Abstract — The performance of Neyman-Pearson detection of correlated stochastic signals using noisy observations is investigated via the error exponent for the miss probability with a fixed level. Using the statespace structure of the signal and observation model, a closed-form expression for the error exponent is derived, and the connection between the asymptotic behavior of the optimal detector and that of the Kalman filter is established. The properties of the error exponent are investigated for the scalar case. It is shown that the error exponent has distinct characteristics with respect to correlation strength: for signal-to-noise ratio (SNR)> 1 the error exponent decreases monotonically as the correlation becomes stronger, whereas ...
The problem of hypothesis testing against independence for a Gauss-Markov random field (GMRF) with n...
We consider the problem of detecting a sparse random signal from the compressive measurements withou...
The detection of hidden two-dimensional Gauss-Markov random fields us-ing sensor networks is conside...
A numerical method for computing the error exponent for Neyman–Pearson detection of two-state Marko...
A numerical method for computing the error exponent for Neyman–Pearson detection of two-state Marko...
Abstract—A numerical method for computing the error exponent for Neyman–Pearson detection of two-sta...
This paper investigates the decentralized detection of Hidden Markov Processes using the Neyman-Pear...
This paper handles the problem of detecting signals with known signature and unknown or random ampli...
This paper handles the problem of detecting signals with known signature and unknown or random ampli...
This paper handles the problem of detecting signals with known signature and unknown or random ampli...
We study detection of random signals corrupted by noise that over time switch their values (states) ...
This paper handles the problem of detecting signals with known signature and unknown or random ampli...
The closed-form solution for Neyman-Pearson optimal detector performance for Laplace noise affords a...
The performance in signal detection is evaluated by the error (false-alarm and missed-detection) pro...
The problem of hypothesis testing against independence for a Gauss-Markov random field (GMRF) is ana...
The problem of hypothesis testing against independence for a Gauss-Markov random field (GMRF) with n...
We consider the problem of detecting a sparse random signal from the compressive measurements withou...
The detection of hidden two-dimensional Gauss-Markov random fields us-ing sensor networks is conside...
A numerical method for computing the error exponent for Neyman–Pearson detection of two-state Marko...
A numerical method for computing the error exponent for Neyman–Pearson detection of two-state Marko...
Abstract—A numerical method for computing the error exponent for Neyman–Pearson detection of two-sta...
This paper investigates the decentralized detection of Hidden Markov Processes using the Neyman-Pear...
This paper handles the problem of detecting signals with known signature and unknown or random ampli...
This paper handles the problem of detecting signals with known signature and unknown or random ampli...
This paper handles the problem of detecting signals with known signature and unknown or random ampli...
We study detection of random signals corrupted by noise that over time switch their values (states) ...
This paper handles the problem of detecting signals with known signature and unknown or random ampli...
The closed-form solution for Neyman-Pearson optimal detector performance for Laplace noise affords a...
The performance in signal detection is evaluated by the error (false-alarm and missed-detection) pro...
The problem of hypothesis testing against independence for a Gauss-Markov random field (GMRF) is ana...
The problem of hypothesis testing against independence for a Gauss-Markov random field (GMRF) with n...
We consider the problem of detecting a sparse random signal from the compressive measurements withou...
The detection of hidden two-dimensional Gauss-Markov random fields us-ing sensor networks is conside...