Convolutional Neural Network Based Multipath Detection Method for Static and Kinematic GPS High Precision Positioning

Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on the fact that the features of multipath characteristics in multipath contaminated data can be learned and identified by a convolutional neural network. The proposed method is validated with simulated and real GPS data and compared with existing multipath mitigation methods in position domain. The results show the proposed method can detect about 80% multipath errors (i.e., recall) in both simulated and real data. The impact of the proposed method on positioning accuracy improvement is demonstrated with two datasets, 18–30% improvement is obtained by down-weighting the detected multipath measurements. The focus of this paper is on the development and test of the proposed convolutional neural network based multipath detection algorithm.

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High-precision piston detection method for segments based on a single convolutional neural network

Seventh Symposium on Novel Photoelectronic Detection Technology and Applications ◽

10.1117/12.2587641 ◽

2021 ◽

Author(s):

Hao Wang ◽

Weirui Zhao ◽

LU Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

High Precision ◽

Detection Method

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Method of determination of the text direction on the image with the use of convolutional neural network

Informatization and communication ◽

10.34219/2078-8320-2020-11-2-96-99 ◽

2020 ◽

pp. 96-99

Author(s):

P.L. Nikolaev

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Binary Classification ◽

Synthetic Data ◽

Real Data ◽

Method Of Determination ◽

Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2357 ◽

2020 ◽

Vol 53 (2) ◽

pp. 15374-15379

Author(s):

Hu He ◽

Xiaoyong Zhang ◽

Fu Jiang ◽

Chenglong Wang ◽

Yingze Yang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Detection Method ◽

Driver Fatigue ◽

Two Stage ◽

Fatigue Detection

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Abandoned Object Detection Method Using Convolutional Neural Network

2020 International Conference on ICT for Smart Society (ICISS) ◽

10.1109/iciss50791.2020.9307547 ◽

2020 ◽

Author(s):

Saluky Saluky ◽

Suhono Harso Supangkat ◽

IGB Baskara Nugraha

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Detection Method ◽

Abandoned Object

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Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽

10.3390/e22090949 ◽

2020 ◽

Vol 22 (9) ◽

pp. 949

Author(s):

Jiangyi Wang ◽

Min Liu ◽

Xinwu Zeng ◽

Xiaoqiang Hua

Keyword(s):

Neural Network ◽

Signal Detection ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Detection Method ◽

Learning Algorithm ◽

Simulated Data ◽

Data Sets ◽

Feature Maps ◽

Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

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