【摘要】：非規(guī)則稀疏陣列由于陣元的稀疏布置,增大了天線陣列的孔徑,使得其方向性增強(qiáng),掃描波束變窄,空間分辨率明顯提高,同時(shí)也大大減弱了陣元間的互耦效應(yīng),而非規(guī)則性則使該陣列能實(shí)現(xiàn)無模糊測(cè)角。然而,在實(shí)際的布陣環(huán)境中存在許多因素會(huì)限制布陣的范圍,如山川、河流、沼澤等。在陣列構(gòu)型的研究中,還需要針對(duì)這些地理約束提出相應(yīng)的布陣方法。此外,對(duì)于通過處理稀疏陣列的接收信號(hào)從而估計(jì)信源方向的算法,其測(cè)向性能都是基于陣列流形精確己知的前提。但是在實(shí)際的工程應(yīng)用中,真實(shí)的陣列流形往往會(huì)隨著許多因素的變化而出現(xiàn)一定程度的偏差,這將使得測(cè)向算法的旁瓣電平升高,嚴(yán)重時(shí)甚至超過主瓣。本文對(duì)存在地理因素限制下非規(guī)則稀疏線陣的陣列構(gòu)型問題,研究了基于天線尺寸約束與地理環(huán)境約束的非規(guī)則陣列構(gòu)型方法。同時(shí)針對(duì)陣列天線中存在誤差影響旁瓣的問題,研究了陣列自校正方法,其主要工作包括以下方面:(1)針對(duì)隨機(jī)稀疏陣列構(gòu)型問題,給出了非規(guī)則稀疏陣列信號(hào)模型,并在此基礎(chǔ)上設(shè)計(jì)了稀疏陣列低旁瓣優(yōu)化布陣的最優(yōu)化模型,研究了基于模式搜索的無約束的最優(yōu)陣元配置的方法。進(jìn)一步地,針對(duì)實(shí)際布陣環(huán)境中存在天線尺寸與地理約束限制的問題,研究了基于粒子群的非規(guī)則陣列構(gòu)型方法。(2)針對(duì)實(shí)際場(chǎng)景中存在陣列誤差條件下的近場(chǎng)窄帶信號(hào)建模問題,首先,分析了陣列位置誤差、接收通道幅度誤差、接收通道相位誤差的產(chǎn)生原因;然后,推導(dǎo)了幾類誤差條件下的通用接收信號(hào)模型;最后,通過仿真實(shí)驗(yàn)說明了陣列誤差會(huì)使定位算法的旁瓣電平升高。(3)提出了針對(duì)近場(chǎng)信號(hào)源的基于迭代優(yōu)化的誤差自校正算法。該算法對(duì)信號(hào)源位置與陣列誤差進(jìn)行聯(lián)合估計(jì),在估計(jì)誤差參數(shù)的同時(shí),將近場(chǎng)源參數(shù)的二維搜索問題轉(zhuǎn)化為兩個(gè)一維搜索問題,降低了算法的計(jì)算復(fù)雜度。此外,針對(duì)誤差快/慢變化的系統(tǒng)中引起的高旁瓣問題,仿真實(shí)驗(yàn)表明了利用該二維校正算法能夠?qū)π盘?hào)源位置與誤差參數(shù)進(jìn)行聯(lián)合估計(jì),從而有效抑制旁瓣。(4)將本文提到的旁瓣抑制技術(shù)運(yùn)用到特定場(chǎng)景中進(jìn)行了仿真實(shí)驗(yàn)與試驗(yàn)驗(yàn)證,分別進(jìn)行了近場(chǎng)50米線陣兩相干目標(biāo)定位問題進(jìn)行仿真模擬分析和5.8米稀疏線陣存在陣列誤差情況下的實(shí)測(cè)數(shù)據(jù)驗(yàn)證分析。
[Abstract]:Because of the sparse arrangement of the array elements, the irregular sparse array increases the aperture of the antenna array, enhances its directivity, narrows the scanning beam, improves the spatial resolution obviously, and greatly weakens the mutual coupling effect between the array elements, while the irregularity enables the array to measure the angle without ambiguity. However, there are many factors in the actual array environment, such as mountains, rivers, swamps and so on. In the study of array configuration, it is also necessary to propose corresponding array arrangement methods for these geographical constraints. In addition, for the algorithm which estimates the direction of the source by processing the received signal of the sparse array, the direction finding performance is based on the accurate known premise of the array manifolds. However, in practical engineering applications, the real array manifolds tend to deviate to a certain extent with the change of many factors, which will increase the sidelobe level of the direction finding algorithm and even exceed the main lobe in serious cases. In this paper, an irregular array configuration method based on antenna size constraint and geographical environment constraint is studied for the array configuration problem of irregular sparse linear array with geographical constraints. At the same time, aiming at the problem of error affecting sidelobe in array antenna, the main work of array self-tuning method is as follows: (1) aiming at the problem of random sparse array configuration, the irregular sparse array signal model is given, and the optimization model of sparse array low sidelobe optimization is designed, and the unconstrained optimal array element allocation method based on pattern search is studied. Furthermore, aiming at the limitation of antenna size and geographical constraints in the actual array environment, the irregular array configuration method based on particle swarm optimization is studied. (2) aiming at the near-field narrowband signal modeling problem with array error in the actual scene, firstly, the causes of array position error, receiving channel amplitude error and receiving channel phase error are analyzed. Then, the general received signal models under several kinds of error conditions are derived. finally, the simulation results show that the array error will increase the sidelobe level of the positioning algorithm. (3) an error self-tuning algorithm based on iterative optimization for near-field signal sources is proposed. The algorithm estimates the position of the signal source and the array error jointly. While the error parameters are estimated, the two-dimensional search problem near the field source parameters is transformed into two one-dimensional search problems, which reduces the computational complexity of the algorithm. In addition, in order to solve the high sidelobe problem caused by fast / slow error change, the simulation results show that the two-dimensional correction algorithm can jointly estimate the position and error parameters of the signal source, thus effectively suppressing the sidelobe. (4) the sidelobe suppression technique mentioned in this paper is applied to a specific scene for simulation and experimental verification. The two-coherent target location problem of near-field 50m linear array is simulated and analyzed, and the measured data of 5.8m sparse linear array with array error are verified and analyzed respectively.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN911.7

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