Journal of Xidian University ›› 2021, Vol. 48 ›› Issue (2): 92-98.doi: 10.19665/j.issn1001-2400.2021.02.012

• Special Issue: Advances in Radar Technology • Previous Articles     Next Articles

Fuzzy data association algorithm assisted by historical features

HAN Zhuoxi1(),WANG Feng1(),CHEN Pei2(),LI Zhuolun3()   

  1. 1. School of Data and Target Engineering,PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,China
    2. Unit 61827 of PLA,Shanghai 200000,China
    3. School of Information System Engineering,PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,China
  • Received:2020-08-02 Revised:2020-12-30 Online:2021-04-20 Published:2021-04-28


Aiming at the problem of strong ambiguity and uncertainty in the observed data,the author proposes a fuzzy data association algorithm based on the historical features of high-resolution one-dimensional range profile.First,for the high-resolution one-dimensional range profile's attitude,amplitude,and time-shift sensitivity,feature extraction is performed to obtain low-sensitivity features.Then,the features of the track initiation are used to construct the initial feature sample database;the features of the historical moment are utilized to construct the historical feature sample database,and the feature sample database is updated in real time.The feature weight is obtained by the interval entropy weight method and the fuzzy membership of the measurement,and the target is calculated to construct a fuzzy matrix.Finally,fuzzy data association is completed based on the principle of maximum fuzzy membership.Experimental results show that,in both the maneuvering and non-maneuvering scenarios of the target,the association performance of the proposed algorithm is better than that of the fuzzy data association algorithm.And with the increase in the clutter density,the association performance of the two algorithms is gradually decreased,but the association performance of the proposed algorithm becomes better.

Key words: high resolution one-dimensional range profile, historical feature, feature extraction, feature aided, interval entropy weight method, fuzzy data association, association

CLC Number: 

  • TN953