了信號載頻和碼元速率與頻率分辨率的奇偶性對循環(huán)譜估計(jì)的影響,推導(dǎo)出基于循環(huán)譜的聯(lián)合截面搜索算法。其次為解決高階MPSK信號因?yàn)楸额l及平方變換引起的隨機(jī)噪聲問題,提出通過對信號進(jìn)行希爾伯特變換的方法達(dá)到降低噪聲的目的。另外,通過數(shù)學(xué)推導(dǎo)和實(shí)驗(yàn)仿真驗(yàn)證分析了調(diào)制信號在譜特征截面的分類特征參數(shù)以及基于循環(huán)譜分析的調(diào)制信號識別流程,并且分析了矩陣抽取算法,引入了相像系數(shù)并給出適用于低信噪比情況下的特征參數(shù),通過仿真驗(yàn)證了該算法的引入在低信噪比條件下可得更好的識別性能。最后分析了影響矩陣抽取算法的兩個主要因素抽取系數(shù)積與離散傅里葉變換點(diǎn)數(shù)對算法性能的影響,通過仿真推出抽取系數(shù)積不宜過大且離散傅里葉變換點(diǎn)數(shù)不宜太小的情況下才能得到較好的識別效果。
[Abstract]:Modulation mode identification and parameter estimation of communication signals are of great significance for military electronic countermeasures and civil spectrum regulation. Due to the complexity and unpredictability of wireless communication environment, the traditional methods of parameter estimation and modulation recognition are difficult to achieve the desired results under the condition of low signal-to-noise ratio (SNR). The signal processing based on cyclic spectrum analysis has strong ability to suppress noise, which has been widely used in parameter estimation and feature parameter extraction, but there are still many problems in the theory of cyclic spectrum analysis. After reading the latest ideas and researches at home and abroad, this paper studies some new methods for parameter estimation and modulation recognition. In this paper, the cyclic spectral periodic diagrams of several commonly used digital and analog modulated signals are first analyzed, and the influence of signal carrier frequency, symbol rate and frequency resolution on cyclic spectrum estimation is analyzed by frequency domain smoothing algorithm. A joint cross section search algorithm based on cyclic spectrum is derived. Secondly, in order to solve the problem of random noise caused by frequency doubling and square transformation in high-order MPSK signals, the method of Hilbert transform is proposed to reduce the noise. In addition, the classification characteristic parameters of modulation signal in spectral feature section and the recognition flow of modulation signal based on cyclic spectrum analysis are analyzed by mathematical derivation and experimental simulation, and the matrix extraction algorithm is analyzed. The image coefficient is introduced and the characteristic parameters suitable for low signal-to-noise ratio (SNR) are given. The simulation results show that the proposed algorithm can achieve better recognition performance under the condition of low signal-to-noise ratio (SNR). Finally, the effects of two main factors, the extraction coefficient product and the number of discrete Fourier transform (DFT), on the performance of the algorithm are analyzed. It is concluded by simulation that the extraction coefficient product should not be too large and the number of discrete Fourier transform points should not be too small in order to obtain a better recognition effect.
【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】：TN911.3

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