975 research outputs found
FRIDA: FRI-Based DOA Estimation for Arbitrary Array Layouts
In this paper we present FRIDA---an algorithm for estimating directions of
arrival of multiple wideband sound sources. FRIDA combines multi-band
information coherently and achieves state-of-the-art resolution at extremely
low signal-to-noise ratios. It works for arbitrary array layouts, but unlike
the various steered response power and subspace methods, it does not require a
grid search. FRIDA leverages recent advances in sampling signals with a finite
rate of innovation. It is based on the insight that for any array layout, the
entries of the spatial covariance matrix can be linearly transformed into a
uniformly sampled sum of sinusoids.Comment: Submitted to ICASSP201
Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays
Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an
essential task in sonar, radar, acoustics, biomedical and multimedia
applications. Many state of the art wide-band DOA estimators coherently process
frequency binned array outputs by approximate Maximum Likelihood, Weighted
Subspace Fitting or focusing techniques. This paper shows that bin signals
obtained by filter-bank approaches do not obey the finite rank narrow-band
array model, because spectral leakage and the change of the array response with
frequency within the bin create \emph{ghost sources} dependent on the
particular realization of the source process. Therefore, existing DOA
estimators based on binning cannot claim consistency even with the perfect
knowledge of the array response. In this work, a more realistic array model
with a finite length of the sensor impulse responses is assumed, which still
has finite rank under a space-time formulation. It is shown that signal
subspaces at arbitrary frequencies can be consistently recovered under mild
conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant
eigenvectors of the wide-band space-time sensor cross-correlation matrix. A
novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order
to recover consistency. The number of sources active at each frequency are
estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can
be fed to any subspace fitting DOA estimator at single or multiple frequencies.
Simulations confirm that the new technique clearly outperforms binning
approaches at sufficiently high signal to noise ratio, when model mismatches
exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans.
on Signal Processing on 12 February 1918. @IEEE201
LFM based Wideband DOA Estimation using Deep Neural Network at Low SNR
This work focuses on deep learning-based wideband direction-of-arrival (DoA) estimation for a wideband in particular LFM in case of extreme noise. We propose a convolutional neural network (CNN) that utilizes the correlation matrix to estimate and trained using multi-channel data in low SNR conditions. By using a systematic approach and treating the problem as a way to identify multiple possible DoAs, the CNN is trained to predict DoAs under different SNR conditions. This allows the CNN to accurately estimate the directions from which signals are coming, regardless of the level of noise in the environment. The architecture proposed exhibits robustness to noise, works effectively with a small number of snapshots, and achieves high resolution in angle estimation. Experimental findings demonstrate notable enhancements in performance under low SNR conditions when compared to existing methods, without the need for parameter tuning for correlated and uncorrelated sources. The enhanced robustness of our solution has broad applications in various fields, including wireless array sensors, acoustic microphones, and sonars
The influence of random element displacement on DOA estimates obtained with (Khatri-Rao-)root-MUSIC
Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the error on DOA estimates due to random errors in the array geometry. Therefore, we propose a stochastic collocation method that relies on a generalized polynomial chaos expansion to connect the statistical distribution of random position errors to the resulting distribution of the DOA estimates. We apply this technique to the conventional root-MUSIC and the Khatri-Rao-root-MUSIC methods. According to Monte-Carlo simulations, this novel approach yields a speedup by a factor of more than 100 in terms of CPU-time for a one-dimensional case and by a factor of 56 for a two-dimensional case
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