一种改进的MIMO阵列子空间方位估计方法

Improved subspace bearing estimator for MIMO array

针对MUSIC(multiple signal classification)类方位估计方法在低信噪比下性能较差的问题,利用多输入多输出系统中已知的发射信号信息和阵列结构本身的特点来改进传统的MUSIC类子空间估计方法。在常规SSMUSIC(signal-subspace scaled MUSIC)方法的基础上,通过对接收信号做匹配滤波和对样本协方差阵做空间平滑来减小噪声子空间的方差,从而大大改善了该方法在低信噪比时的性能。仿真实验结果表明:在文中给定的实验条件下,相比常规SSMUSIC方法,所提方法具有更低的分辨门限和更小的均方根误差。

It is well known that the conventional bearing estimation method of MUSIC class shows a poor performance at low SNRs.In order to solve this problem,the paper proposes a modified SSMUSIC method by exploiting the waveform information of transmitted signals in multi-input multi-output（MIMO） system and the characteristic of array configuration.Compared with the conventional SSMUSIC,the proposed method achieves much better performance at low SNRs by applying matched filtering to the received data and performing a spatial smooth for the sample covariance matrix to reduce the standard deviation of the noise subspace.Simulation results show that under the experimental conditions,the proposed method has a lower resolution threshold and smaller RMSE than those of the SSMUSIC method.