An algorithm based on back propagation neural network and particle swarm optimization is proposed to solve the direction of arrival (DOA) estimation of coherent signals received by the sensor array in colored noise environment. First, a spatial differential smoothing algorithm is adopted to eliminate colored noise and the independent signals to obtain a covariance matrix only containing the coherent sources. Then, the first line of the covariance matrix is extracted as an input characteristic parameter vector, meanwhile, the DOA of the coherent signals are taken as output. Finally, the trained back propagation neural network optimized by particle swarm algorithm is exploited to reckon the directions of coherent signals. The algorithm put forward in this paper does not require eigen-decomposition and spectral peak searching, so the computational burden is low. Theoretical analysis and simulations demonstrate that the proposed algorithm has high angular resolution and direction finding accuracy in colored noise environment.INDEX TERMS Coherent signals, back propagation neural network, colored noise, particle swarm optimization.
Mutual coupling and gain-phase errors are very common in sensor channels for array signal processing, and they have serious impacts on the performance of most algorithms, especially in practical applications. Therefore, a new approach for direction of arrival (DOA) estimation of far-field sources in mixed far-field and near-field signals in the presence of mutual coupling and gain-phase imperfections is addressed. First, the model of received data with two kinds of array errors is founded. Then matrix transformation is used for simplifying the spectrum function according to the structure of the uniform linear array (ULA). At last, DOA of far-field signals can be obtained through searching the peaks of the modified spatial spectrum. The usefulness and behavior of the presented approach are illustrated by simulated experiments.
With the improvement of living standard and the development of science and technology, Internet of Vehicle (IOV) will play an important part in industrial transportation as a main research field of Internet of Things. As a result, it is very necessary to grasp the location of vehicle. However, the traditional single global position system is easily affected by the external environment, so an accessorial locating approach based on wideband direction of arrival (DOA) estimation in intelligent transportation is proposed. First, model the array received signal on the road infrastructure. Then, by means of random forest regression (RFR) in the supervised learning, upper triangle elements of the covariance matrix of each frequency and the actual DOA are, respectively, extracted as the input features and output parameters; thus, the corresponding prediction coefficients are solved by training. After that, the trained RFR model can be used to calculate the final direction using test samples. Finally, these vehicles can be located according to the geometrical relation between the vehicle and the infrastructure. The proposed algorithm is not only suitable for uncorrelated signals but also for uncorrelated and correlated mixed signals without wideband focusing. The simulations show that compared with some sparse recovery algorithm, the prediction accuracy and resolution are effectively improved.
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