Joint Smoothing, Tracking, and Forecasting Based on Continuous-Time Target Trajectory Fitting
For tracking systems dealing with smooth motion targets like aircraft or ships, this framework reduces reliance on precise statistical models and handles irregular sensor data, though it is domain-specific.
The paper proposes a continuous-time state estimation framework that unifies smoothing, tracking, and forecasting by modeling target trajectory as a function of time and performing online data fitting. The method outperforms state-of-the-art estimators in both maneuvering and non-maneuvering target scenarios.
We present a continuous time state estimation framework that unifies traditionally individual tasks of smoothing, tracking, and forecasting (STF), for a class of targets subject to smooth motion processes, e.g., the target moves 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 the goal of finding the trajectory FoT that best fits the observations in a sliding time-window. Then, the state of the target, whether the past (namely, smoothing), the current (filtering) or the near-future (forecasting), can be inferred from the FoT. Our framework releases stringent statistical modeling of the target motion in real time, and is applicable to a broad range of real world targets of significance such as passenger aircraft and ships which move on scheduled, (segmented) smooth paths but little statistical knowledge is given about their real time movement and even about the sensors. In addition, the proposed STF framework inherits the advantages of data fitting for accommodating arbitrary sensor revisit time, target maneuvering and missed detection. The proposed method is compared with state of the art estimators in scenarios of either maneuvering or non-maneuvering target.