Recently, sparse arrays, such as coprime arrays and nested arrays, have been show promise in order to improve active and passive sensing in radar, navigation, under-water acoustics, and wireless communications.

Sparse array signal processing provides a systematical framework for sparse sampling and array structure with enlarged aperture, enhanced spatial resolution, increased degrees of freedom (DOFs), and reduced mutual coupling. Difference co-array-based approaches, e.g., spatial smoothing technique-based algorithms, Toeplitz property-based algorithms, and sparse reconstruction methods, can circumvent spatial aliasing and offer unique responses to targets with sparse sampling in time, space, and frequency. Temporal and spatial sparse sampling encounter merits in direction of arrival (DOA) estimation and adaptive beamforming.

This Special Issue aims to collate original research articles with a focus on the advances made in the processing and applications of sparse arrays, along with the methods and algorithms associated with this. Review articles focused on the current state of the art are also encouraged.

Potential topics include but are not limited to the following:

• Sparse array geometry optimization for high accuracy DOA estimation
• Generalizations of co-prime and nested arrays for increased DOFs
• Beamforming for sparse array antennas
• Sparse array calibration and mutual coupling effect
• Convex and nonconvex optimization related to sparse array signal processing
• Sparse recovery-based methods for DOA estimation in sparse array signal processing
• Multi-dimensional sparse array signal processing
• Coherent DOA estimation for sparse arrays
• DOA tracking for sparse array
• Hardware implementation and design
• Applications to sonar, radar, communication, and other areas