The INS nonlinear alignment with a large azimuth misalignment angle using predictive filter is discussed in this paper. In order to deal with the problem that the gyro errors are non-observable, an improved algorithm combining predictive filters and extended Kalman filters is proposed.

The INS nonlinear alignment with a large azimuth misalignment angle using predictive filter is discussed in this paper. In order to deal with the problem that the gyro errors are non-observable, an improved algorithm combining predictive filters and extended Kalman filters is proposed.

The signal-to-noise ratio and lateral reflector continuity are both improved by the application of predictive filters whose effectiveness are aided by the repeatability of the Chirp source.

When using concepts of predictive filters, a family of parameter estimation techniques, we can obtain better estimates.

There are different types of predictive filters, each relying on different assumptions and objectives.

There are many applications of predictive filters in different fields and contexts.

There are several predictive filters, each appropriate for a different type of uncertainty representation and dynamic modeling.

In digital signal processing, stochastic control and the prediction of electric power load, etc, we arc concerned with the optimal prediction of stationary random signal so In the past time, the Wiener predictive filtering method has been used to solve the optimal prediction of stationary random signals. The solution of Wiener-Hoff equation must be determined, and this is very burdonsome. Recently, the models of stationary random signals have been proposed from the point of View of the time series analysis...

In digital signal processing, stochastic control and the prediction of electric power load, etc, we arc concerned with the optimal prediction of stationary random signal so In the past time, the Wiener predictive filtering method has been used to solve the optimal prediction of stationary random signals. The solution of Wiener-Hoff equation must be determined, and this is very burdonsome. Recently, the models of stationary random signals have been proposed from the point of View of the time series analysis by the mathe-maticians, and the formulae of their pure prediction are obtained. This method is very simple, but does not consider such a case of the filtering pre- diction with the interference of noise. Therefore, in this paper, the equivalent conversions between the autocorrelation functions and power spectra with the stationary signal models are studied; the optimal predictive formulae of the stationary signal models with the interference of white noise have been derived, Thus, we can first convert the autocorrelation functions or power spectra to the models of stationary random signals, then use the formulae of the optimal prediction of stationary random signals with the interference of white noise to solve the Wiener filtering prediction.

This paper is based on properties of lattice predictive filter, it modifies the error criterion of lattice filter using as ARMA model identification, and puts forward a lattice identification method for ARMA model based on new error criterion. This algorithm structure is simple. Computer identification test shows: this algorithm has better identification performances.

The deconvolution technique present in this paper is referred to a modification of the conventional least-square one, which normally begins with the determination of the locations where non-zero reflection coefficients lie. In practice, seismic data are categorized into two types according to which type of points, predictable or unpredictable, they are subordinate to. A predictive filter is then defined to make predicted errors minimun at the predictable points. In this way, the deconvolution process is...

The deconvolution technique present in this paper is referred to a modification of the conventional least-square one, which normally begins with the determination of the locations where non-zero reflection coefficients lie. In practice, seismic data are categorized into two types according to which type of points, predictable or unpredictable, they are subordinate to. A predictive filter is then defined to make predicted errors minimun at the predictable points. In this way, the deconvolution process is completed. This paper pioneers the so-called " Location Deconvolution" by which reflection coefficients are correctly defined once the non-zero reflection coefficient locations are given. An adaptive iterative algorithm is also presented to succcsivcly estimate the predictable and unpredictable points for seismic data so as to determine the locations where non-zero reflection coefficients lie.