自适应波束形成及在多信号识别中的应用

发布时间：2018-10-10 16:26

【摘要】：随着现代电磁环境越来越复杂,空间中的多个信号的参数在时域和频域上会产生严重的交叠,当信号在时域产生交叠时,就不能利用信号的时域参数对信号进行分离;当信号在频域产生交叠时,就无法利用信号的频域参数将多个信号进行分离,所以要从如此复杂的环境中提取出特定的信号并识别出此信号是一个亟需解决的问题。本文围绕自适应波束形成(ABF)展开研究,依据它的空域自适应滤波特性来解决从多个信号中提取出特定信号的问题;并采用提取信号的指纹特征的方法,把此特定信号识别出来。本文把ABF应用到多信号识别问题中来。然而,在实际应用中由于受到信号导向矢量失配或信号协方差矩阵误差的影响,ABF算法的稳健性变差,本文针对此问题进行了深入的研究,分析算法在各种误差下的稳健性,并且针对现有的ABF算法的不足,对算法提出了改进。本文提出了两种改进的对角加载算法,基于改进的GLC对角加载算法,有效地减小了原有的GLC对角加载算法中加载因子的计算量,并且在低信噪比和小快拍的情况下,该算法性能良好;基于零陷展宽的对角加载算法,该算法把零陷展宽和对角加载结合在一起,既解决了干扰零陷过窄的问题,又解决了期望信号协方差矩阵误差和导向矢量误差存在时,算法的稳健性变差的问题。除此之外,基于协方差矩阵重建的LCMV算法被提出,该算法能用在二维天线阵中,展宽了零陷,克服了干扰信号导向矢量失配的情况,并且该算法的权矢量计算过程中,并未用到期望信号的成分,所以在期望信号导向矢量失配时,该算法也具有较好的稳健性。本文针对多个信号在复杂的环境中难以识别的问题,提出了基于ABF的多信号识别的设计方案,该方案用基于协方差重建的LCMV的ABF算法完成了对特定信号的提取,并且用脉冲包络上升沿对此特定信号进行了识别。最后,通过仿真实验验证了应用ABF在时域、频域交叠的多信号中提取出特定的信号的可行性,并且通过实测数据实验验证了基于ABF的多信号识别的设计方案的可行性。
[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.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN911.7

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