【摘要】：多波束成像聲納通過在接收端對大規(guī)模換能器陣列進行采樣,經(jīng)數(shù)字波束形成在一定角度范圍內(nèi)形成均勻密集分布的接收波束,從而實時獲取水下目標(biāo)的二維圖像,在海洋資源開發(fā)和水下搜救等方面應(yīng)用廣泛。但是大規(guī)模換能器陣列帶來聲納系統(tǒng)硬件復(fù)雜度的提高和成本、體積、功耗的增加。陣列稀疏化技術(shù)是解決上述問題的有效途徑之一�，F(xiàn)有的稀疏陣列設(shè)計大部分都是針對單波束情形,約束條件少。而對于成像聲納這類多波束情形下的稀疏陣列設(shè)計,尚未得到很好的解決。因此,本文對適用于多波束情形下的陣列稀疏化技術(shù)進行研究,具有重要的理論意義和應(yīng)用價值。主要研究內(nèi)容與成果如下:(1)研究了基于Farrow結(jié)構(gòu)多波束形成器的陣列稀疏方法,旨在通過得到一組稀疏的加權(quán)系數(shù)矢量來實現(xiàn)陣元位置的稀疏。采用Farrow結(jié)構(gòu)指向可調(diào)波束形成器,結(jié)合基于子孔徑旋轉(zhuǎn)的多波束形成方法,僅用一組加權(quán)系數(shù)即可實現(xiàn)90度視野范圍內(nèi)的538個波束形成;構(gòu)建了基于Farrow結(jié)構(gòu)多波束形成器的陣列稀疏的凸優(yōu)化模型,仿真和實測數(shù)據(jù)表明該多波束稀疏方法可以根據(jù)設(shè)定的門限得到滿意解,但是陣列稀疏率不高。(2)研究了基于智能優(yōu)化算法和凸優(yōu)化的多波束陣列稀疏方法。將前人提出的基于粒子群算法的單波束陣列稀疏方法拓展到多波束陣列,仿真結(jié)果表明該方法尋優(yōu)能力不佳、計算量過大;針對現(xiàn)有方法的缺陷,提出了一種基于改進的二進制風(fēng)驅(qū)動優(yōu)化和凸優(yōu)化的混合算法,同時對陣列的位置和加權(quán)矢量進行優(yōu)化,直到獲得滿足方向圖性能的最小陣元數(shù)目的稀疏陣列;仿真和實測數(shù)據(jù)表明,該算法能夠得到滿足性能要求的陣列最優(yōu)權(quán)值和陣列布局,無論是尋優(yōu)性能還是計算效率相比于現(xiàn)有方法都有了較大的提高。(3)研究了基于列空間相關(guān)的多波束陣列稀疏方法。考慮到陣元接收信號本身存在很大的相關(guān)性,研究了接收陣列導(dǎo)向矩陣的列空間相關(guān)模型;引入投影誤差,基于每一列在其他列張成的子空間上的投影誤差確定陣元的相對冗余,并設(shè)計了詳細(xì)的算法流程;仿真和實測數(shù)據(jù)表明,基于列空間相關(guān)的稀疏方法相比于前兩種方法尋優(yōu)性能有了提升,并且能在同等的主旁瓣性能下達(dá)到最優(yōu)的陣列稀疏率。綜上,本文給出的三種稀疏方法各有優(yōu)缺點�；贔arrow結(jié)構(gòu)多波束形成器的陣列稀疏方法所需加權(quán)系數(shù)少、計算量小,但是波束主瓣寬度較寬,陣列稀疏率不高;后兩種多波束陣列稀疏方法的尋優(yōu)性能都強于基于Farrow結(jié)構(gòu)多波束形成器的稀疏方法,但是計算時間有所增加。比較而言,基于列空間相關(guān)的多波束陣列稀疏方法綜合性能最優(yōu)。
[Abstract]:By sampling a large transducer array at the receiving end, the multi-beam imaging sonar can form a uniform and dense receiving beam through digital beamforming in a certain range of angles, so that the two-dimensional images of underwater targets can be obtained in real time. It is widely used in marine resources development and underwater search and rescue. However, large-scale transducer array brings about the increase of hardware complexity and cost, volume and power consumption of sonar system. Array sparsity is one of the effective ways to solve the above problems. Most of the existing sparse array designs are for single beam cases with few constraints. But the sparse array design of imaging sonar is not well solved. Therefore, it is of great theoretical significance and practical value to study the sparse array technique for multi-beam cases. The main research contents and results are as follows: (1) the array sparsity method based on Farrow structure multi-beam forming is studied in order to obtain a set of sparse weighting coefficient vectors to achieve the sparse location of the array elements. With the Farrow structure pointing adjustable beamformer and the multi-beamforming method based on sub-aperture rotation, 538 beamforming in the range of 90-degree visual field can be realized with only one set of weighting coefficients. A convex optimization model of array sparsity based on Farrow structure is constructed. The simulation and measured data show that the proposed method can obtain satisfactory solutions according to the threshold set. But the sparse rate of the array is not high. (2) A multi-beam array sparse algorithm based on intelligent optimization and convex optimization is studied. The sparse method of single beam array based on particle swarm optimization is extended to multi-beam array. The simulation results show that the algorithm has poor optimization ability and too much computation. A hybrid algorithm based on improved binary wind-driven optimization and convex optimization is proposed. The position and weighted vector of the array are optimized until the sparse array with minimum number of elements satisfying the performance of the pattern is obtained. The simulated and measured data show that the algorithm can obtain the optimal array weight and array layout to meet the performance requirements. Both optimization performance and computational efficiency have been greatly improved compared with the existing methods. (3) Multi-beam array sparse method based on column space correlation is studied. Considering that the received signals of array elements have great correlation, the column space correlation model of the receiving array guidance matrix is studied, and the projection error is introduced. Based on the projection errors of each column in the subspace of other Zhang Cheng columns, the relative redundancy of the array elements is determined, and a detailed algorithm flow is designed. Compared with the previous two methods, the sparse method based on column space can improve the performance and achieve the optimal array sparsity ratio at the same performance of the main sidelobe. In summary, the three sparse methods presented in this paper have their own advantages and disadvantages. The method of array sparsity based on Farrow structure requires less weighting coefficient and less computation, but the width of the main lobe of the beam is wider and the sparse rate of the array is not high. The performance of the latter two multi-beam array sparse methods is better than that of the multi-beam forming method based on Farrow structure, but the computation time is increased. By comparison, the multi-beam array sparse method based on column space correlation is optimal.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TB565.1

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