利用盖尔圆定理的SIMO系统信道阶数盲估计

目前盲信道估计大多采用基于二阶统计量的估计方法，然而这一类方法的性能受到信道阶数估计准确度的严重影响。在低信噪比、存在明显首尾系数的信道条件下，大多数信道阶数估计方法的性能并不理想。本文针对这种情况，分析了接收信号自协方差矩阵的秩与信道阶数的关系。由于盖尔圆理论能够有效实现矩阵秩的估计，因此我们提出一种基于盖尔圆理论的信道阶数估计方法。该方法构造了一种改进的自适应判决门限，能够在不同的信道条件下具有更强的鲁棒性。通过仿真验证，本文所提方法在低信噪比和存在首尾系数的信道条件下，能获得较高的信道阶数估计准确率。 

Channel Order Estimation Using Gerschgorin Disks in SIMO Systems

So far the majority of blind channel estimation methods mainly utilize the second-order statistics. The performances of these methods are seriously affected by the results of channel order estimation. Under the condition of low signal-to-noise ratio (SNR) and the channel with significant head-tail coefficients, the performances of most channel order estimation methods are not ideal. In this paper, we analyze the relationship between the rank of the sample covariance matrix and the channel order. As the Gerschgorin Disks theory can be used to estimate the rank effectively, we propose a method of channel order estimation based on Gerschgorin Disks. This method constructs an improved adaptive decision threshold, which makes the method more robust under various channel conditions. Simulation results show that this method performs better on channel order estimation when SNR is low and the channel has head-tail coefficients.