Trajectory Estimation with Multi-range-rate System Based on Sparse Representation and Spline Model Optimization  

作　　者：Liu Jiying,Zhu Jubo,Xie Meihua Department of Mathematics and Systems Science,National University of Defense Technology,Changsha 410073,China 

出　　处：《Chinese Journal of Aeronautics》2010年第1期84-90,共7页中国航空学报（英文版）

基　　金：National Natural Science Foundation of China(60604020)

摘　　要：The classic state methods for trajectory estimation in boost phase with multi-range-rate system include method of point-by-point manner and that of spline-model-based manner. Both are deficient in terms of model-approximation accuracy and systematic error determination thus resulting in the estimation errors well beyond the requirements, especially, concerning the maneuvering trajectory. This article proposes a new high-precision estimation approach based on the residual error analysis. The residual error comprises three components, i. e. systematic error, model truncation error and random error. The approach realizes self-adaptive estimation of systematic errors in measurements following the theory of sparse representation of signals to minimize the low-frequency components of residual errors. By taking median- and high-frequency components as indexes, the spline model-approximation is improved by optimizing node sequence of the spline function and the weight selection for data fusion through iteration. Simulation has validated the performances of the proposed method.The classic state methods for trajectory estimation in boost phase with multi-range-rate system include method of point-by-point manner and that of spline-model-based manner. Both are deficient in terms of model-approximation accuracy and systematic error determination thus resulting in the estimation errors well beyond the requirements, especially, concerning the maneuvering trajectory. This article proposes a new high-precision estimation approach based on the residual error analysis. The residual error comprises three components, i. e. systematic error, model truncation error and random error. The approach realizes self-adaptive estimation of systematic errors in measurements following the theory of sparse representation of signals to minimize the low-frequency components of residual errors. By taking median- and high-frequency components as indexes, the spline model-approximation is improved by optimizing node sequence of the spline function and the weight selection for data fusion through iteration. Simulation has validated the performances of the proposed method.

关 键 词：tracking radar systematic errors truncation error sparse representation splines 

分 类 号：TJ013[兵器科学与技术—兵器发射理论与技术]