39,667 research outputs found
Matched subspace detection with hypothesis dependent noise power
We consider the problem of detecting a subspace signal in white Gaussian noise when the noise power may be different under the null hypothesisâwhere it is assumed to be knownâand the alternative hypothesis. This situation occurs when the presence of the signal of interest (SOI) triggers an increase in the noise power. Accordingly, it may be
relevant in the case of a mismatch between the actual SOI subspace and its presumed value, resulting in a modelling error. We derive the generalized likelihood ratio test
(GLRT) for the problem at hand and contrast it with the GLRT which assumes known and equal noise power under the two
hypotheses. A performance analysis is carried out and the distributions of the two test statistics are derived. From this analysis, we discuss the differences between the two detectors and provide explanations for the improved performance of the new detector. Numerical simulations attest to the validity of the analysis
A Fast Blind Impulse Detector for Bernoulli-Gaussian Noise in Underspread Channel
The Bernoulli-Gaussian (BG) model is practical to characterize impulsive
noises that widely exist in various communication systems. To estimate the BG
model parameters from noise measurements, a precise impulse detection is
essential. In this paper, we propose a novel blind impulse detector, which is
proven to be fast and accurate for BG noise in underspread communication
channels.Comment: v2 to appear in IEEE ICC 2018, Kansas City, MO, USA, May 2018 Minor
erratums added in v
Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise
In this paper, we consider the problems of state estimation and false data
injection detection in smart grid when the measurements are corrupted by
colored Gaussian noise. By modeling the noise with the autoregressive process,
we estimate the state of the power transmission networks and develop a
generalized likelihood ratio test (GLRT) detector for the detection of false
data injection attacks. We show that the conventional approach with the
assumption of Gaussian noise is a special case of the proposed method, and thus
the new approach has more applicability. {The proposed detector is also tested
on an independent component analysis (ICA) based unobservable false data attack
scheme that utilizes similar assumptions of sample observation.} We evaluate
the performance of the proposed state estimator and attack detector on the IEEE
30-bus power system with comparison to conventional Gaussian noise based
detector. The superior performance of {both observable and unobservable false
data attacks} demonstrates the effectiveness of the proposed approach and
indicates a wide application on the power signal processing.Comment: 8 pages, 4 figures in IEEE Conference on Communications and Network
Security (CNS) 201
Time-frequency detection of Gravitational Waves
We present a time-frequency method to detect gravitational wave signals in
interferometric data. This robust method can detect signals from poorly modeled
and unmodeled sources. We evaluate the method on simulated data containing
noise and signal components. The noise component approximates initial LIGO
interferometer noise. The signal components have the time and frequency
characteristics postulated by Flanagan and Hughes for binary black hole
coalescence. The signals correspond to binaries with total masses between to and with (optimal filter) signal-to-noise ratios of 7
to 12. The method is implementable in real time, and achieves a coincident
false alarm rate for two detectors 1 per 475 years. At this false
alarm rate, the single detector false dismissal rate for our signal model is as
low as 5.3% at an SNR of 10. We expect to obtain similar or better detection
rates with this method for any signal of similar power that satisfies certain
adiabaticity criteria. Because optimal filtering requires knowledge of the
signal waveform to high precision, we argue that this method is likely to
detect signals that are undetectable by optimal filtering, which is at present
the best developed detection method for transient sources of gravitational
waves.Comment: 24 pages, 5 figures, uses REVTE
Sensitive White Space Detection with Spectral Covariance Sensing
This paper proposes a novel, highly effective spectrum sensing algorithm for
cognitive radio and whitespace applications. The proposed spectral covariance
sensing (SCS) algorithm exploits the different statistical correlations of the
received signal and noise in the frequency domain. Test statistics are computed
from the covariance matrix of a partial spectrogram and compared with a
decision threshold to determine whether a primary signal or arbitrary type is
present or not. This detector is analyzed theoretically and verified through
realistic open-source simulations using actual digital television signals
captured in the US. Compared to the state of the art in the literature, SCS
improves sensitivity by 3 dB for the same dwell time, which is a very
significant improvement for this application. Further, it is shown that SCS is
highly robust to noise uncertainty, whereas many other spectrum sensors are
not
Evaluation of bistable systems versus matched filters in detecting bipolar pulse signals
This paper presents a thorough evaluation of a bistable system versus a
matched filter in detecting bipolar pulse signals. The detectability of the
bistable system can be optimized by adding noise, i.e. the stochastic resonance
(SR) phenomenon. This SR effect is also demonstrated by approximate statistical
detection theory of the bistable system and corresponding numerical
simulations. Furthermore, the performance comparison results between the
bistable system and the matched filter show that (a) the bistable system is
more robust than the matched filter in detecting signals with disturbed pulse
rates, and (b) the bistable system approaches the performance of the matched
filter in detecting unknown arrival times of received signals, with an
especially better computational efficiency. These significant results verify
the potential applicability of the bistable system in signal detection field.Comment: 15 pages, 9 figures, MikTex v2.
Detection of multiplicative noise in stationary random processes using second- and higher order statistics
This paper addresses the problem of detecting the presence of colored multiplicative noise, when the information process can be modeled as a parametric ARMA process. For the case of zero-mean multiplicative noise, a cumulant based suboptimal detector is studied. This detector tests the nullity of a specific cumulant slice. A second detector is developed when the multiplicative noise is nonzero mean. This detector consists of filtering the data by an estimated AR filter. Cumulants of the residual data are then shown to be well suited to the detection problem. Theoretical expressions for the asymptotic probability of
detection are given. Simulation-derived finite-sample ROC curves are shown for different sets of model parameters
- âŠ