Least squares estimation of polynomial phase signals via stochastic tree-search

Estimating the parameters for a constant amplitude, polynomial phase signal with additive Gaussian noise is considered. The difficulty in this problem is that there are many unobserved integers when a linear regression model is used for wrapped phases. Analysing the least squares target function based on the regression model, we use the differencing approach to simplify it. Thus a tree-search algorithm can be used to find the solution of the least squares problem. To reduce the computational complexity, statistical inference methods are applied. Then an attractive recursive algorithm is derived. Simulation results show that this algorithm works at a lower SNR than that for existing methods