8,018 research outputs found
Adaptive processing with signal contaminated training samples
We consider the adaptive beamforming or adaptive detection problem in the case of signal contaminated training samples, i.e., when the latter may contain a signal-like component. Since this results in a significant degradation of the signal to interference and noise ratio at the output of the adaptive filter, we investigate a scheme to jointly detect the contaminated samples and subsequently take this information into account for estimation of the disturbance covariance matrix. Towards this end, a Bayesian model is proposed, parameterized by binary variables indicating the presence/absence of signal-like components in the training samples. These variables, together with the signal amplitudes and the disturbance covariance matrix are jointly estimated using a minimum mean-square error (MMSE) approach. Two strategies are proposed to implement the MMSE estimator. First, a stochastic Markov Chain Monte Carlo method is presented based on Gibbs sampling. Then a computationally more efficient scheme based on variational Bayesian analysis is proposed. Numerical simulations attest to the improvement achieved by this method compared to conventional methods such as diagonal loading. A successful application to real radar data is also presented
Separating Gravitational Wave Signals from Instrument Artifacts
Central to the gravitational wave detection problem is the challenge of
separating features in the data produced by astrophysical sources from features
produced by the detector. Matched filtering provides an optimal solution for
Gaussian noise, but in practice, transient noise excursions or ``glitches''
complicate the analysis. Detector diagnostics and coincidence tests can be used
to veto many glitches which may otherwise be misinterpreted as gravitational
wave signals. The glitches that remain can lead to long tails in the matched
filter search statistics and drive up the detection threshold. Here we describe
a Bayesian approach that incorporates a more realistic model for the instrument
noise allowing for fluctuating noise levels that vary independently across
frequency bands, and deterministic ``glitch fitting'' using wavelets as
``glitch templates'', the number of which is determined by a trans-dimensional
Markov chain Monte Carlo algorithm. We demonstrate the method's effectiveness
on simulated data containing low amplitude gravitational wave signals from
inspiraling binary black hole systems, and simulated non-stationary and
non-Gaussian noise comprised of a Gaussian component with the standard
LIGO/Virgo spectrum, and injected glitches of various amplitude, prevalence,
and variety. Glitch fitting allows us to detect significantly weaker signals
than standard techniques.Comment: 21 pages, 18 figure
Canonical correlation analysis of high-dimensional data with very small sample support
This paper is concerned with the analysis of correlation between two
high-dimensional data sets when there are only few correlated signal components
but the number of samples is very small, possibly much smaller than the
dimensions of the data. In such a scenario, a principal component analysis
(PCA) rank-reduction preprocessing step is commonly performed before applying
canonical correlation analysis (CCA). We present simple, yet very effective
approaches to the joint model-order selection of the number of dimensions that
should be retained through the PCA step and the number of correlated signals.
These approaches are based on reduced-rank versions of the Bartlett-Lawley
hypothesis test and the minimum description length information-theoretic
criterion. Simulation results show that the techniques perform well for very
small sample sizes even in colored noise
Semiblind Channel Estimation and Data Detection for OFDM Systems With Optimal Pilot Design
This paper considers semiblind channel estimation and data detection for orthogonal frequency-division multiplexing (OFDM) over frequency-selective fading channels. We show that the samples of an OFDM symbol are jointly complex Gaussian distributed, where the mean and covariance are determined by the locations and values of fixed pilot symbols. We exploit this distribution to derive a novel maximum-likelihood (ML) semiblind gradient-descent channel estimator. By exploiting the channel impulse response (CIR) statistics, we also derive a semiblind data detector for both Rayleigh and Ricean fading channels. Furthermore, we develop an enhanced data detector, which uses the estimator error statistics to mitigate the effect of channel estimation errors. Efficient implementation of both the semiblind and the improved data detectors is provided via sphere decoding and nulling-canceling detection. We also derive the Cramér-Rao bound (CRB) and design optimal pilots by minimizing the CRB. Our proposed channel estimator and data detector exhibit high bandwidth efficiency (requiring only a few pilot symbols), achieve the CRB, and also nearly reach the performance of an ideal reference receiver
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