A low complexity blind estimator of narrowband polynomial phase signals

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 thesignals.Then, thecoefficientsof eachpolynomial 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 estimato...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Alon Amar, Amir Leshem, Alle-Jan van der Veen
• Keywords: Artificial Intelligence, Coefficient Spaces, Estimator, Maximum Likelihood Estimator, TSP 2010