Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

摘要

We present a continuous time state estimation framework that unifiestraditionally individual tasks of smoothing, tracking, and forecasting (STF),for a class of targets subject to smooth motion processes, e.g., the targetmoves with nearly constant acceleration or affected by insignificant noises.Fundamentally different from the conventional Markov transition formulation,the state process is modeled by a continuous trajectory function of time (FoT)and the STF problem is formulated as an online data fitting problem with thegoal of finding the trajectory FoT that best fits the observations in a slidingtime-window. Then, the state of the target, whether the past (namely,smoothing), the current (filtering) or the near-future (forecasting), can beinferred from the FoT. Our framework releases stringent statistical modeling ofthe target motion in real time, and is applicable to a broad range of realworld targets of significance such as passenger aircraft and ships which moveon scheduled, (segmented) smooth paths but little statistical knowledge isgiven about their real time movement and even about the sensors. In addition,the proposed STF framework inherits the advantages of data fitting foraccommodating arbitrary sensor revisit time, target maneuvering and misseddetection. The proposed method is compared with state of the art estimators inscenarios of either maneuvering or non-maneuvering target.