Dr. Stefano Fortunati has received the 2019 EURASIP JASP Best Paper Award for the paper Single-snapshot DOA estimation by using Compressed Sensing, a collaboration between CMRE and the University of Pisa in the framework of the CMRE exploratory project on compressive sensing to advance in the application of high resolution acoustic beamforming and target direction of arrival estimation.
The award will be presented to Dr. Fortunati at the next EUSIPCO conference in A Coruna, Spain, 2-6 September 2019.
The paper has been published in the EURASIP Journal of Advances on Signal Processing, a peer-reviewed open access journal published by Springer. It can be downloaded from: https://link.springer.com/article/10.1186/1687-6180-2014-120. Its citation is:
Fortunati, S., Grasso, R., Gini, F. et al. EURASIP J. Adv. Signal Process. (2014) 2014: 120. https://doi.org/10.1186/1687-6180-2014-120
This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.