A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akai...A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akaike's AIC criterion.Some numerical results of gyro drift models are obtained for analysis of gyro system. As the trend and irregular components of the observed time series can be modeled simultaneously, it is statistically more accurate and efficient than that modeled separately.展开更多
In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the t...In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.展开更多
In this paper a method for modelling and forecasting of a class of nonstationary time series with Kalman filter using moving window is proposed. The procedure of the method is as follows: in terms of parameter estimat...In this paper a method for modelling and forecasting of a class of nonstationary time series with Kalman filter using moving window is proposed. The procedure of the method is as follows: in terms of parameter estimation during recursive process by using LSM, the state space equation is constructed, then the Kalman filter using moving window is made to get the data with reduced level of observation noise. Finally, the precise parameter estimation can be obtained by using the LSM again. The algorithm is carried on recursively. Good results for estimating and forecasting are shown by simulation, examples. The algorithm of Kalman filter using moving window proposed by us is introduced in this paper, which can guarantee the precision and convergence of Kalman filter.展开更多
The modeling and simulation of sea clutter are important in detecting radar targets in sea backgrounds.Because the nonstationary property of sea clutter is ignored in traditional statistical models,a new method based ...The modeling and simulation of sea clutter are important in detecting radar targets in sea backgrounds.Because the nonstationary property of sea clutter is ignored in traditional statistical models,a new method based on measured sea clutter is proposed in this paper.First,we convert the measured sea clutter data under different sea conditions[xI,xQ]into real amplitude SM.Instantaneous phaseθis then extracted from the coherent radar’s baseband data.Second,we select a candidate statistic model and estimate its parameters based on SM by utilizing maximum likelihood estimation.Finally,we generate random series A using corresponding random data generator and then add instantaneous phaseθinto A,i.e.,y=Ae^(jθ),to obtain simulated sea clutter series.Through a comparison of simulated sea clutter and measured sea clutter data via histogram,the validity of the proposed method is proved.展开更多
文摘A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akaike's AIC criterion.Some numerical results of gyro drift models are obtained for analysis of gyro system. As the trend and irregular components of the observed time series can be modeled simultaneously, it is statistically more accurate and efficient than that modeled separately.
基金supported by the Doctoral Research Fund of the Ministry of Education, China (Grant No.20040285008)Grant-in-Aid for Scientific Research (B), the Ministry of Education, Science, Sports andCulture, Japan, 2005 (Grant No. 17300228)
文摘In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.
文摘In this paper a method for modelling and forecasting of a class of nonstationary time series with Kalman filter using moving window is proposed. The procedure of the method is as follows: in terms of parameter estimation during recursive process by using LSM, the state space equation is constructed, then the Kalman filter using moving window is made to get the data with reduced level of observation noise. Finally, the precise parameter estimation can be obtained by using the LSM again. The algorithm is carried on recursively. Good results for estimating and forecasting are shown by simulation, examples. The algorithm of Kalman filter using moving window proposed by us is introduced in this paper, which can guarantee the precision and convergence of Kalman filter.
文摘The modeling and simulation of sea clutter are important in detecting radar targets in sea backgrounds.Because the nonstationary property of sea clutter is ignored in traditional statistical models,a new method based on measured sea clutter is proposed in this paper.First,we convert the measured sea clutter data under different sea conditions[xI,xQ]into real amplitude SM.Instantaneous phaseθis then extracted from the coherent radar’s baseband data.Second,we select a candidate statistic model and estimate its parameters based on SM by utilizing maximum likelihood estimation.Finally,we generate random series A using corresponding random data generator and then add instantaneous phaseθinto A,i.e.,y=Ae^(jθ),to obtain simulated sea clutter series.Through a comparison of simulated sea clutter and measured sea clutter data via histogram,the validity of the proposed method is proved.
