TY - GEN
T1 - Optimal sensing matrix for sparse linear models
AU - Pazos, S.
AU - Hurtado, M.
AU - Muravchik, C.
AU - Nehorai, A.
PY - 2011
Y1 - 2011
N2 - In this paper, we propose a method for designing the optimal sensing of measurements which can be characterized by a sparse linear model. The aim of the sensing operation is not only to reduce the amount of data to be processed but also to reject undesired signals (interferences). As a result, we reduce the computation time and the error for estimating the unknown parameters of the model, with respect to the uncompressed data. Using synthetic data, we analyze the performance of the proposed algorithm. Additionally, we use real radar data to show an application of the method.
AB - In this paper, we propose a method for designing the optimal sensing of measurements which can be characterized by a sparse linear model. The aim of the sensing operation is not only to reduce the amount of data to be processed but also to reject undesired signals (interferences). As a result, we reduce the computation time and the error for estimating the unknown parameters of the model, with respect to the uncompressed data. Using synthetic data, we analyze the performance of the proposed algorithm. Additionally, we use real radar data to show an application of the method.
UR - https://www.scopus.com/pages/publications/84857164655
U2 - 10.1109/CAMSAP.2011.6135997
DO - 10.1109/CAMSAP.2011.6135997
M3 - Conference contribution
AN - SCOPUS:84857164655
SN - 9781457721052
T3 - 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
SP - 257
EP - 260
BT - 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
T2 - 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Y2 - 13 December 2011 through 16 December 2011
ER -