This paper presents a method for improving the performance of wideband direction-of-arrival (DOA) subspace estimators. The method exploits the fact that the signal subspace varies smoothly along the spectrum to improve the estimation of this subspace and, in turn, the DOA estimates that may be subsequently computed. In an initial step, it computes the sample covariance matrix at a set of frequency bins and obtains from them the corresponding signal projection matrices. It then smooths this last set by means of a least squares fitting to a low-order polynomial and, finally, yields a small set of signal projection matrices, that can then be employed by wideband DOA estimators such as Incoherent MUltiple SIgnal Classification (IC-MUSIC) and Test of Orthogonality of Projected Subspaces (TOPS). The method provides a significant improvement in the RMS error performance of these two estimators with a small increase in computational burden. Its performance is assessed in several numerical examples.

翻译：本文介绍了改进宽带方向抵达子空间测量器性能的一种方法。该方法利用信号子空间在频谱上变化顺利这一事实来改进对这个子空间的估计,并进而计算出随后可能计算的DA的估计数。在第一步,它将样本共差矩阵用一组频率箱进行计算,并从中获取相应的信号投影矩阵。然后,通过与低级多级多功能测量器相适应的最小方块来平滑这最后一组信号投影矩阵,最后产生一小套信号投影矩阵,然后由宽带的DA估计器使用,例如不连贯的单星际分布(IC-MUSIC)和预测子空间异常性测试(TOPS),该方法大大改进了这两个估算器的RMS错误性能,而计算负担略有增加,其性能在若干数字实例中得到了评估。