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stochastic-signals
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Statistical performance analysis of MDL source enumeration in array processing
, Article IEEE Transactions on Signal Processing ; Volume 58, Issue 1 , 2010 , Pages 452-457 ; 1053587X (ISSN) ; Malek Mohammadi, M ; Nayebi, M. M ; Aref, M. R ; Sharif University of Technology
Abstract
In this correspondence, we focus on the performance analysis of the widely-used minimum description length (MDL) source enumeration technique in array processing. Unfortunately, available theoretical analysis exhibit deviation from the simulation results. We present an accurate and insightful performance analysis for the probability of missed detection.We also show that the statistical performance of the MDL is approximately the same under both deterministic and stochastic signal models. Simulation results show the superiority of the proposed analysis over available results
A square root sampling law for signal recovery
, Article IEEE Signal Processing Letters ; Volume 26, Issue 4 , 2019 , Pages 562-566 ; 10709908 (ISSN) ; Gohari, A ; Marvasti, F ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
The problem of finding the optimal node density for reconstructing a stochastic signal from its noisy samples in sensor networks is considered. The signal could be nonstationary and nonbandlimited. A weight is assigned to each location that indicates the relative importance of the signal at that location. It is shown that when the number of samples is very large, the optimal density of the samples at each location is proportional to the square root of the weight associated to that location