Sparse Sensor Array Design and Processing for High-Resolution Sensing
This talk addresses direction-of-arrival (DOA) estimation, a critical function in radar and wireless communications. We will present recent advances in sparse sensor arrays, which offer high spatial resolution and accuracy with reduced hardware costs. These innovative solutions utilize compressive sensing, convex optimization, and machine learning. The focus will be on design strategies and methods that increase efficiency for various applications.
Sub-Nyquist Tensor Array Signal Processing
Tensor signal modeling is integrated with sub-Nyquist sampling to reduce storage and processing costs for multidimensional signals.The theory of sub-Nyquist tensor signals is developed, generating a higher-order coarray tensor whose missing slices are completed to correspond to the Nyquist regime.Based on this formulation, DOA estimation techniques based on the coarray tensor and sub-Nyquist beamforming are presented.Finally, promising research trends in this new paradigm are discussed.
