Improved performance of recursive tracking filters using batch initialization and process noise adaptation

摘要

Performance of nonlinear recursive tracking filters may be improved using batch initialization and process noise adaptation. ^These techniques are applied to the problem of radar tracking of a nonmaneuvering target vehicle in exoatmospheric flight. ^Although this problem has received considerable attention in the estimation literature, the approach discussed in this article is new in two respects. ^The novel features include the method of covariance initialization of the recursive filter and an adaptive model for a process-noise matrix. ^A batch initialization algorithm generates a covariance matrix with non-zero correlations of position and velocity errors, and this feature accelerates the convergence of errors in the estimates of velocity. ^The process-noise matrix is based on the gravity-gradient effect, and the tuning method is adaptive because it uses the most recent state estimate and covariance matrix. ^This feature compensates for nonlinearities in the filter dynamics model, and it improves the agreement between filter covariance and actual errors in the estimates. ^(Author)