Design of robust differential microphone arrays with the Jacobi–Anger expansion

Due to their small size, differential microphone arrays (DMAs) are very attractive. Moreover, they have been effective in combating noise and reverberation. Recently, a new class of DMAs of different orders have been developed with the MacLaurin’s series and the frequency-independent patterns. However, the MacLaurin’s series does not approximate well the exponential function, which appears in the general definition of the beampattern, when the intersensor spacing is not small enough. To circumvent this problem, we propose in this paper to approximate the exponential function with the Jacobi–Anger expansion. Based on this approximation and the frequency-independent Chebyshev patterns, we derive first-, second-, and third-order DMAs. Furthermore, in order to improve the robustness of DMAs against white noise amplification, we propose to use more microphones combined with minimum-norm filters. It is also shown that the Jacobi–Anger expansion is optimal from a mean-squared error perspective. Simulations are carried out to evaluate the performance of the proposed DMAs.     

一种基于模式搜索算法的基阵波束优化方法

波束形成在声纳/雷达信号处理中占有重要的地位,而设计出满足要求的波束形状是声纳/雷达信号空间处理的重要内容。自适应波束形成算法理论上可以获得最优的工作性能,但其计算复杂度高、宽容性差等缺点严重地限制了这些算法的实际应用。本文采用基于模式搜索的波束优化算法,获得了与实际接收数据无关的、类似于窗函数的幅度权系数。与加窗波束形成算法相比,这种方法可以在保证主瓣宽度不展宽的情况下,有效降低旁瓣级,并且,这种方法对基阵的几何形状没有任何限制。