We proposed a new model for non-stationary time series analysis based on the IAR (inhomogeneous autoregressive) model, and a method for model parameter estimation when the set of basis is given. In this paper, we further propose a method for parameter estimation including that of basis set: we set a new condition that power of the input sequence is concentrated in low-frequency domain, and developed an iterative estimation method. We firstly select an initial set of basis, from which new sets are created in order to minimize the difference between the model and data. Among new sets of basis, we select a good one that gives minimum standard deviation of estimated frequencies.

This letter proposes the method using a filter to suppress the very large noise obstructive to the radio pulsar surveys. This noise suppression filter is constructed from the average of the amplitude spectrum of pulsar signal for each channel. Using this method, the dispersion measure, one of the important parameters in the pulsar surveys, can easily be extracted.

We proposed a new model for non-stationary time series analysis based on an inhomogeneous AR (autoregressive) equation. Time series data is regarded as white noise plus output of an AR system excited by non-stationary input sequence represented in terms of a set of basis. A method of model parameter estimation was presented when the set of basis and the AR order are given. In order to extend the method, we present a method of parameter estimation when the AR order is unknown: we set two new criteria 1) minimize the root mean square error of the output sequence, and 2) minimize scattering of estimated frequencies. Then, we derive a procedure for the estimation of the AR order and the other unknown parameters.