本文關(guān)鍵詞： 海雜波 微弱信號(hào)檢測 混沌 噪聲 分形　出處：《南京信息工程大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：海雜波受到諸如海浪、海風(fēng)和潮汐等環(huán)境因素影響,具有類似噪聲的特性,是典型的非平穩(wěn)信號(hào)。在雷達(dá)對(duì)海進(jìn)行觀測時(shí),海雜波大量尖峰會(huì)嚴(yán)重影響雷達(dá)對(duì)海監(jiān)測效果,由于小目標(biāo)的雷達(dá)反射截面積很小,容易淹沒在海雜波和噪聲中。傳統(tǒng)檢測方法存在精度低、泛化性差和實(shí)時(shí)性欠佳的問題。如何從海雜波背景下有準(zhǔn)確、可靠地發(fā)現(xiàn)小目標(biāo),是當(dāng)前雷達(dá)信號(hào)處理領(lǐng)域的研究重點(diǎn)。本文研究了混沌混合信號(hào)的噪聲抑制和利用問題,分別提出基于EMD方差特性的混沌信號(hào)自適應(yīng)去噪算法和基于粒子群優(yōu)化的自適應(yīng)隨機(jī)共振檢測方法。研究了海雜波在FRFT域下的分形特性,在單尺度和高尺度條件下,分別提出了基于階數(shù)自適應(yīng)的小目標(biāo)檢測方法和基于多重分形的小目標(biāo)檢測方法。引入分形聚類方法篩選海雜波數(shù)據(jù),用于支持向量機(jī)的選擇性集成學(xué)習(xí),提出了基于自適應(yīng)分形聚類的微弱信號(hào)檢測方法。具體的研究重點(diǎn)如下：基于經(jīng)驗(yàn)?zāi)B(tài)分解理論,研究了不同混沌系統(tǒng)分解分量的方差特性,發(fā)現(xiàn)噪聲導(dǎo)致總分解層數(shù)的增加和分解分量方差最大值所在層數(shù)增大,確定需要去噪處理的分量層數(shù),結(jié)合提升小波的優(yōu)勢,提出一種基于EMD方差特性的混沌信號(hào)自適應(yīng)去噪算法。采用Lorenz、Chen系統(tǒng)和實(shí)測海雜波雷達(dá)數(shù)據(jù)進(jìn)行實(shí)驗(yàn)。結(jié)果表明,在低噪條件下,比傳統(tǒng)小波去噪方法均方誤差降低30%以上,信噪比提高15dB-3.5dB,可在保留有用信號(hào)的基礎(chǔ)上有效地去除海雜波中的噪聲,提高海雜波數(shù)據(jù)質(zhì)量。根據(jù)隨機(jī)共振系統(tǒng)中利用噪聲能量增強(qiáng)待測微弱信號(hào)的特性,研究二維Duffing振子參數(shù)對(duì)隨機(jī)共振輸出信噪比的影響。利用粒子群算法全局優(yōu)化的特點(diǎn),對(duì)二維Duffing振子三個(gè)參數(shù)進(jìn)行優(yōu)化,提出一種基于粒子群的自適應(yīng)隨機(jī)共振的微弱信號(hào)檢測方法,將自適應(yīng)隨機(jī)共振微弱信號(hào)檢測問題轉(zhuǎn)化為參數(shù)并行尋優(yōu)問題,實(shí)測海雜波數(shù)據(jù)實(shí)驗(yàn)表明,輸出信噪比提升明顯,能有效地從海雜波背景中檢測到微弱周期信號(hào)。研究了海雜波在FRFT域下的分形特點(diǎn),采用分?jǐn)?shù)布朗運(yùn)動(dòng)建模,推導(dǎo)證明其受階數(shù)和尺度的雙重影響。根據(jù)FRFT補(bǔ)償雷達(dá)信號(hào)速度和加速度補(bǔ)償?shù)奶攸c(diǎn),在單尺度下提出基于階數(shù)自適應(yīng)的海雜波小目標(biāo)檢測方法,有效地提高了檢測門限,比分形時(shí)域檢測方法提高26.3%。在高尺度下提出基于高尺度多重分形的小目標(biāo)檢測方法,發(fā)現(xiàn)在負(fù)高尺度上純海雜波與目標(biāo)單元差異明顯,兩種方法均較好地解決海清變化對(duì)小目標(biāo)檢測的干擾。進(jìn)一步引入分形聚類法篩選海雜波數(shù)據(jù),用于提高支持向量機(jī)訓(xùn)練效率,提升了海雜波背景下的微弱信號(hào)檢測性能。本文通過對(duì)海雜波噪聲背景下小目標(biāo)的特性進(jìn)行分析研究,結(jié)合經(jīng)驗(yàn)?zāi)B(tài)分解、隨機(jī)共振和分形等理論,提出了海雜波去噪和微弱信號(hào)檢測方法,較好地緩解了海情對(duì)目標(biāo)信號(hào)的干擾,對(duì)海面小目標(biāo)的識(shí)別和海面安全監(jiān)測具有一定的理論意義和實(shí)際應(yīng)用價(jià)值。
[Abstract]:Sea clutter is affected by wind and tidal waves, effects of environmental factors such as characteristics, similar to noise, is a typical non-stationary signal. In the observation of radar on sea, sea clutter large spikes will seriously affect the radar on sea monitoring effect, because the RCS Target is very small, easy to drown in the sea clutter and noise. The traditional detection method of low precision, poor generalization capability and poor real-time problems. From the background of sea clutter accurately, reliably detect small targets, is a current research topic in radar signal processing field. This paper studies the use of noise suppression and chaotic signal problems. Put forward adaptive chaotic signal EMD variance characteristic denoising algorithm based on particle swarm optimization and adaptive stochastic resonance detection method based on the research. The fractal features of sea clutter in FRFT domain, the single scale and high scale. Under the proposed respectively small target detection method based on adaptive order and based on small target detection method of multi fractal. The fractal clustering method for the screening of sea clutter data for selective ensemble learning support vector machine is proposed, weak signal detection method based on adaptive fractal clustering. Focus on specific experience as follows: Based on the theory of modal decomposition, variance decomposition characteristics of components of different chaotic systems, found that noise causes the total decomposition layers increase and decomposition of variance maximum value number increases, determine the need for denoising the component layer, combined with the advantages of wavelet transform, proposed an adaptive denoising algorithm based on chaotic signal based on EMD variance characteristics. Using Lorenz, Chen system and sea clutter radar experimental data. The results show that in low noise conditions, compared with the traditional wavelet denoising method of mean square error The difference is reduced by more than 30%, to improve the signal-to-noise ratio of 15dB-3.5dB, can effectively remove the noise in sea clutter based on retaining the useful signal and improve the quality of sea clutter data. According to the noise energy using stochastic resonance system to enhance properties of weak signal to be measured, influence of two-dimensional Duffing oscillator parameters on ratio of stochastic resonance the output signal to noise. Using the characteristic of particle swarm algorithm for global optimization of two-dimensional Duffing oscillator, three parameters are optimized, a weak signal detection method of adaptive particle swarm optimization based on stochastic resonance, the problem of adaptive stochastic resonance weak signal detection into a parameter optimization problem, the measured sea clutter data experiments show that the output signal-to-noise ratio can significantly enhance the weak periodic signal detection effectively from sea clutter. The sea clutter fractal characteristics of wave in FRFT domain, using the fractional Brown motion model, The derivation is greatly influenced by the order number and scale. According to the characteristics of FRFT radar signal compensation speed and acceleration compensation, adaptive order sea clutter based on small target detection method is proposed in the single scale, effectively improve the detection threshold than the fractal time domain detection method of high 26.3%. of small target detection in high scale based on multi fractal in high scale, found in the negative high scale pure sea clutter and the target unit is significantly different, two methods to solve the interference of Hai Qing change on small target detection based on fractal clustering method. Further screening of sea clutter data is used to improve SVM training efficiency, enhance the weak the performance of signal detection in sea clutter background. Based on the characteristics of small target in sea clutter wave noise background analysis, combined with empirical mode decomposition, stochastic resonance and fractal theory, put forward The method of sea clutter denoising and weak signal detection has better relieved the interference of the sea situation to the target signal, and has certain theoretical significance and practical application value for small target recognition and sea surface safety monitoring.

【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN957.51
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本文編號(hào)：1522550