Discrete-time signal processing
Discrete-time signal processing
Wavelets and subband coding
Multirate Digital Signal Processing
Multirate Digital Signal Processing
Decentralized compression and predistribution via randomized gossiping
Proceedings of the 5th international conference on Information processing in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Distributed sparse random projections for refinable approximation
Proceedings of the 6th international conference on Information processing in sensor networks
Rate-constrained collaborative noise reduction for wireless hearing aids
IEEE Transactions on Signal Processing
Identification of Matrices Having a Sparse Representation
IEEE Transactions on Signal Processing
Sampling signals with finite rate of innovation
IEEE Transactions on Signal Processing
Gaussian multiterminal source coding
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Hi-index | 35.68 |
We study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin to a Slepian-Wolf setup, but in the sampling rather than the lossless compression case. Two different scenarios are considered: In the case of universal reconstruction, we look for a sensing and recovery mechanism that works for all possible signals, whereas in what we call almost sure reconstruction, we allow to have a small set (with measure zero) of unrecoverable signals. We derive achievability bounds on the number of samples needed for both scenarios. Our results show that, only in the almost sure setup can we effectively exploit the signal correlations to achieve effective gains in sampling efficiency. In addition to the above theoretical analysis, we propose an efficient and robust distributed sampling and reconstruction algorithm based on annihilating filters. We evaluate the performance of our method in one synthetic scenario, and two practical applications, including the distributed audio sampling in binaural hearing aids and the efficient estimation of room impulse responses. The numerical results confirm the effectiveness and robustness of the proposed algorithm in both synthetic and practical setups.