摘要
基阵的角度分辨力受到基阵孔径的限制，俗称“瑞利限”。本文主要研究虚拟阵列扩展技术，构造虚拟阵元位置处的信号或信息，扩展阵列孔径，提高角度分辨力。
本文首先简要介绍了常规波束形成的原理和矢量信号处理的基础，并利用常规波束形成对虚拟后的阵元信号进行处理和性能比较。然后研究了三种虚拟阵列扩展方法:四阶累积量法、外推法、内插法。四阶累积量虚拟阵列扩展法能够抑制高斯噪声，将基阵扩展到二倍孔径。论文的工作将该方法推广到矢量阵信号处理上。常规外推法在信噪比较低时性能较差，改进方法利用Bartlett空间谱作为先验知识，使其适合于水声阵列信号处理。常规的内插法主要应用于阵型变换，不适用于线阵的孔径扩展，论文将内插思想与自回归AR模型相结合，使得内插法适用于线阵孔径扩展。本论文的主要内容是对于上述三种方法都进行理论建模、实验仿真，比较它们间的优劣势。
仿真实验结果表明:四阶累积量法效果好，但计算量大，不能够恢复时域信息;对于两个目标靠得太近时，外推法效果不好，外推性能受到信源个数影响;内插法需要知道信源的个数，扩展能力也受到信源个数影响。最后，利用三种算法分别对实验数据进行处理，验证算法的性能。处理结果表明了三种方法处理效果都较常规波束形成有很大提高。
Angular resolution is limited by the aperture of array, which is called Rayleigh limit. The purpose of this thesis researched the algorithms which can extend array aperture, with which the received signal in the place out of the aperture can be deduced by the real received signals. In other words, it is equal to extend the array aperture, and enhance the angular resolution.
The principles of beamforming and the foundation of vector array processing is introduced briefly, which is used as post processing when the virtual receive signal is constructed. Secondly, three methods to extending array aperture are researched, which are four-order cumulants, signal extrapolation and array interpolation. The use of cumulants in narrowband signal array processing is interpreted, which can eliminate Gauss noise, and extend aperture two times longer than that of original array. The application of cumulants to vector hydrophone array is proposed in this thesis. Signal extrapolation modified is used in underwater array processing in this thesis, which used Bartlett spatial spectrum as a priori knowledge. Normally, array interpolation is unfit for extending array aperture, and it is just used for virtual array transform. It is shown that a new array interpolation method whose combine with autoregressive models in this thesis can extend array aperture, which can extend array aperture. This thesis has made modeling theoretically, simulating experiment, and compared performance for each algorithm.
Simulation results verify that effect based on four-order cumulants is best, whereas it need more computation than others. Signal extrapolation could not distinguish two targets when the targets are close to each other, and the precise of signal deduced depend on the number of targets. Array interpolation method also needs information of the number of targets. In the last,the experimental data obtained from lake experimentation was disposed. It is proved that the effect processed with each of three algorithms had much improvement than beamforming did.
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