Matrix analysis
The Journal of Machine Learning Research
An automated acoustic systemto monitor and classify birds
EURASIP Journal on Applied Signal Processing
On the virtual array concept for the fourth-order direction findingproblem
IEEE Transactions on Signal Processing
Applications of cumulants to array processing .I. Apertureextension and array calibration
IEEE Transactions on Signal Processing
On the virtual array concept for higher order array processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Parallel factor analysis in sensor array processing
IEEE Transactions on Signal Processing
WAVES: weighted average of signal subspaces for robust widebanddirection finding
IEEE Transactions on Signal Processing
Identifiability results for blind beamforming in incoherentmultipath with small delay spread
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
TOPS: new DOA estimator for wideband signals
IEEE Transactions on Signal Processing - Part I
Quadratic optimization for simultaneous matrix diagonalization
IEEE Transactions on Signal Processing
Covariance sparsity-aware DOA estimation for nonuniform noise
Digital Signal Processing
Hi-index | 35.68 |
In real-world applications such as those for speech and audio, there are signals that are nonstationary but can be modeled as being stationary within local time frames. Such signals are generally called quasi-stationary or locally stationary signals. This paper considers the problem of direction-of-arrival (DOA) estimation of quasi-stationary signals. Specifically, in our problem formulation we assume: i) sensor array of uniform linear structure; ii) mutually uncorrelated wide-sense quasi-stationary source signals; and iii) wide-sense stationary noise process with unknown, possibly nonwhite, spatial covariance. Under the assumptions above and by judiciously examining the structures of local second-order statistics (SOSs), we develop a Khatri-Rao (KR) subspace approach that has two notable advantages. First, through an identifiability analysis, it is proven that this KR subspace approach can operate even when the number of sensors is about half of the number of sources. The idea behind is to make use of a "virtual" array structure provided inherently in the local SOS model, of which the degree of freedom is about twice of that of the physical array. Second, the KR formulation naturally provides a simple yet effective way of eliminating the unknown spatial noise covariance from the signal SOSs. Extensive simulation results are provided to demonstrate the effectiveness of the KR subspace approach under various situations.