21,365 research outputs found
A Detailed Investigation into Low-Level Feature Detection in Spectrogram Images
Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model-based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model-based detection has a limitation—it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model-based strategies that perform the same level of information extraction. The implementations used to derive the results presented within this paper are available online from http://stdetect.googlecode.com
Blind Receiver Design for OFDM Systems Over Doubly Selective Channels
We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch
Matched direction detectors and estimators for array processing with subspace steering vector uncertainties
In this paper, we consider the problem of estimating and detecting a signal whose associated spatial signature is known to lie in a given linear subspace but whose coordinates in this subspace are otherwise unknown, in the presence of subspace interference and broad-band noise. This situation arises when, on one hand, there exist uncertainties about the steering vector but, on the other hand, some knowledge about the steering vector errors is available. First, we derive the maximum-likelihood estimator (MLE) for the problem and compute the corresponding Cramer-Rao bound. Next, the maximum-likelihood estimates are used to derive a generalized likelihood ratio test (GLRT). The GLRT is compared and contrasted with the standard matched subspace detectors. The performances of the estimators and detectors are illustrated by means of numerical simulations
Multibaseline gravitational wave radiometry
We present a statistic for the detection of stochastic gravitational wave
backgrounds (SGWBs) using radiometry with a network of multiple baselines. We
also quantitatively compare the sensitivities of existing baselines and their
network to SGWBs. We assess how the measurement accuracy of signal parameters,
e.g., the sky position of a localized source, can improve when using a network
of baselines, as compared to any of the single participating baselines. The
search statistic itself is derived from the likelihood ratio of the cross
correlation of the data across all possible baselines in a detector network and
is optimal in Gaussian noise. Specifically, it is the likelihood ratio
maximized over the strength of the SGWB, and is called the maximized-likelihood
ratio (MLR). One of the main advantages of using the MLR over past search
strategies for inferring the presence or absence of a signal is that the former
does not require the deconvolution of the cross correlation statistic.
Therefore, it does not suffer from errors inherent to the deconvolution
procedure and is especially useful for detecting weak sources. In the limit of
a single baseline, it reduces to the detection statistic studied by Ballmer
[Class. Quant. Grav. 23, S179 (2006)] and Mitra et al. [Phys. Rev. D 77, 042002
(2008)]. Unlike past studies, here the MLR statistic enables us to compare
quantitatively the performances of a variety of baselines searching for a SGWB
signal in (simulated) data. Although we use simulated noise and SGWB signals
for making these comparisons, our method can be straightforwardly applied on
real data.Comment: 17 pages and 19 figure
ML Detection in Phase Noise Impaired SIMO Channels with Uplink Training
The problem of maximum likelihood (ML) detection in training-assisted
single-input multiple-output (SIMO) systems with phase noise impairments is
studied for two different scenarios, i.e. the case when the channel is
deterministic and known (constant channel) and the case when the channel is
stochastic and unknown (fading channel). Further, two different operations with
respect to the phase noise sources are considered, namely, the case of
identical phase noise sources and the case of independent phase noise sources
over the antennas. In all scenarios the optimal detector is derived for a very
general parametrization of the phase noise distribution. Further, a high
signal-to-noise-ratio (SNR) analysis is performed to show that
symbol-error-rate (SER) floors appear in all cases. The SER floor in the case
of identical phase noise sources (for both constant and fading channels) is
independent of the number of antenna elements. In contrast, the SER floor in
the case of independent phase noise sources is reduced when increasing the
number of antenna elements (for both constant and fading channels). Finally,
the system model is extended to multiple data channel uses and it is shown that
the conclusions are valid for these setups, as well.Comment: (To appear in IEEE Transactions on Communications, 2015), Contains
additional material (Appendix B. T-slot Detectors
Joint data detection and channel estimation for OFDM systems
We develop new blind and semi-blind data detectors and channel estimators for orthogonal frequency-division multiplexing (OFDM) systems. Our data detectors require minimizing a complex, integer quadratic form in the data vector. The semi-blind detector uses both channel correlation and noise variance. The quadratic for the blind detector suffers from rank deficiency; for this, we give a low-complexity solution. Avoiding a computationally prohibitive exhaustive search, we solve our data detectors using sphere decoding (SD) and V-BLAST and provide simple adaptations of the SD algorithm. We consider how the blind detector performs under mismatch, generalize the basic data detectors to nonunitary constellations, and extend them to systems with pilots and virtual carriers. Simulations show that our data detectors perform well
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