When massive numbers of wireless IoT devices are being deployed, cognitive spectrum management is critical to satisfy the explosive broadband requirements of IoT applications. Heterogeneous the target of the spatial spectrum estimation which is part of array signal processing is to achieve the evaluation of space signal parameters and source location, which result in that the spatial spectrum estimation becomes the most basic content of the array signal processing. It needs a method by which the large dimensional array still has its consistency. Therefore, this paper studies an improved large dimensional array parameterized spatial spectrum estimation method based on Pisarenko method, named G-Pisarenko method. Firstly, an improved estimator about the logarithm of the covariance matrix of a certain bilinear form is analyzed which is based on the theory of large dimension random matrix. We can find out a relatively better method, i.e., MW method. The method will become the primitive method for us to improve. Then, aimed at the relating covariance matrix in MW, we use an improved large dimensional array estimation method which can improve the logarithm of the covariance matrix estimation. Finally, we compare the improved method and the original method by simulation, and it can be seen the clear advantage of G-Pisarenko method when the sample number and observed dimensions are in the same order of magnitude.

中文翻译：

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物联网中参数空间谱估计的一种改进方法

当部署大量无线IoT设备时，认知频谱管理对于满足IoT应用爆炸性的宽带需求至关重要。作为阵列信号处理的一部分，空间频谱估计的目标是实现空间信号参数和源位置的评估，这导致空间频谱估计成为阵列信号处理的最基本内容。它需要一种使大尺寸数组仍然具有一致性的方法。因此，本文研究了一种基于Pisarenko方法的改进的大尺寸阵列参数化空间谱估计方法，即G-Pisarenko方法。首先，基于大尺寸随机矩阵理论，对某双线性形式协方差矩阵的对数进行了改进的估计。我们可以找到一个相对更好的方法，即MW方法。该方法将成为我们需要改进的原始方法。然后，针对兆瓦中的相关协方差矩阵，我们使用了一种改进的大维数组估计方法，该方法可以提高协方差矩阵估计的对数。最后，通过仿真比较了改进方法和原始方法，可以看出，当样本数量和观测维数在同一数量级时，G-Pisarenko方法具有明显的优势。然后，针对兆瓦中的相关协方差矩阵，我们使用了一种改进的大维数组估计方法，该方法可以提高协方差矩阵估计的对数。最后，通过仿真比较了改进方法和原始方法，可以看出，当样本数量和观测维数在同一数量级时，G-Pisarenko方法具有明显的优势。然后，针对兆瓦中的相关协方差矩阵，我们使用了一种改进的大维数组估计方法，该方法可以提高协方差矩阵估计的对数。最后，通过仿真比较了改进方法和原始方法，可以看出，当样本数量和观测维数在同一数量级时，G-Pisarenko方法具有明显的优势。