本文關(guān)鍵詞：基于壓縮傳感理論的雷達(dá)信號(hào)檢測(cè)方法研究　出處：《大連海事大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 壓縮傳感 信號(hào)檢測(cè) 稀疏表示 PPS信號(hào)

【摘要】：信號(hào)檢測(cè)作為雷達(dá)系統(tǒng)中的重要問(wèn)題,一直受到國(guó)內(nèi)外學(xué)者的關(guān)注,主要因?yàn)樾盘?hào)檢測(cè)不管在軍用領(lǐng)域還是民用領(lǐng)域都有著廣泛的應(yīng)用,例如雷達(dá)偵查、船舶安全航行等。為了獲得更好的抗干擾性和更高的分辨率,雷達(dá)系統(tǒng)經(jīng)常采用大時(shí)寬帶寬信號(hào)作為發(fā)射信號(hào)。但在傳統(tǒng)的奈奎斯特采樣框架下,大時(shí)寬帶寬勢(shì)必會(huì)帶來(lái)大數(shù)據(jù)量的采集、傳輸、儲(chǔ)存和處理問(wèn)題。而壓縮傳感理論的出現(xiàn)恰好成為解決這一問(wèn)題的有效工具。多項(xiàng)式相位信號(hào)(PPS)是一種常用的雷達(dá)寬帶信號(hào),因此本文將對(duì)PPS的檢測(cè)問(wèn)題進(jìn)行研究,結(jié)合壓縮傳感理論,分析和實(shí)現(xiàn)PPS信號(hào)的檢測(cè)算法。本文首先研究了 PPS信號(hào)的稀疏表示方法,然后針對(duì)不同階次的PPS信號(hào)構(gòu)造不同的稀疏字典。針對(duì)二階PPS信號(hào),即LFM信號(hào),為其構(gòu)造了波形延時(shí)字典和FRFT正交基字典,并通過(guò)實(shí)驗(yàn)驗(yàn)證這兩種字典都能對(duì)LFM信號(hào)稀疏表示,但FRFT字典的抗白噪聲干擾更強(qiáng)。針對(duì)三階PPS信號(hào),為其構(gòu)造了波形匹配字典。其次研究了基于壓縮傳感檢測(cè)模型的建立以及檢測(cè)模型下壓縮檢測(cè)算法的設(shè)計(jì)與實(shí)現(xiàn)。首先建立了一種高斯白噪聲信道條件下的檢測(cè)模型。現(xiàn)有的檢測(cè)方法有基于稀疏系數(shù)位置的檢測(cè)算法,但其在低信噪條件下檢測(cè)效果不佳,而且針對(duì)的是已知信號(hào)。于是將歸一化殘差引入到檢測(cè)算法中。并分別根據(jù)LFM信號(hào)和三階PPS信號(hào)在雷達(dá)信號(hào)中的不同應(yīng)用,針對(duì)LFM信號(hào),引入歸一化殘差檢測(cè)算法,驗(yàn)證了此算法的有效性。然后將多脈沖檢測(cè)引入到LFM信號(hào)檢測(cè)中,又提出了一種多重檢測(cè)算法和一種積累檢測(cè)算法,經(jīng)過(guò)仿真證明了這兩種多脈沖檢測(cè)算法相對(duì)于單脈沖的歸一化殘差檢測(cè)算法都提高了檢測(cè)性能,并且積累檢測(cè)算法在性能上更具有優(yōu)勢(shì)。針對(duì)三階PPS信號(hào),重點(diǎn)研究了多分量模型下的歸一化殘差的檢測(cè)算法,并根據(jù)歸一化殘差斜率的特性提出了一種信源個(gè)數(shù)估計(jì)算法,最后驗(yàn)證了此算法對(duì)信源個(gè)數(shù)估計(jì)的有效性。
[Abstract]:As an important problem in radar system, signal detection has been concerned by scholars at home and abroad, mainly because signal detection has been widely used in both military and civil fields, such as radar detection. In order to obtain better anti-jamming and higher resolution, radar systems often use wide-band signals as transmitting signals, but under the traditional Nyquist sampling framework. Large time broadband width is bound to bring a large amount of data acquisition, transmission. Storage and processing problems. The emergence of compression sensing theory is an effective tool to solve this problem. Polynomial phase signal (PPS) is one of the commonly used radar wideband signals. Therefore, this paper will study the detection of PPS, combined with the compression sensing theory, analysis and implementation of PPS signal detection algorithm. Firstly, this paper studies the sparse representation of PPS signal. Then we construct different sparse dictionaries for different PPS signals, and construct waveform delay dictionaries and FRFT orthogonal basis dictionaries for second-order PPS signals, that is, LFM signals. The experiments show that the two dictionaries can represent the LFM signals sparsely, but the FRFT dictionaries can resist white noise more strongly, especially for the third-order PPS signals. The waveform matching dictionary is constructed for it. Secondly, the establishment of compression sensor detection model and the design and implementation of compression detection algorithm based on the detection model are studied. Firstly, a detection method based on Gao Si white noise channel is established. Model. The existing detection methods are based on sparse coefficient location detection algorithm. But its detection effect is not good under the condition of low signal noise. Then the normalized residuals are introduced into the detection algorithm, and according to the different applications of LFM signal and third-order PPS signal in radar signal, the LFM signal is targeted. The normalized residual detection algorithm is introduced to verify the effectiveness of the algorithm. Then multi-pulse detection is introduced into LFM signal detection and a multi-detection algorithm and an accumulation detection algorithm are proposed. The simulation results show that the two multi-pulse detection algorithms improve the detection performance compared with the normalized residual detection algorithm of single pulse, and the cumulative detection algorithm has more advantages in performance. For third-order PPS signals. In this paper, the normalized residual detection algorithm based on multi-component model is studied. According to the characteristic of normalized residual slope, a source number estimation algorithm is proposed. Finally, the validity of this algorithm for estimating the number of information sources is verified.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN957.51

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