UDM-RBM-Based DOA Estimation in Alpha Unstable Impulse Noise and Multipath Signals

The source signals' Direction of Arrival Estimation (DOAE) has been one amongst the critical issues in sensor array processing. Numerous high-resolution DOAE mechanisms have been propounded; however, in these frameworks, a general issue noted is that the additive noise is assumed to be Gaussian distributed. To overcome these issues, this paper proposes a novel Uniform Distribution Modified Restricted Boltzmann Machine (UDM-RBM)-centric DOAE model. Here, a signal model is selected initially; also, the Covariance Matrix (CM) of an array is attained. Subsequently, for all frequencies, a steering vector grounded on the Direction of Arrival (DOA) and Short Time Fourier Transform (STFT) vector formulation is computed. After that, by utilizing Average Mean Modified Gaussian Naive Byes-Feature Mapping (AMM-GNB-FM), both values are given for feature mapping 1. Conversely, as of the input signal, the noise is found; then, it is injected into the impulse noise vector function. This vector is given for feature mapping 2. Now, both feature map 1 and 2 are amalgamated; then, utilizing Chebyshev Distance-centric Ebola Optimization Algorithm (CD-EBOA), they are given to feature dimensionality reduction. After that, utilizing UDM-RBM, the reduced features are given to train the classifier for DOAE. Hence, the experiential outcomes display an efficient DOAE for the proposed mechanism when analogized with the prevailing approaches.