In this paper, the problem of estimating the direction of arrival of signals of which some may be perfectly correlated is considered. This method can be applied in the situation that the non-Gaussian independent and coherent signals coexist with unknown Gaussian noise. In this method at first via mappings, the virtual uniform linear array (ULA) and also the shifted versions of this virtual ULA by assuming that all the DOAs are located in one section are constructed. In order to avoid coloring the noise because of these mappings we use a cumulant matrix instead of a covariance ones. In this method since we construct all the subarrays virtually for detection of coherent signals we do not need the array with regular configuration. The advantag...
The problem of estimating direction of arrivals in the presence of spatially colored background nois...
A new spectral direction of arrival (DOA) estimation algorithm is proposed that can rapidly estimate...
In this paper, we propose a new computationally efficient subspace-based method without eigendecompo...
Sensor arrays play important roles in signal transmission/reception, estimation, and tracking, and h...
This paper considers the problem of direction-of-arrival (DOA) estimation of coherent signals on pas...
A direction of arrival (DOA) estimation algorithm in the presence of an unknown mutual coupling is p...
Direction of arrival (DOA) estimation methods based on arbitrary even order (2q, q ≥ 2) cumulants of...
This paper proposes two subspace-based methods for direction-of-arrival (DOA) estimation of coherent...
In the past few years there have been attempts to develop subspace methods for DoA (direction of arr...
AbstractIn this paper, a robust algorithm based on spatial differencing matrix is proposed for sourc...
In the past few years there have been attempts to develop subspace methods for DoA (direction of arr...
This paper proposes a multi-invariance ESPRIT-based method for estimation of 2D direction (MIMED) of...
A novel direction-of-arrival (DOA) estimation method is proposed based on the sparse cumulants fitti...
Recently, direction-of-arrival estimation (DOA) algorithms based on arbitrary even-order (2q) cumula...
Recently, direction-of-arrival estimation (DOA) algorithms based on arbitrary even-order (2q) cumula...
The problem of estimating direction of arrivals in the presence of spatially colored background nois...
A new spectral direction of arrival (DOA) estimation algorithm is proposed that can rapidly estimate...
In this paper, we propose a new computationally efficient subspace-based method without eigendecompo...
Sensor arrays play important roles in signal transmission/reception, estimation, and tracking, and h...
This paper considers the problem of direction-of-arrival (DOA) estimation of coherent signals on pas...
A direction of arrival (DOA) estimation algorithm in the presence of an unknown mutual coupling is p...
Direction of arrival (DOA) estimation methods based on arbitrary even order (2q, q ≥ 2) cumulants of...
This paper proposes two subspace-based methods for direction-of-arrival (DOA) estimation of coherent...
In the past few years there have been attempts to develop subspace methods for DoA (direction of arr...
AbstractIn this paper, a robust algorithm based on spatial differencing matrix is proposed for sourc...
In the past few years there have been attempts to develop subspace methods for DoA (direction of arr...
This paper proposes a multi-invariance ESPRIT-based method for estimation of 2D direction (MIMED) of...
A novel direction-of-arrival (DOA) estimation method is proposed based on the sparse cumulants fitti...
Recently, direction-of-arrival estimation (DOA) algorithms based on arbitrary even-order (2q) cumula...
Recently, direction-of-arrival estimation (DOA) algorithms based on arbitrary even-order (2q) cumula...
The problem of estimating direction of arrivals in the presence of spatially colored background nois...
A new spectral direction of arrival (DOA) estimation algorithm is proposed that can rapidly estimate...
In this paper, we propose a new computationally efficient subspace-based method without eigendecompo...