Parameter estimation of phase-modulated signals using Bayesian unwrapping
IEEE Transactions on Signal Processing
Identifiability and aliasing in polynomial-phase signals
IEEE Transactions on Signal Processing
A new parameter estimation method of linear frequency modulation signal
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Polynomial-phase estimation, phase unwrapping and the nearest lattice point problem
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Fast communication: On the Cramér-Rao bound for polynomial phase signals
Signal Processing
Hi-index | 35.69 |
A novel method for signal parameter estimation is presented, termed the nonlinear instantaneous least squares (NILS) estimator. The basic idea is to use the observations in a sliding window to compute an instantaneous (short-term) estimate of the amplitude used in the separated nonlinear least squares (NLLS) criterion. The effect is a significant improvement of the numerical properties in the criterion function, which becomes well-suited for a signal parameter search. For small-sized sliding windows, the global minimum in the NLIS criterion function is wide and becomes easy to find. For maximum size windows, the NILS is equivalent to the NLLS estimator, which implies statistical efficiency for Gaussian noise. A “blind” signal parameter search algorithm that does not use any a priori information is proposed. The NILS estimator can be interpreted as a signal-subspace projection-based algorithm. Moreover, the NILS estimator can be interpreted as an estimator based on the prediction error of a (structured) linear predictor. Hereby, a link is established between NLLS, signal-subspace fitting, and linear prediction-based estimation approaches. The NILS approach is primarily applicable to deterministic signal models. Specifically, polynomial-phase signals are studied, and the NILS approach is evaluated and compared with other approaches. Simulations show that the signal-to-noise ratio (SNR) threshold is significantly lower than that of the other methods, and it is confirmed that the estimates are statistically efficient. Just as the NLLS approach, the NILS estimator can be applied to nonuniformly sampled data