An efficient RBF-DCNN based DOA estimation in multipath and impulse noise wireless environment,Transactions on Emerging Telecommunications Technologies

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An efficient RBF-DCNN based DOA estimation in multipath and impulse noise wireless environment
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2022-07-16 , DOI: 10.1002/ett.4606
Harikrushna Gantayat ₁ , Trilochan Panigrahi ₂ , Pradyumna Patra ₁

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The typical MultiPath (MP) propagation environment attains lots of MP signals as of the antenna array, and the Impulse Noise (IPN) accompanies these MP signals. The Directions-of-Arrivals (DOA) of an MP signal is tough to be assessed because of these IPN. Though numerous existing techniques were generated to assess the MP signal's DOA, they cannot attain a desirable output. To achieve robust and accurate DOA estimation (DOAE) in multipath and IPN wireless environment, this work proposes an efficient Radial Basis Function based Deep Convolutional Neural Network (RBF-DCNN) centered DOAE. Here, initially, the noises in the inputted signal are eradicated utilizing Improved Particle Filter (IPF). After denoising, the imperative features are extracted as of that signal. Next, the required features are selected as of that signal, which is utilized for Dimension Reduction (DR). The Modified Salp Swarm Optimization Algorithm (MSSOA) is employed for feature selection. Then, the dimension reduced signal is inputted to the RBF-DCNN, which estimated the DOA centered upon the received signal's SNR value. Numerical simulation outcomes are rendered to exemplify the proposed method's effectiveness.

中文翻译：

多径和脉冲噪声无线环境中基于 RBF-DCNN 的有效 DOA 估计

典型的多路径 (MP) 传播环境从天线阵列获得大量 MP 信号，并且这些 MP 信号伴随有脉冲噪声 (IPN)。由于这些 IPN，MP 信号的到达方向 (DOA) 很难评估。尽管产生了许多现有技术来评估 MP 信号的 DOA，但它们无法获得理想的输出。为了在多路径和 IPN 无线环境中实现鲁棒和准确的 DOA 估计 (DOAE)，本工作提出了一种有效的基于径向基函数的深度卷积神经网络 (RBF-DCNN) 中心 DOAE。在这里，最初，使用改进的粒子滤波器 (IPF) 来消除输入信号中的噪声。去噪后，从该信号中提取必要特征。接下来，根据该信号选择所需的特征，用于降维（DR）。改进的 Salp 群优化算法 (MSSOA) 用于特征选择。然后，降维信号被输入到 RBF-DCNN，它以接收信号的 SNR 值为中心估计 DOA。数值模拟结果被呈现以举例说明所提出方法的有效性。

更新日期：2022-07-19

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