A flight maneuver recognition method based on multi-strategy affine canonical time warping

Abstract

Maneuver recognition for unmanned combat air vehicle (UCAV) is a necessary technique for autonomous air combat. As a spatiotemporal alignment problem of multidimensional time series, the flight maneuver recognition is solved by a novel alignment measure, multi-strategy affine canonical time warping approach (MACTW) and its derivative form, which are extensions of affine canonical time warping (ACTW). MACTW makes several contributions: (1) it proposes multi-strategy success-history based adaptive differential evolution algorithm with linear population size reduction (MLSHADE) to accelerate the search of warping path of dynamic time warping (DTW); (2) it introduces affine strategy to address offset and scaling of canonical time warping (CTW), which is a combination of DTW and canonical correlation analysis (CCA); and (3) it extends ACTW based on MLSHADE to align multidimensional time series In addition, MLSHADE is a novel optimization technique that employs weighted mutation, inferior solution search, and eigen Gaussian walk strategies to improve the optimization efficiency. The experimental results on the CEC 2018 test suite illustrate the superior benefits of MLSHADE. UCAV flight maneuver recognition system which includes segmentation, preprocessing and recognition modules is modeled. The experimental results on UCR datasets and UCAV maneuver datasets including action units and long maneuver datasets illustrate the superiority of MACTW and its derivative form compared with other state-of-the-art alignment measures.