Simple and Efficient Nonparametric Method for Estimating the Number of Signals Without Eigendecomposition

Inspired by the computational simplicity and numerical stability of QR decomposition, a nonparametric method for estimating the number of signals without eigendecomposition (MENSE) is proposed for the coherent narrowband signals impinging on a uniform linear array (ULA). By exploiting the array geometry and its shift invariance property to decorrelate the coherency of signals through subarray averaging, the number of signals is revealed in the rank of the QR upper-trapezoidal factor of the autoproduct of a combined Hankel matrix formed from the cross correlations between some sensor data. Since the infection of additive noise is defused, signal detection capability is improved. A new detection criterion is then formulated in terms of the row elements of the QR upper-triangular factor when finite array data are available, and the number of signals is determined as a value of the running index for which this ratio criterion is maximized, where the QR decomposition with column pivoting is also used to improve detection performance. The statistical analysis clarifies that the MENSE detection criterion is asymptotically consistent. Furthermore, the proposed MENSE algorithm is robust against the array uncertainties including sensor gain and phase errors and mutual coupling and against the deviations from the spatial homogeneity of noise model. The effectiveness of the MENSE is verified through numerical examples, and the simulation results show that the MENSE is superior in detecting closely spaced signals with a small number of snapshots and/or at relatively low signal-to-noise ratio (SNR)