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平滑·滤波·跟踪·预报的统一框架

已有 6979 次阅读 2017-8-8 19:57 |个人分类:科技动态|系统分类:论文交流| 预报, 代码, 滤波器, 平滑器, 跟踪器

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

Tiancheng Li, Huimin Chen, Shudong Sun, Juan M Corchado


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, conditioned on a priori model information if any . 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.
Comments:16 pages, 8 figures, 5 tables, 80 references; Codes available
Subjects:Applications (stat.AP); Systems and Control (cs.SY)
Cite as:arXiv:1708.02196 [stat.AP]


The paper: https://arxiv.org/abs/1708.02196

The code: https://sites.google.com/site/tianchengli85/matlab-codes/fot4stf




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