Efficient DOA estimation of impinging stochastic EM signal using neural networks

An Artificial Neural Network Model for Eficient Estimation of the Number of Mobile Stochastic EM Sources in a Space Sector

“…To generate a set for training a neural model, and a set of samples to test it, the same model of stochastic radiation source in the remote zone is applied, as in the papers [10][11][12][13][14][15][16][17]. The model of stochastic radiation sources in the distant zone represents radiation of a uniform linear antenna array of N elements located at the distance d (Figure 1).…”

“…r i,m the distance between the i-th array element (1≤i≤N) and m-th sampling points in a distant zone (1≤m≤M), where M is the number of sampling points. sampling points and elements of antenna arrays that represent sources of radiation [10][11][12][13][14][15][16][17] If the corresponding angles in the azimuthal and elevation level of M sampling points (y 1 y 2 , ..., y M ) in far zone represent with (θ 1 ,φ 1 ), (θ 1 ,φ 1 ), ... , (θ M ,φ M ) respectivly, then the correlation matrix of signals at these points can be determined in the following manner: [8] In the case of multiple sources where S is the number of sources, matrix M has the following layout:…”

“…For training the neural network which the model contains, it is enough to take only the first level of matrix C E ([C E11 , C E12 , … , CE 1M ]) because it turned out that the first type contains sufficient information to determine the angular position of the radiation source [9,10].…”

“…However, because of its complex matrix calculation requires powerful hardware resources, they are not suitable for real-time operations. A good alternative to the superresolution algorithms, the application of artificial neural networks [5][6][7] to solve problems DoA where neural models that avoid complex matrix calculations can have an approximate accuracy of the MUSIC algorithm without having to be significantly faster than the MUSIC algorithm which makes them more suitable choice for deployment in real time [8][9][10][11][12][13][14][15][16][17].…”

Enhanced Iris Recognition based on Image Match and Hamming Distance

“…The rapid development of global economy, science and technology makes the modern society of increasingly high demand to the security [1]. The traditional identification methods cannot meet the need of information security, so people proposed biometric identification technology by using biometric identification, such as fingerprint, iris and voice.…”