Single snapshot DOA estimation using compressed sensing

Abstract

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 ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 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.

Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

“…The easiest way to exploit this, known as Digital Beam Forming (DBF), applies a Fourier transform to translate the time delay of received signals at each antenna into a phase shift, which is proportional to the angle of arrival of the signal. There are more advanced algorithms such as MVDR [15], MIMO-Monopulse [16], subspace methods such as MUSIC [17] or ESPRIT [18], or methods based on compressive sensing [19], [20], [21]. However, these methods usually require higher computational costs and longer integration times; hence, they are not always easily applicable in automotive scenarios.…”

Compressive Sensing Based Two-Dimensional DOA Estimation Using L-Shaped Array in a Hostile Environment

“…Also these methods suffer from high computational complexity. However, in hostile conditions, due to physical constraints, less number of snapshots or only one snapshot may be accessible for DOA estimation [17,18]. All adaptive algorithms, which depend on an estimation of noise covariance matrix, will fail for single snapshot instance.…”

Super resolution DOA estimation with incomplete single snapshot data via matrix completion and compress sensing

“…So the DOA estimation with short snapshots is receiving more and more attention and many literature devote to it [1]. More recently, DOA estimation based on CS (CS-DOA) becomes a hot field because of its advantage in fully exploiting the inherent sparseness of the DOA [2] and reducing the observation.…”