A MLE-based blind signal separation method for time–frequency overlapped signal using neural network

Abstract

The blind signal separation (BSS) algorithm obtains each original/source signal from the observed signal collected by the receiving antenna or sensor. Objective/loss/cost function and optimization method are two key parts of BSS algorithm. Modifying the objective function and optimization from the perspective of neural network (NN) is a novel concept in BSS domain. $$L_2$$ L 2 regularization is adopted as a term of maximum likelihood estimation (MLE)-based objective function like in Liu et al. (Sensors 21(3):973, 2021); however, we modified the probability density function (PDF) term of the objective function and used the kernel density estimation method for time–frequency overlapped digital communication signal. Multiple optimizers are studied in this paper, and we figure out the right optimizer for our application scenario. A varies of comparison experiments—whose separation results will be provided in forms of correlation coefficient and performance index—are carried out, which indicate our method can converge quickly and achieve satisfactory separation results with performance index (PI) lower than 0.02 when signal-to-noise ratio (SNR) no less than 10dB. Additionally, it demonstrates performance of our method is better than that of typical separation—FastICA, especially for the lower SNR environment, and it shows that our method is not sensitive to the frequency overlap level (FOL) of the source signal, even FOL as high as $$100\%$$ 100 % ; it still can get high-precision separation results with $$\textrm{PI}<0.02$$ PI < 0.02 .

Independently Convolutional Gated Recurrent Neural Unit for Space based ADS-B Signal Separation with Single Antenna

“…With the development of deep learning in signal processing Pang et al (2022); Wang et al (2021), many methods based on deep learning have been proposed for ADS-B signal separation due to powerful capability in feature extraction. In existing methods, Complex Neural Network Yang et al (2021) and Temporal Convolutional Network Wang et al (2022); Bi and Li (2022) are used to extract valid features for single antenna-based ADS-B signal separation.…”