The Robust And High Accurate Fusion Filter For Trajectory Target Realtime Tracking

The trjactory target has a high maneuverability and wide range of dynamics as well as a complex measurement environment. This brings great challenges to the measurement and control systems. Meanwhile, the introductions of new instruments to the system not only mean an increasing coverage and a local improvable accuracy, but also bring new unceritainties of the measurement information. Therefore, robust and high accurate state estimation methods should been studied to obtain an excellent tracking performance throught the flight phases of the trajectory target.Because of the continuity of trajectories and the complementary of measuring instruments, real-time fusion filters are used to ensure a robust and high accurate trajectory target tracking in this paper. Concretely, the prior dynamic charctersitic and the measuring information are integraed by high accurate dynamic modelings and nonlinear filters based on UKF. Meanwhile, the self-calibration of systematical error and detection of gross error are also regarded in detail. In addition, methods for actual fusion system designing are discussed in detail utilizing the data processing method here. The main contributes of this paper are listed as follows:(1) Parmetric and semi-parmetric modeling for trajectory targetAfter the comparizations of the commonly used dynamic models for trajectory target, the researches are focused on the parametric and the semi-parametric modeling. For the parametric method, the adaptive sliding polynomial with equality constraint and the adaptive recursive spline are proposed. They do not need much prior information, such as the most substantial forces acting on the target or the dynamic charctersitic of the target. Therefore, the two methods are easy to implemented and have high accuracy and strongh ability of maneuver detection. For the semi-parametric method, a new recursive model with parameter selecting techniquies is developed for the real-time purpose. The new model can achieve an excellent improvement of tracking precision by introdcting non-parameter portions to identify the higher order maneuvers which cann't be expressed by the commonly used model.(2) Simplified UKF based on the strategies of simplified sampling and dual filterFirst, the cost function basing on the maximum-likelihood principle is developed. Sencond, according to the linearity and separability of the system, four simplifications of UKF are proposed utilizing the different simplified sampling strategies. Then, taking the UKF in the additive noise case as an example, the influences of the state augmentation and sigma points resample are discussed in detail. On this basis, some universal designing principles for a practical UKF are given. Finally, an improved DUKF is derived to estimate the state and the parameter simultaneously. The new filter has a random control input and sequential dual estimation structure. The utilization of the prior structure information of the system make those improved algorithms have less calculation as well as better robustness and accuracy than the original UKF.(3) Self-calibration of systematical error for trajectory target tracking A grading calibrated scheme is proposed, which is based on the impaction analysisof systematical error on the filter results. First, for the two commonly used parametric method, i.e. the fitting of systematical error by using moving windows and the EMBET, forms of recursive estimation are realized. Secondly, a self-calibration filter based on semi-parameter modeling is investigated. Finally, the detection algorithms for the existence of systematical error are proposed by theory of sparse representation theory. Those algorothms require prior information decreasingly in turn. By taking full advantage of the modeling and sparseness of systematical error, they can realize the dectection and calibration of systematical error simultaneously online.(4) Detection of gross error for trajectory target trackingAfter the analysis of the cumulative effect on filter results for gross error, the evaluation for the reliability of Kalman filter is developed referring to the reliability theory in adjustment systems. Then, an algorithm so called detection facing to the state is introduced to construct the criterion and the threshold for gross error detection. Next, by integrating the selection of quasi-accurate observation and the Kalman framework, a new filter called quasi-accurate filter is developed. The algorithm has a strong resistance for the diversion of gross error and a notable detection accuracy, which makes it needs less quasi-accurate observations and more suitable for the systems with complexity observe structures or less observations. Finally, by improving the weighted mode of the innovation, the depth-weighted Kalman filter is proposed. The new filter utilizes the correlation of the observe information obtained at the same time and can release the effect of gross error without any straight detection steps.(5) The design of the real-time fusion system for trajectory target trackingFirst, the full data processing flow for trjectory target tracking system is suggested and the realization scheme of the system is dicussed in detail. Then, a simulation platform with modularized configuration is designed, which can be applied in data validaton, test simulation and practical application. The platform has a preferable compatibility for other algorithms and is easily extended to other tracking tasks.The algorithms and the fusion systems in this paper have already been successfully used in the research on trajectory target tracking. Methods for the performance analysis and the filter designing here can also benefit researches on the state estimation of general dynamic systems and the design of other nonlinearal filters.