Blog
About

0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Neural Network with Convolutional Module and Residual Structure for Radar Target Recognition Based on High-Resolution Range Profile

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In the conventional neural network, deep depth is required to achieve high accuracy of recognition. Additionally, the problem of saturation may be caused, wherein the recognition accuracy is down-regulated with the increase in the number of network layers. To tackle the mentioned problem, a neural network model is proposed incorporating a micro convolutional module and residual structure. Such a model exhibits few hyper-parameters, and can extended flexibly. In the meantime, to further enhance the separability of features, a novel loss function is proposed, integrating boundary constraints and center clustering. According to the experimental results with a simulated dataset of HRRP signals obtained from thirteen 3D CAD object models, the presented model is capable of achieving higher recognition accuracy and robustness than other common network structures.

          Related collections

          Most cited references 41

          • Record: found
          • Abstract: not found
          • Conference Proceedings: not found

          Deep residual learning for image recognition

           K HE,  X. Zhang,  S Ren (2021)
            Bookmark
            • Record: found
            • Abstract: not found
            • Conference Proceedings: not found

            Very deep convolutional networks for large-scale image recognition

              Bookmark
              • Record: found
              • Abstract: not found
              • Conference Proceedings: not found

              Delving deep into rectifiers: surpassing human-level performance on ImageNet classification

               K HE,  X. ZHANG,  S Ren (2021)
                Bookmark

                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                21 January 2020
                February 2020
                : 20
                : 3
                Affiliations
                Coast Defense College, Naval Aviation University, Yantai 264001, China; liss1965@ 123456163.com (S.L.); lixiangping401@ 123456126.com (X.L.); lovelin19841204@ 123456163.com (B.D.); wang17616244926@ 123456163.com (X.W.)
                Author notes
                [* ]Correspondence: fuzq2413@ 123456163.com ; Tel.: +86-186-6008-1572
                Article
                sensors-20-00586
                10.3390/s20030586
                7038176
                31973114
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                Categories
                Article

                Biomedical engineering

                loss function, neural network, target recognition, hrrp, residual structure

                Comments

                Comment on this article