Direction-of-arrival estimation method based on deep learning
A direction-of-arrival estimation and deep learning technology, applied in the field of signal processing, can solve problems such as unreliable performance, unrealistic assumptions of signal and noise models, etc., and achieve good suppression effect and robust direction-of-arrival estimation function
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[0018] Take the phased array antenna with 16 elements as an example. The range of possible incoming wave directions (take -80° to 80°) uniformly takes 33 directions, namely -80°, -75°, -70°, ..., 80°, and the deep neural network has a total of 33 output units , Each output unit corresponds to a direction, used to estimate the probability that the direction of arrival of the desired signal is that direction. Take the cross entropy between the estimated probability and the true direction of arrival as the loss function, and train the deep neural network with a large amount of collected data. The antenna array receives a signal with a length of 100, and arranges the signal of each element at the same time as a component in a spatial order into a vector to obtain a signal vector sequence with a length of 100. Each vector in the sequence is a 16-dimensional vector. Take the analytic signal for each signal vector, and obtain a sequence of analytic signal vectors with...
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