Abstract
In this paper, we investigate the problem of detecting dynamically evolving signals. We model the signal as an n dimensional vector that is either zero or has s non-zero components. At each time step t ∈ N the nonzero components change their location independently with probability p. The statistical problem is to decide whether the signal is a zero vector or in fact it has non-zero components. This decision is based on m noisy observations of individual signal components collected at times t = 1, . . ., m. We consider two different sensing paradigms, namely adaptive and non-adaptive sensing. For non-adaptive sensing, the choice of components to measure has to be decided before the data collection process started, while for adaptive sensing one can adjust the sensing process based on observations collected earlier. We characterize the difficulty of this detection problem in both sensing paradigms in terms of the aforementioned parameters, with special interest to the speed of change of the active components. In addition, we provide an adaptive sensing algorithm for this problem and contrast its performance to that of non-adaptive detection algorithms.
Original language | English |
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Pages (from-to) | 977-1012 |
Number of pages | 36 |
Journal | Bernoulli |
Volume | 25 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 May 2019 |
Funding
This work was partially supported by a grant from the Nederlandse organisatie voor Wetenschap-pelijk Onderzoek (NWO 613.001.114). We are very grateful for the comments of the editor and the two anonymous referees, which helped improving the presentation.
Keywords
- Adaptive sensing
- Dynamically evolving signals
- Sequential experimental design
- Sparse signals