Research On Sequential Covariance Steady-State Filtering Of Time-Delay Systems

In recent years,with the rapid development of technology in the military,industrial,and intelligent systems,single-sensor observing systems have been far from meeting the needs of target tracking or state estimation,and thus the research on multi-sensor information fusion has been widespreade concerned.In multi-sensor systems,the system has a time delay due to the aging of sensors and other various components and the inevitable factors of the system itself.In the control system,the time delay will deteriorate the system performance,so that the observed information can not be delivered in time,which will damage the stability of the system and have a considerable adverse impact on the control ability of the system.In addition,due to the existence of time delay,when the observation data of multi-sensors is fused,the calculation of the cross-covariance between local sensors is especially complicated,and even some cross-covariances can not be obtained.In order to reduce the computational burden of cross-covariance between local sensors and simplify the complex multi-sensor fusion algorithm,the local state estimation and the corresponding error variance matrix be calculated by the SCI fusion estimation algorithm.Since the SCI fusion estimation algorithm avoids the calculation of the cross-covariance,the accuracy of the fusion result is reduced,but the fusion result of the SCI is conservative and the computational burden is significantly reduced.Based on the projective theorem and linear minimum variance theory,this paper deduces the multi-sensor time-delay system and uses the SCI fusion algorithm to estimate the system state.The main research contents are as follows:Firstly,for the observation delay multi-sensor time-delay system with uncorrelated white noise and statement ? observation delay muti-senor time-delay system with uncorrelated noise,based on classical Kalman Filter algorithm and then applied SICI fusion estimation algorithm.The fusion results obtained compared with the fusion results of SCI and the results of the matrix-weighted fusion.It is concluded that the fusion results of SICI fusion algorithm are consistent and conservative.Secondly,the steady state suboptimal Kalman filtering is applied to multi-delaytime systems with correlated noise.The muti-time delay system with correlated noise will transformed into a muti-time delay system with uncorrelated white noise by de-correlation method and then the system is expanded and augmented.Applaying the SCI fusion estimation algorithm and SICI fusion estimation algorithm the fusion result compared with the matrix-weighted optimal fusion result.Both of the fusion methods fusion accuracy is similar to the matrix weighting method,but the computational burden greatly reduced.Besides,the consistent fusion result obtained.Finally,through several simulation researches and data analysis,it is proved that the SCI and SICI fusion estimation algorithm is extremely convenient in multi-sensor time-delay systems and the fusion results are conservative and consistent.