Sensor Selection for Angle of Arrival Estimation Based on the Two-Target Cramér-Rao Bound

Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. In this work, we...

Array Design Based on the Worst-Case Cramér-Rao Bound to Account for Multiple Targets

Sensor selection is a useful method to help reduce computational, hardware, and power requirements while maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown target directions. We propose to tackle the sensor selection problem...

Sensor selection and rate distribution based beamforming in wireless acoustic sensor networks

Power usage is an important aspect of wireless acoustic sensor networks (WASNs) and reducing the amount of information that is to be transmitted is one effective way to save it. In previous contributions, we presented sensor selection as well as rate distribution methods to reduce the power usage of beamforming algorithms in WASNs. Taking...

Sparse sensing for composite matched subspace detection

In this paper, we propose sensor selection strategies, based on convex and greedy approaches, for designing sparse samplers for composite detection. Particularly, we focus our attention on sparse samplers for matched subspace detectors. Differently from previous works, that mostly rely on random matrices to perform compression of the sub...

Subset Selection for Kernel-Based Signal Reconstruction

In this work, we introduce subset selection strategies for signal reconstruction based on kernel methods, particularly for the case of kernel-ridge regression. Typically, these methods are employed for exploiting known prior information about the structure of the signal of interest. We use the mean squared error and a scalar function of the...