本文選題：動作識別 + 動態(tài)時間規(guī)整�。� 參考：《空軍工程大學學報(自然科學版)》2017年04期

【摘要】：針對飛行動作數(shù)據(jù)隨機性強與長度不一致的問題,提出通過減小動態(tài)時間規(guī)整(DTW)算法的搜索空間,并定義不同特征參數(shù)貢獻度的概念,實現(xiàn)對飛行數(shù)據(jù)的多元時間序列融合,從而完成對戰(zhàn)術(shù)機動動作的識別。通過引入預(yù)分類和細分類結(jié)合的方式,對動作數(shù)據(jù)進行預(yù)處理,然后根據(jù)改進的動態(tài)時間規(guī)整(WDTW)算法對待測數(shù)據(jù)進行識別。仿真實驗表明,相比傳統(tǒng)DTW算法,WDTW算法通過降低算法復(fù)雜度,識別計算時間變化明顯;對核密度與精準度系數(shù)的分析表明識別準確率亦有所提高。實驗結(jié)果驗證了所提方法的準確性。
[Abstract]:Aiming at the problem of strong randomness and inconsistent length of flight action data, this paper proposes a method to fuse flight data in multivariate time series by reducing the search space of dynamic time warping (DTW) algorithm and defining the concept of contribution degree of different characteristic parameters. In order to complete the tactical maneuvering action recognition. By introducing the combination of pre-classification and fine-classification, the action data is preprocessed, and then the measurement data is recognized according to the improved dynamic time warping (WDTW) algorithm. The simulation results show that compared with the traditional DTW algorithm, the complexity of the algorithm is reduced and the computing time is changed obviously, and the analysis of the kernel density and precision coefficient shows that the recognition accuracy is also improved. The experimental results verify the accuracy of the proposed method.
【作者單位】： 空軍工程大學航空航天工程學院;
【基金】：航空科學基金(20155196022)
【分類號】：E926.3;O211.61

【相似文獻】

相關(guān)期刊論文 前1條

1 姜鳴;王哲龍;劉曉博;趙紅宇;胡耀華;;基于BSN和CHMMs的人體日常動作識別方法研究[J];大連理工大學學報;2013年01期

相關(guān)碩士學位論文 前1條

1 孫巖;基于隱馬爾可夫模型的動作識別的研究與實現(xiàn)[D];沈陽航空航天大學;2015年

，

本文編號：1917554