A polynomial phase parameter estimation-phase unwrapping algorithm
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
Identifiability and aliasing in polynomial-phase signals
IEEE Transactions on Signal Processing
Efficient estimation of a narrow-band polynomial phase signal impinging on a sensor array
IEEE Transactions on Signal Processing
An analytical constant modulus algorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing
Estimating parameters of multiple wideband polynomial-phase sourcesin sensor arrays
IEEE Transactions on Signal Processing
Direction-of-arrival estimation for constant modulus signals
IEEE Transactions on Signal Processing
Direction of arrival estimation using parametric signal models
IEEE Transactions on Signal Processing
Generalized High-Order Phase Function for Parameter Estimation of Polynomial Phase Signal
IEEE Transactions on Signal Processing - Part I
Asymptotic properties of the algebraic constant modulus algorithm
IEEE Transactions on Signal Processing
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Consider the problem of estimating the parameters of multiple polynomial phase signals observed by a sensor array. In practice, it is difficult to maintain a precisely calibrated array. The array manifold is then assumed to be unknown, and the estimation is referred to as blind estimation. To date, only an approximated maximum likelihood estimator (AMLE) was suggested for blindly estimating the polynomial coefficients of each signal. However, this estimator requires a multidimensional search over the entire coefficient space. Instead, we propose an estimation approach which is based on two steps. First, the signals are separated using a blind source separation technique, which exploits the constant modulus property of the signals. Then, the coefficients of each polynomial are estimated using a least squares method applied to the unwrapped phase of the estimated signal. This estimator does not involve any search in the coefficient spaces. The computational complexity of the proposed estimator increases linearly with respect to the polynomial order, whereas that of the AMLE increases exponentially. Simulation results show that the proposed estimator achieves the Cramér-Rao lower bound at moderate or high signal to noise ratio.