370 research outputs found
Neural Networks for improved signal source enumeration and localization with unsteered antenna arrays
Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case of coherent signals where conventional methods fail. For covariance matrix estimation, four different network architectures are assessed and the best performing architecture achieves a 20 times improvement in performance over the sample covariance matrix. Additionally, this network can achieve comparable performance to the sample covariance matrix with 1/8-th the amount of snapshots. For direction of arrival estimation, preliminary results are provided comparing six architectures which all demonstrate high levels of accuracy and demonstrate the benefits of progressively training artificial neural networks by training on a sequence of sub- problems and extending to the network to encapsulate the entire process
Partial Relaxation Approach: An Eigenvalue-Based DOA Estimator Framework
In this paper, the partial relaxation approach is introduced and applied to
DOA estimation using spectral search. Unlike existing methods like Capon or
MUSIC which can be considered as single source approximations of multi-source
estimation criteria, the proposed approach accounts for the existence of
multiple sources. At each considered direction, the manifold structure of the
remaining interfering signals impinging on the sensor array is relaxed, which
results in closed form estimates for the interference parameters. The
conventional multidimensional optimization problem reduces, thanks to this
relaxation, to a simple spectral search. Following this principle, we propose
estimators based on the Deterministic Maximum Likelihood, Weighted Subspace
Fitting and covariance fitting methods. To calculate the pseudo-spectra
efficiently, an iterative rooting scheme based on the rational function
approximation is applied to the partial relaxation methods. Simulation results
show that the performance of the proposed estimators is superior to the
conventional methods especially in the case of low Signal-to-Noise-Ratio and
low number of snapshots, irrespectively of any specific structure of the sensor
array while maintaining a comparable computational cost as MUSIC.Comment: This work has been submitted to IEEE for possible publication.
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