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 Cramr-Rao lower bound at moderate or high signal to noise ratio.
Bibliographical noteFunding Information:
Manuscript received January 19, 2010; accepted April 28, 2010. Date of publication May 17, 2010; date of current version August 11, 2010. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Sergiy A. Vorobyov. This work was supported in part by the NWO-STW under the VICI program (Project 10382).
- Algebraic constant modulus algorithm
- blind estimation
- maximum likelihood estimation
- polynomial phase signals
- synthetic aperture radar