scholarly journals A time domain random flicker band-limited Gaussian noise jamming algorithm for LMS-based GPS

2021 ◽  
Vol 336 ◽  
pp. 07010
Author(s):  
Tao Wu ◽  
Jixiang Wu ◽  
Hui Wang ◽  
Ligang Cui ◽  
Yinsong Yang
Keyword(s):  
Time Domain ◽  
Gaussian Noise ◽  
Mean Square ◽  
Least Mean Square ◽  
Performance Gain ◽  
Gps Receiver ◽  
Gps Receivers ◽  
Average Performance ◽  
Band Limited

Global position systems (GPS) receiver based on least mean square (LMS) could resist the interference by null jamming direction. Considering that the existing band-limited Gaussian noise jamming signals were easily suppressed by LMS-based GPS receivers, a time domain random flicker band-limited Gaussian noise jamming algorithm was proposed to improve its performance. By disturbing the convergence of LMS, it could achieve the purpose of suppressing the LMS-based GPS. Simulation result shows that the proposed algorithm has an average performance gain of 2.7dB~4.6dB under different number of interferences compared with band-limited Gaussian noise.

scholarly journals An evolutionary band-limited Gaussian noise jamming algorithm for LMS-based GPS

2021 ◽  
Vol 336 ◽  
pp. 07009
Author(s):  
Tao Wu ◽  
Qiubai Zou ◽  
Jixiang Wu ◽  
Wenqiang Li ◽  
Zhenze Jia
Keyword(s):  
Evolutionary Algorithm ◽  
Gaussian Noise ◽  
Adaptive Algorithm ◽  
Gps Receiver ◽  
Noise Interference ◽  
Simulation Results ◽  
Band Limited ◽  
Equal Power ◽  
Jamming Effect

In this paper, evolutionary algorithm is used to optimize the flicker function of band-limited Gaussian noise interference, which disturbs the convergence of adaptive algorithm based on LMS in GPS receiver, so that the average null depth of GPS receiver under equal power is lower, and better jamming effect is achieved. The simulation results show that the jamming algorithm in this paper has better jamming effect than the band-limited Gaussian noise jamming in different jamming quantity.

Automated System for Crops Recognition and Classification

2018 ◽  
pp. 1208-1223 ◽  
Author(s):  
Alaa M. AlShahrani ◽  
Manal A. Al-Abadi ◽  
Areej S. Al-Malki ◽  
Amira S. Ashour ◽  
Nilanjan Dey
Keyword(s):  
Gaussian Noise ◽  
Critical Issue ◽  
The Other ◽  
Automated System ◽  
Mean Square ◽  
Least Mean Square ◽  
Classification Rate ◽  
Bag Of Features ◽  
Profit Optimization ◽  
Imagej Software

Marketing profit optimization and preventing the crops' infections are a critical issue. This requires crops recognition and classification based on their characteristics and different features. The current work proposed a recognition/classification system that applied to differentiate between fresh (healthy) from rotten crops as well as to identify each crop from the other based on their common feature vectors. Consequently, image processing is employed to perform the statistical measurements of each crop. ImageJ software was employed to analyze the desired crops to extract their features. These extracted features are used for further crops recognition and classification using the Least Mean Square Error (LMSE) algorithm in Matlab. Another classification method based on Bag of Features (BoF) technique is employed to classify crops into classes, namely healthy and rotten. The experimental results are applied of databases for orange, mango, tomato and potatoes. The achieved recognition (classification) rate by using the LMSE for all datasets (healthy and rotten) has 100%. However, after adding 10%, 20%, and 30% Gaussian noise, the obtained the average recognition rates were 85%, 70%, and 25%; respectively. Moreover, the classification (healthy and rotten) using BoF achieved accuracies of 100%, 88%, 94%, and 75% for potatoes, mango, orange, and tomato; respectively. Furthermore, the classification for all the healthy datasets achieved accuracy of 88%.

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