目標追跡から目標軌道へ：正則化多項式軌道最適化
From Target Tracking to Targeting Track -- Part II: Regularized Polynomial Trajectory Optimization
目標の軌道を多項式で表現し、次数制限やL0ノルム正則化を用いて測定値に最も適合する軌道を推定する手法を提案した。
著者: Tiancheng Li, Yan Song, Guchong Li, Hao Li
分類: eess.SY, cs.RO, cs.SY, eess.SP
原文アブストラクト
Target tracking entails the estimation of the evolution of the target state over time, namely the target trajectory. Different from the classical state space model, our series of studies, including this paper, model the collection of the target state as a stochastic process (SP) that is further decomposed into a deterministic part which represents the trend of the trajectory and a residual SP representing the residual fitting error. Subsequently, the tracking problem is formulated as a learning problem regarding the trajectory SP for which a key part is to estimate a trajectory FoT (T-FoT) best fitting the measurements in time series. For this purpose, we consider the polynomial T-FoT and address the regularized polynomial T-FoT optimization employing two distinct regularization strategies seeking trade-off between the accuracy and simplicity. One limits the order of the polynomial and then the best choice is determined by grid searching in a narrow, bounded range while the other adopts $\ell_0$ norm regularization for which the hybrid Newton solver is employed. Simulation results obtained in both single and multiple maneuvering target scenarios demonstrate the effectiveness of our approaches.
