Adaptive estimation algorithm of boost-phase trajectory using binary asynchronous observation

Abstract

A kind of adaptive filter algorithm based on the estimation of the unknown input is proposed for studying the adaptive adjustment of process noise variance of boost phase trajectory. Polynomial model is used as the motion model of the boost trajectory, truncation error is regarded as an equivalent to the process noise and the unknown input and process noise variance matrix is constructed from the estimation value of unknown input according to the quantitative relationship among the unknown input, the state estimation error, and optimal process noise variance. The simulation results show that in the absence of prior information, the unknown input is estimated effectively in terms of magnitude, a positive definite matrix of process noise covariance which is close to the optimal value is constructed real-timely, and the state estimation error approximates the error lower bound of the optimal estimation. The estimation accuracy of the proposed algorithm is similar to that of the current statistical model algorithm using accurate prior information.

Multiple Model Ballistic Missile Tracking With State-Dependent Transitions and Gaussian Particle Filtering

“…Based on the profile-based modelling of the boost phase and the line-of-sight measurements, the ML estimation method is applied for constructing and solving an optimisation function for estimating relevant parameters. A kind of adaptive filter algorithm is proposed in [6] for the boostphase trajectory estimation. Polynomial model is used as the motion model of the boost trajectory and the corresponding process noise variance is constructed to make sure the state estimation error approximates the error lower bound of the optimal estimation.…”