Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

被引:2
作者
Jung, Kwangyong [1 ]
Min, Sawon [2 ]
Kim, Jeongwoo [2 ]
Kim, Nammoon [2 ]
Kim, Euntai [1 ]
机构
[1] Yonsei Univ, Sch Elect & Elect Engn, Seoul 03722, South Korea
[2] Hanwha Syst Co Ltd, Radar Syst Ctr, Land Radar Team, Yongin 17121, South Korea
关键词
Evidence theory; Feature extraction; Radar cross-sections; Wideband; Narrowband; Missiles; Support vector machines; Basic probability assignment; Dempster-Shafer evidence theory; generalized evidence theory; generalized fuzzy number; reentry target classification; BASIC PROBABILITY ASSIGNMENT; RECOGNITION; NUMBERS; HEIGHTS;
D O I
10.1109/ACCESS.2021.3071515
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study focuses on the reentry target classification and fuses target features based on the generalized evidence theory. The features are extensively investigated, and the ballistic factor and length of the high-resolution range profile are selected. The evidence theory is advantageous for solving feature fusion, representing uncertainty, and is widely used in defense applications. However, determining the generalized basic probability assignment (GBPA) and dealing with uncertainty is a matter that requires further improvement. In this paper, we propose a new method to determine GBPA using uncertainty with time-series radar data. First, the samples of each known class are encoded as a generalized fuzzy number (GFN), and the power set comprising the frame of discernment (FOD) is calculated from the GFN and each intersection area. Subsequently, the test samples with uncertainty are encoded as triangular fuzzy numbers, reflecting the mean and standard deviation of a Kalman filter. Finally, the firing strength between the model and the input is calculated as the degree of support for the class hypothesis, which is used to determine the GBPA. The proposed algorithm is compared with the existing methods and exhibits high classification accuracy and a short classification time without leakage. In experiments with various input uncertainties, the results demonstrate that our method can effectively reflect the input uncertainty and determine the GBPA.
引用
收藏
页码:55567 / 55580
页数:14
相关论文
共 50 条
  • [21] Classification of oil spill in the Krishna-Godavari offshore using ERS-1 SAR images with a fuzzy logic approach
    Ramakrishnan, R.
    Majumdar, T. J.
    INDIAN JOURNAL OF GEO-MARINE SCIENCES, 2013, 42 (04) : 431 - 436
  • [22] Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks
    Raj, Raghu G.
    Fox, Maxine R.
    Narayanan, Ram M.
    SENSORS, 2021, 21 (15)
  • [23] An Improved Target Classification Method for 4-D Radar Sensor Using RAM
    Wan, Qingmian
    Peng, Hongli
    Su, Shixian
    Li, Hanchen
    Mao, Junfa
    IEEE SENSORS JOURNAL, 2025, 25 (01) : 1465 - 1477
  • [24] Speed Based Surface EMG Classification Using Fuzzy Logic for Prosthetic Hand Control
    Ahmad, S. A.
    Ishak, A. J.
    Ali, S. H.
    5TH KUALA LUMPUR INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING 2011 (BIOMED 2011), 2011, 35 : 121 - +
  • [25] Detection, Characterization and Classification of Short Duration Voltage Events using DWT and Fuzzy Logic
    Kamthekar, Pallavi R.
    Gautam, Pankaj V.
    Munje, Ravindra K.
    2017 INTERNATIONAL CONFERENCE ON INNOVATIVE MECHANISMS FOR INDUSTRY APPLICATIONS (ICIMIA), 2017, : 242 - 247
  • [26] A Novel Information Theoretic Approach to Gene Selection for Cancer Classification Using Microarray Data
    Naseem, Imran
    Togneri, Roberto
    Bennamoun, Mohammed
    CURRENT BIOINFORMATICS, 2015, 10 (04) : 431 - 440
  • [27] A New Approach to Vehicle Shift Quality Subjective Evaluation Based on Fuzzy Logic and Evidence Theory
    Chen, Gang
    Zhang, Weigong
    Gong, Zongyang
    Sun, Wei
    ICIEA: 2009 4TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOLS 1-6, 2009, : 2783 - 2786
  • [28] Radar target classification method with reduced aspect dependency and improved noise performance using multiple signal classification algorithm
    Secmen, M.
    Turhan-Sayan, G.
    IET RADAR SONAR AND NAVIGATION, 2009, 3 (06) : 583 - 595
  • [29] Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network
    Vasuki, Perumal
    Mohamed, S.
    Roomi, Mansoor
    JOURNAL OF APPLIED REMOTE SENSING, 2013, 7
  • [30] A novel feature extraction method for radar target classification using fusion of early-time and late-time regions
    Lee, Seung-Jae
    Choi, In-Sik
    Chae, Dae-Young
    JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS, 2017, 31 (10) : 1020 - 1033