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This paper is concerned with a detection framework under scheduled communication for a binary hypothesis testing problem. A scheduler is designed to smartly select useful sensor measurements for transmission and leave non-useful ones, which results in that only a subset of measurements is sent to the testing agency. To this purpose, a likelihood ratio based scheduler is implemented to decide the transmission...
For active sensors, waveform/signal optimization is of great importance to improve system performance. In this paper, the adaptive waveform parameter is designed to improve the classification performance by minimizing the Bayesian error probability for the optimal decision of a symmetric binary hypothesis testing problem. It is well known that the probability of error can be bounded by the Chernoff...
This paper focuses on optimal detection of quadrature phase-shift keying (QPSK) and binary phase-shift keying (BPSK) direct sequence spread spectrum (DS-SS) signals in additive white Gaussian noise (AWGN) with unknown spreading sequence and other parameters. Using complex Gaussian mixture signal model and invariance principle, we derive constant-false-alarm-rate (CFAR) invariant detectors such as...
In this paper we propose a nonparametric hypothesis test for stationarity based on local Fourier analysis. We employ a test statistic that measures the variation of time-localized estimates of the power spectral density of an observed random process. For the case of a white Gaussian noise process, we characterize the asymptotic distribution of this statistic under the null hypothesis of stationarity,...
We consider the sensor selection problem in a wireless sensor network attempting to solve a binary hypothesis testing problem. The selection is based only on the sensor observations and the focus is on the extreme case where the position of the sensors is not exploited except through its influence on the sensor observations. Decentralized processing approaches are desired. A subset of sensors are...
The accuracy in classifying electroencephalographic (EEG) data in brain-computer interfaces (BCI) depends on the number of measuring channels, the amount of data used to train the classifier, and the signal-to-noise ratio (SNR). Of all those factors, the SNR is the hardest to adjust in real-life applications. For this reason, a spatial filter based on a linear minimum mean squared error (LMMSE) beamformer...
Many locally optimum, sub-optimum and ad hoc detectors exploit knowledge of the noise probability density function (PDF) to obtain their test statistic for the detection of stochastic signals in non-Gaussian noise and appear to offer potential for highly improved performance for very small signal to noise ratios. The authors explore the realism of this conclusion by comparing performance of a locally...
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