【摘要】：隨著現(xiàn)代電磁環(huán)境越來(lái)越復(fù)雜,空間中的多個(gè)信號(hào)的參數(shù)在時(shí)域和頻域上會(huì)產(chǎn)生嚴(yán)重的交疊,當(dāng)信號(hào)在時(shí)域產(chǎn)生交疊時(shí),就不能利用信號(hào)的時(shí)域參數(shù)對(duì)信號(hào)進(jìn)行分離;當(dāng)信號(hào)在頻域產(chǎn)生交疊時(shí),就無(wú)法利用信號(hào)的頻域參數(shù)將多個(gè)信號(hào)進(jìn)行分離,所以要從如此復(fù)雜的環(huán)境中提取出特定的信號(hào)并識(shí)別出此信號(hào)是一個(gè)亟需解決的問(wèn)題。本文圍繞自適應(yīng)波束形成(ABF)展開(kāi)研究,依據(jù)它的空域自適應(yīng)濾波特性來(lái)解決從多個(gè)信號(hào)中提取出特定信號(hào)的問(wèn)題;并采用提取信號(hào)的指紋特征的方法,把此特定信號(hào)識(shí)別出來(lái)。本文把ABF應(yīng)用到多信號(hào)識(shí)別問(wèn)題中來(lái)。然而,在實(shí)際應(yīng)用中由于受到信號(hào)導(dǎo)向矢量失配或信號(hào)協(xié)方差矩陣誤差的影響,ABF算法的穩(wěn)健性變差,本文針對(duì)此問(wèn)題進(jìn)行了深入的研究,分析算法在各種誤差下的穩(wěn)健性,并且針對(duì)現(xiàn)有的ABF算法的不足,對(duì)算法提出了改進(jìn)。本文提出了兩種改進(jìn)的對(duì)角加載算法,基于改進(jìn)的GLC對(duì)角加載算法,有效地減小了原有的GLC對(duì)角加載算法中加載因子的計(jì)算量,并且在低信噪比和小快拍的情況下,該算法性能良好;基于零陷展寬的對(duì)角加載算法,該算法把零陷展寬和對(duì)角加載結(jié)合在一起,既解決了干擾零陷過(guò)窄的問(wèn)題,又解決了期望信號(hào)協(xié)方差矩陣誤差和導(dǎo)向矢量誤差存在時(shí),算法的穩(wěn)健性變差的問(wèn)題。除此之外,基于協(xié)方差矩陣重建的LCMV算法被提出,該算法能用在二維天線陣中,展寬了零陷,克服了干擾信號(hào)導(dǎo)向矢量失配的情況,并且該算法的權(quán)矢量計(jì)算過(guò)程中,并未用到期望信號(hào)的成分,所以在期望信號(hào)導(dǎo)向矢量失配時(shí),該算法也具有較好的穩(wěn)健性。本文針對(duì)多個(gè)信號(hào)在復(fù)雜的環(huán)境中難以識(shí)別的問(wèn)題,提出了基于ABF的多信號(hào)識(shí)別的設(shè)計(jì)方案,該方案用基于協(xié)方差重建的LCMV的ABF算法完成了對(duì)特定信號(hào)的提取,并且用脈沖包絡(luò)上升沿對(duì)此特定信號(hào)進(jìn)行了識(shí)別。最后,通過(guò)仿真實(shí)驗(yàn)驗(yàn)證了應(yīng)用ABF在時(shí)域、頻域交疊的多信號(hào)中提取出特定的信號(hào)的可行性,并且通過(guò)實(shí)測(cè)數(shù)據(jù)實(shí)驗(yàn)驗(yàn)證了基于ABF的多信號(hào)識(shí)別的設(shè)計(jì)方案的可行性。
[Abstract]:As the modern electromagnetic environment becomes more and more complex, the parameters of multiple signals in space will be overlapped seriously in the time domain and frequency domain. When the signal is overlapped in the time domain, the time domain parameters of the signal can not be used to separate the signal. When the signal overlaps in the frequency domain, it is impossible to separate multiple signals by using the frequency domain parameters of the signal, so it is an urgent problem to extract the specific signal from such a complex environment and identify the signal. This paper focuses on adaptive beamforming (ABF), according to its spatial domain adaptive filtering characteristics to solve the problem of extracting specific signals from multiple signals, and using the method of extracting the fingerprint features of the signal to identify the specific signal. In this paper, ABF is applied to the problem of multi-signal recognition. However, due to the influence of signal steering vector mismatch or signal covariance matrix error in practical application, the robustness of ABF algorithm becomes worse. In this paper, the robustness of the algorithm under various errors is analyzed. Aiming at the deficiency of the existing ABF algorithm, the improvement of the algorithm is put forward. In this paper, two improved diagonal loading algorithms are proposed. Based on the improved GLC diagonal loading algorithm, the computational complexity of the loading factor in the original GLC diagonal loading algorithm is effectively reduced, and in the case of low signal-to-noise ratio (SNR) and small shot, Based on the diagonal loading algorithm of zero trapping broadening, the algorithm combines zero trapping broadening with diagonal loading, which solves the problem of interfering zero trapping too narrow. It also solves the problem that the robustness of the algorithm becomes worse when the error of covariance matrix of expected signal and the error of guidance vector exist. In addition, the LCMV algorithm based on covariance matrix reconstruction is proposed. The algorithm can be used in two-dimensional antenna array to widen the zero trapping, overcome the mismatch of interference signal guidance vector, and in the process of weight vector calculation of the algorithm, Because the desired signal components are not used, the proposed algorithm is robust when the desired signal orientation vector mismatches. In order to solve the problem that multiple signals are difficult to recognize in complex environment, a design scheme of multi-signal recognition based on ABF is proposed in this paper. The scheme uses the ABF algorithm of LCMV based on covariance reconstruction to extract specific signals. The specific signal is identified with the rise edge of the pulse envelope. Finally, the feasibility of extracting specific signals from overlapping signals in time domain and frequency domain by using ABF is verified by simulation experiments, and the feasibility of the design scheme of multi-signal recognition based on ABF is verified by the experiment of measured data.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

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