海面目標(biāo)的稀疏檢測方法研究
發(fā)布時間：2018-12-16 11:50
【摘要】：海面目標(biāo)檢測因受到海雜波的影響,傳統(tǒng)目標(biāo)檢測方法易產(chǎn)生高虛警問題,如何有效抑制非平穩(wěn)的、相關(guān)性強(qiáng)的海雜波,提高海面目標(biāo)的檢測能力,一直是雷達(dá)檢測領(lǐng)域的難點(diǎn)問題。在高頻近似情況下,海面目標(biāo)(例如艦船等)的后向散射常呈現(xiàn)出多散射中心的特點(diǎn),散射中心數(shù)量一般遠(yuǎn)小于觀測區(qū)域的可分辨單元數(shù)量,基本符合壓縮感知(CS,ComPressive Sensing)處理方法對目標(biāo)散射稀疏先驗的要求。因此,本文利用壓縮感知技術(shù)重點(diǎn)開展了以下研究工作:1.在高斯噪聲背景下,研究了迭代軟閾值(IST,Iterative Soft Thresholding)算法的點(diǎn)目標(biāo)重構(gòu)能力和輸出噪聲特性。重點(diǎn)討論了基于IST的兩種固定門限檢測器,推導(dǎo)了相應(yīng)的檢測概率和虛警概率的解析表達(dá)式,仿真分析表明,欠采樣下的IST固定門限檢測器性能優(yōu)于匹配濾波最優(yōu)檢測器。另外,給出了基于IST的恒虛警(CFAR)檢測器架構(gòu)及其性能分析。2.在海雜波背景和點(diǎn)目標(biāo)檢測條件下,建立了海雜波復(fù)合K分布模型,研究了 OMP(Orthogonal Matching Pursuit)和 FOCUSS(Focal Undetermined System Solver)算法對中低海情海雜波的抑制性能,相比經(jīng)典的白化濾波方法,它們具有更好的海雜波濾除能力。針對海雜波的復(fù)雜動力學(xué)行為,初步探索了海雜波抑制的深度學(xué)習(xí)(DeeP Learning)方法,利用卷積自編碼器(CAE,Convolutional Auto-Encode)對回波譜圖中的海雜波和目標(biāo)進(jìn)行了有效分離,初步驗證了方法的可行性。3.在海雜波背景下,研究了擴(kuò)展目標(biāo)的稀疏檢測方法。擴(kuò)展目標(biāo)的多散射中心通常呈現(xiàn)區(qū)域連續(xù)分布的特點(diǎn),這里利用目標(biāo)連續(xù)區(qū)域邊界的稀疏性和非零的目標(biāo)散射點(diǎn)與周圍散射點(diǎn)之間的連續(xù)依賴關(guān)系,提出了結(jié)合總體變分(Total Variation,TV)正則化約束的SF-LASSO算法,仿真結(jié)果表明SF-LASSO算法能較準(zhǔn)確反演目標(biāo)位置和基本輪廓。
[Abstract]:Because the sea surface target detection is affected by sea clutter, the traditional target detection method is easy to produce high false alarm problem. How to effectively suppress the non-stationary and strongly correlated sea clutter and improve the detection ability of the sea surface target, It has always been a difficult problem in the field of radar detection. In the case of high frequency approximation, the backscattering of sea surface targets (such as ships, etc.) often presents the characteristics of multiple scattering centers. The number of scattering centers is generally much smaller than the number of discernible units in the observed region, which basically accords with compression sensing (CS,). A priori requirement for sparse target scattering by ComPressive Sensing) processing method. Therefore, this paper focuses on the following research work using compressed sensing technology: 1. In the background of Gao Si noise, the point target reconstruction ability and output noise characteristics of iterative soft threshold (IST,Iterative Soft Thresholding) algorithm are studied. In this paper, two kinds of fixed threshold detectors based on IST are discussed, and the analytical expressions of detection probability and false alarm probability are derived. The simulation results show that the performance of IST fixed threshold detector under under-sampling is better than that of matched filter optimal detector. In addition, the architecture and performance analysis of CFAR (CFAR) detector based on IST are given. 2. Under the condition of sea clutter background and point target detection, the composite K distribution model of sea clutter is established, and the suppression performance of OMP (Orthogonal Matching Pursuit) and FOCUSS (Focal Undetermined System Solver) algorithms to sea clutter in middle and low sea conditions is studied, compared with the classical whitening filtering method. They have better filtering capability of sea clutter. Aiming at the complex dynamic behavior of sea clutter, the depth learning (DeeP Learning) method for sea clutter suppression is preliminarily explored. The sea clutter and target in echo spectrum are effectively separated by convolution self-encoder (CAE,Convolutional Auto-Encode). The feasibility of the method is preliminarily verified. 3. In the background of sea clutter, the sparse detection method of extended targets is studied. The multi-scattering centers of extended targets usually show the characteristics of continuous regional distribution. In this paper, the sparsity of the boundary of the continuous region of the target and the continuous dependence between the non-zero scattering points and the surrounding scattering points are used. A new SF-LASSO algorithm with global variation (Total Variation,TV) regularization constraints is proposed. The simulation results show that the SF-LASSO algorithm can accurately retrieve the target position and the basic contour.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN957.51
本文編號：2382284
[Abstract]:Because the sea surface target detection is affected by sea clutter, the traditional target detection method is easy to produce high false alarm problem. How to effectively suppress the non-stationary and strongly correlated sea clutter and improve the detection ability of the sea surface target, It has always been a difficult problem in the field of radar detection. In the case of high frequency approximation, the backscattering of sea surface targets (such as ships, etc.) often presents the characteristics of multiple scattering centers. The number of scattering centers is generally much smaller than the number of discernible units in the observed region, which basically accords with compression sensing (CS,). A priori requirement for sparse target scattering by ComPressive Sensing) processing method. Therefore, this paper focuses on the following research work using compressed sensing technology: 1. In the background of Gao Si noise, the point target reconstruction ability and output noise characteristics of iterative soft threshold (IST,Iterative Soft Thresholding) algorithm are studied. In this paper, two kinds of fixed threshold detectors based on IST are discussed, and the analytical expressions of detection probability and false alarm probability are derived. The simulation results show that the performance of IST fixed threshold detector under under-sampling is better than that of matched filter optimal detector. In addition, the architecture and performance analysis of CFAR (CFAR) detector based on IST are given. 2. Under the condition of sea clutter background and point target detection, the composite K distribution model of sea clutter is established, and the suppression performance of OMP (Orthogonal Matching Pursuit) and FOCUSS (Focal Undetermined System Solver) algorithms to sea clutter in middle and low sea conditions is studied, compared with the classical whitening filtering method. They have better filtering capability of sea clutter. Aiming at the complex dynamic behavior of sea clutter, the depth learning (DeeP Learning) method for sea clutter suppression is preliminarily explored. The sea clutter and target in echo spectrum are effectively separated by convolution self-encoder (CAE,Convolutional Auto-Encode). The feasibility of the method is preliminarily verified. 3. In the background of sea clutter, the sparse detection method of extended targets is studied. The multi-scattering centers of extended targets usually show the characteristics of continuous regional distribution. In this paper, the sparsity of the boundary of the continuous region of the target and the continuous dependence between the non-zero scattering points and the surrounding scattering points are used. A new SF-LASSO algorithm with global variation (Total Variation,TV) regularization constraints is proposed. The simulation results show that the SF-LASSO algorithm can accurately retrieve the target position and the basic contour.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN957.51
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