Special Issue on Optimum Sparse Arrays and Sensor Placement for Environmental Sensing
• 大类 : 工程技术 - 3区
• 小类 : 工程：电子与电气 - 3区
Sampling using a set of spatially distributed sensors finds extensive applications for environmental sensing. Environmental sensing can be either active or passive. Active sensing is achieved by transmitting probing signals and measuring target backscattering from, e.g., an airborne or ground-based vehicle, or an indoor robot. Passive sensing, on the other hand, aims at localizing emitters using signals of opportunity, including electromagnetic, acoustics, and ultrasound. Sparse arrays are under-sampled sensor arrays, in which several sensors are removed from the original configuration. Sparse arrays may create spatial aliasing, which can be avoided by optimizing sensor placements. In addition to the employed signal processing scheme, sensor placement affects the underlying inference performance. That is, non-optimal sensor placement configurations might lead to significantly low signal-to-noise or signal-to-interference ratios. Since the number of sensors typically dictates the number of costly front-end transmitters and receivers, sensing objectives are constrained by the limited number of available sensors and their permissible positions.
This Special Issue deals with optimum sparse arrays and sensor placement for environment sensing, including detection, localization, estimation, imaging and classification. It focuses on sparsity in the sensing, and not limited to sparse signal recovery. The Special Issue welcomes contributions in astronomy, structural health monitoring, radar and ultrasound imaging, wireless communications and 5G, graph signal processing, and other application areas.
Topics to be covered in this Special Issue include but are not limited to:
Sensor placement under a single or multiple objectives
Sensor placement using convex and submodular optimization methods
Knowledge-based and cognitive sparse array design
Hardware realization and design
Machine learning techniques for sensor placement
Sparse sampling for graphs and networks
Applications to sonar, radar, ultrasound imaging, MRI, radio astronomy, localization, speech enhancement, 5G and mmWave systems