本文選題：時(shí)頻分析 + 信號(hào)分離�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著戰(zhàn)場(chǎng)電磁環(huán)境的日趨復(fù)雜,戰(zhàn)場(chǎng)中截獲到的雷達(dá)信號(hào)也日益繁雜,不僅調(diào)制種類繁多,而且疊加進(jìn)入接收機(jī)的分量個(gè)數(shù)也在加劇。在復(fù)雜的截獲信號(hào)中得到各分量的類型和參數(shù),進(jìn)而對(duì)各輻射源進(jìn)行正確識(shí)別和高效干擾是制定戰(zhàn)略決策的重要因素。要正確的分析截獲的多分量雷達(dá)信號(hào),將其包含的分量提取和重建是一個(gè)不可避免的過程,本文重點(diǎn)基于信號(hào)的時(shí)頻分布特征對(duì)多分量信號(hào)的提取和重建技術(shù)進(jìn)行探討。首先,總結(jié)了常見雷達(dá)信號(hào)類型的時(shí)頻特征和稀疏特征,并進(jìn)一步研究了多分量雷達(dá)信號(hào)在時(shí)頻分布中存在的規(guī)律。研究發(fā)現(xiàn)計(jì)算時(shí)頻分布時(shí)不可避免的需要在干擾項(xiàng)抑制和信號(hào)項(xiàng)模糊之間均衡選擇,因此引入自適應(yīng)方向核的二次時(shí)頻分布。該時(shí)頻分布根據(jù)信號(hào)項(xiàng)和干擾項(xiàng)在模糊域的特征,通過自適應(yīng)選擇模糊域方向核,在干擾項(xiàng)抑制和信號(hào)項(xiàng)模糊之間達(dá)到較為理想的均衡。然后,為了獲取信號(hào)的分量結(jié)構(gòu)研究了信號(hào)的瞬時(shí)頻率估計(jì)算法,研究發(fā)現(xiàn)已有算法不適用于存在交叉分量的信號(hào),因此引入了梯度旋轉(zhuǎn)方法來增強(qiáng)時(shí)頻分布圖像,并提出了基于端點(diǎn)梯度的片段連接和擬合算法,不僅消除分量瞬時(shí)頻率跟蹤錯(cuò)誤,也降低了估計(jì)誤差。最后,在瞬時(shí)頻率估計(jì)的基礎(chǔ)上采用時(shí)變?yōu)V波方法提取和重建各個(gè)分量信號(hào),分析發(fā)現(xiàn)時(shí)變?yōu)V波在分量交叉處存在較大的畸變,因此引入幅度校正算法并提出基于時(shí)變階的短時(shí)分?jǐn)?shù)階傅立葉變換時(shí)變?yōu)V波算法。所提算法大幅度的提升了信號(hào)提取和重建的準(zhǔn)確性,特別是針對(duì)非線性調(diào)頻信號(hào)的提取和重建。論文針對(duì)多分量信號(hào)時(shí)頻分析中存在交叉項(xiàng)干擾與信號(hào)項(xiàng)模糊相矛盾問題,引入了自適應(yīng)方向核的時(shí)頻分布算法獲得了較理想的時(shí)頻分布圖像。進(jìn)一步在該圖像上使用旋轉(zhuǎn)梯度增強(qiáng)和瞬時(shí)頻率連接擬合算法,取出了各分量的瞬時(shí)頻率。最后采用基于時(shí)變階的短時(shí)分?jǐn)?shù)階傅立葉變換的時(shí)變?yōu)V波算法,提取并重建出各個(gè)分量的時(shí)域波形。將信號(hào)的提取和重建分解為信號(hào)建模、時(shí)頻分析、瞬時(shí)頻率提取和時(shí)變?yōu)V波四個(gè)步驟進(jìn)行,形成了一套完整有效的多分量雷達(dá)信號(hào)提取和重建的方案。
[Abstract]:With the increasing complexity of the electromagnetic environment of the battlefield, the radar signals intercepted in the battlefield are becoming more and more complicated. Not only there are many kinds of modulation, but also the number of components superimposed into the receiver is increasing. It is an important factor to make strategic decision to obtain the types and parameters of each component in the complex intercepted signal, and then identify the emitter correctly and interfere with each other efficiently. In order to correctly analyze the captured multi-component radar signal, it is an inevitable process to extract and reconstruct the components contained in it. This paper focuses on the multi-component signal extraction and reconstruction technology based on the time-frequency distribution characteristics of the signal. Firstly, the time-frequency characteristics and sparse features of common radar signal types are summarized, and the existence of multi-component radar signals in time-frequency distribution is further studied. It is found that when computing time-frequency distribution, it is necessary to choose between interference suppression and fuzzy signal term, so the quadratic time-frequency distribution of adaptive direction kernel is introduced. According to the feature of signal and interference term in fuzzy domain, the time-frequency distribution adaptively selects the direction kernel of fuzzy domain, and achieves a more ideal balance between interference term suppression and signal term ambiguity. Then, in order to obtain the component structure of the signal, the instantaneous frequency estimation algorithm is studied. It is found that the existing algorithm is not suitable for the signal with cross components, so a gradient rotation method is introduced to enhance the time-frequency distribution image. A segment connection and fitting algorithm based on endpoint gradient is proposed, which not only eliminates the instantaneous frequency tracking error, but also reduces the estimation error. Finally, based on the instantaneous frequency estimation, the time-varying filtering method is used to extract and reconstruct each component signal, and it is found that the time-varying filter has a large distortion at the intersection of the components. Therefore, an amplitude correction algorithm is introduced and a short-time fractional Fourier transform time-varying filtering algorithm based on time-varying order is proposed. The proposed algorithm greatly improves the accuracy of signal extraction and reconstruction, especially for nonlinear FM signal extraction and reconstruction. In order to solve the problem that there is a contradiction between the crossover interference and the ambiguity of the signal in multi-component signal time-frequency analysis, an adaptive direction-kernel time-frequency distribution algorithm is introduced to obtain an ideal time-frequency distribution image. Furthermore, the instantaneous frequency of each component is extracted by using rotation gradient enhancement and instantaneous frequency connection fitting algorithm on the image. Finally, the time-varying filtering algorithm based on short-time fractional Fourier transform is used to extract and reconstruct the time-domain waveforms of each component. The signal extraction and reconstruction are decomposed into four steps: signal modeling, time-frequency analysis, instantaneous frequency extraction and time-varying filtering, which form a complete and effective scheme for multi-component radar signal extraction and reconstruction.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN957.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 劉歌;汪洪艷;張國毅;;基于時(shí)頻圖像處理方法的多分量信號(hào)分離[J];電子信息對(duì)抗技術(shù);2017年02期

2 劉歌;張國毅;胡鑫磊;王曉峰;;基于時(shí)頻圖像處理的多分量LFM信號(hào)分離[J];航天電子對(duì)抗;2015年05期

3 ;FM interference suppression for PRC-CW radar based on adaptive STFT and time-varying filtering[J];Journal of Systems Engineering and Electronics;2010年02期

4 趙兆;是湘全;;基于STFT和時(shí)變?yōu)V波的調(diào)頻干擾抑制方法[J];探測(cè)與控制學(xué)報(bào);2009年03期

5 馬世偉;謝為群;朱曉錦;陳光化;;基于參數(shù)自適應(yīng)時(shí)頻分布的瞬時(shí)頻率估計(jì)[J];儀器儀表學(xué)報(bào);2006年11期

6 李滔;湯建龍;楊紹全;;基于時(shí)頻分布的信號(hào)相位跳變檢測(cè)與估計(jì)方法[J];信號(hào)處理;2006年01期

相關(guān)博士學(xué)位論文 前2條

1 蔡權(quán)偉;多分量信號(hào)的信號(hào)分量分離技術(shù)研究[D];電子科技大學(xué);2006年

2 鄒虹;多分量線性調(diào)頻信號(hào)的時(shí)頻分析[D];西安電子科技大學(xué);2000年

相關(guān)碩士學(xué)位論文 前1條

1 羊初發(fā);基于EMD的時(shí)頻分析與濾波研究[D];電子科技大學(xué);2009年

，

本文編號(hào)：1846735