Accuracy Improvement of All-Pole Spectrum Estimation Using Particle Swarm Optimization and Its Comparison with Classic Methods

Spectrum estimation has many applications in digital signal processing. Parametric models for spectrum estimation include AR model, MA model, and ARMA model. Normally, classic methods such as Levinson-Durbin or Burg method are used to calculate parameters of AR model. Clearly, there is a distance (error) between spectrum estimated using these algorithms and the actual signal spectrum. In this paper, Particle Swarm Optimization (PSO) method is used to reduce this error. Results show at least 40% improvement in decreasing error of estimated all-polespectrum in comparison with classic spectral estimation methods.

Keywords:

Spectrum Estimation , Linear Prediction , Particle Swarm Optimization , Global Optimization , Autoregressive model , Levinson-Durbin method , Burg method