本文關鍵詞：基于壓縮傳感理論的雷達信號檢測方法研究　出處：《大連海事大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 壓縮傳感 信號檢測 稀疏表示 PPS信號

【摘要】：信號檢測作為雷達系統(tǒng)中的重要問題,一直受到國內外學者的關注,主要因為信號檢測不管在軍用領域還是民用領域都有著廣泛的應用,例如雷達偵查、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,雷達系統(tǒng)經常采用大時寬帶寬信號作為發(fā)射信號。但在傳統(tǒng)的奈奎斯特采樣框架下,大時寬帶寬勢必會帶來大數據量的采集、傳輸、儲存和處理問題。而壓縮傳感理論的出現恰好成為解決這一問題的有效工具。多項式相位信號(PPS)是一種常用的雷達寬帶信號,因此本文將對PPS的檢測問題進行研究,結合壓縮傳感理論,分析和實現PPS信號的檢測算法。本文首先研究了 PPS信號的稀疏表示方法,然后針對不同階次的PPS信號構造不同的稀疏字典。針對二階PPS信號,即LFM信號,為其構造了波形延時字典和FRFT正交基字典,并通過實驗驗證這兩種字典都能對LFM信號稀疏表示,但FRFT字典的抗白噪聲干擾更強。針對三階PPS信號,為其構造了波形匹配字典。其次研究了基于壓縮傳感檢測模型的建立以及檢測模型下壓縮檢測算法的設計與實現。首先建立了一種高斯白噪聲信道條件下的檢測模型�，F有的檢測方法有基于稀疏系數位置的檢測算法,但其在低信噪條件下檢測效果不佳,而且針對的是已知信號。于是將歸一化殘差引入到檢測算法中。并分別根據LFM信號和三階PPS信號在雷達信號中的不同應用,針對LFM信號,引入歸一化殘差檢測算法,驗證了此算法的有效性。然后將多脈沖檢測引入到LFM信號檢測中,又提出了一種多重檢測算法和一種積累檢測算法,經過仿真證明了這兩種多脈沖檢測算法相對于單脈沖的歸一化殘差檢測算法都提高了檢測性能,并且積累檢測算法在性能上更具有優(yōu)勢。針對三階PPS信號,重點研究了多分量模型下的歸一化殘差的檢測算法,并根據歸一化殘差斜率的特性提出了一種信源個數估計算法,最后驗證了此算法對信源個數估計的有效性。
[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.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN957.51

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