Convolutional Neural Network Reference for Track-Before-Detect Applications

Author:

Mazurek Przemyslaw1ORCID

Affiliation:

1. Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, 71-126 Szczecin, Poland

Abstract

TBD (Track-Before-Detect) algorithms allow the detection and tracking of objects of which the signal is lost in the background noise. The use of convolutional neural networks (ConvNN) allows to obtain more effective algorithms than the previous, because it is possible to take into account the background as well as the spatial and temporal characteristics of the tracked object signal. The article presents solutions for taking into account the motion with variable trajectory and speed through segmental interpolation and rectification of the trajectory, which allows the effective convolutional implementation of the TBD algorithm. The boundary of object detection was determined depending on the number of pixels of the object in relation to the number of pixels of the image stack and signal strength for the simplest neural network, so it is possible to analyse and compare more complex solutions with the proposed reference.

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3