We study blind identification and equalization of finite impulse response (FIR) and multi-input and multi-output (MIMO) channels driven by colored signals. We first show a sufficient condition for an FIR MIMO channel ...
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We study blind identification and equalization of finite impulse response (FIR) and multi-input and multi-output (MIMO) channels driven by colored signals. We first show a sufficient condition for an FIR MIMO channel to be identifiable up to a scaling and permutation using the second-order statistics of the channel output. This condition is that the channel matrix is irreducible (but not necessarily column-reduced), and the input signals are mutually uncorrelated and of distinct power spectra. We also show that this condition is necessary in the sense that no single part of the condition can be further weakened without another part being strengthened. While the above condition is a strong result that sets a fundamental limit of blind identification, there does not yet exist a working algorithm under that condition. In the second part of this paper, we show that a method called blind identification via decorrelating subchannels (BIDS) can uniquely identify an FIR MIMO channel if a) the channel matrix is nonsingular (almost everywhere) and column-wise coprime and (b) the input signals are mutually uncorrelated and of sufficiently diverse power spectra. The BIDS method requires a weaker condition on the channel matrix than that required,by most existing methods for the same problem.
We develop methods for automatic detection and localization of landmines using chemical sensorarrays and statistical signal processing techniques. The transport of explosive vapors emanating from buried landmines is ...
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We develop methods for automatic detection and localization of landmines using chemical sensorarrays and statistical signal processing techniques. The transport of explosive vapors emanating from buried landmines is modeled as a diffusion process in a two-layered system consisting of ground and air. Measurement and statistical models are then obtained from the associated concentration distribution. We derive two detectors (the generalized likelihood ratio (GLR) test and the mean detector) and determine their performance in terms of the probabilities of false alarm and detection. To determine the unknown location of a landmine, we derive a maximum likelihood (ML) estimation algorithm and evaluate its performance by computing the Cramer-Rao bound (CRB). The results are applied to the design of chemical sensorarrays, satisfying criteria specified in terms of detection and estimation performance measures and for optimally selecting the number and positions of sensors and the number of time samples, To illustrate the potential of the proposed techniques in a realistic demining scenario, we derive a moving-sensor algorithm in which the stationary sensorarray is replaced by a single moving sensor. Numerical examples are given to demonstrate the applicability of our results.
in this paper we address polarimetric adaptive detection of targets embedded in compound-Gaussian clutter with unknown covariance matrix. To this end we assume that a set of secondary data, free of signal components a...
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in this paper we address polarimetric adaptive detection of targets embedded in compound-Gaussian clutter with unknown covariance matrix. To this end we assume that a set of secondary data, free of signal components and with the same covariance structure of the cell under test, is available. We resort to a design procedure based upon the generalized likelihood ratio test (GLRT): first we derive the GLRT assuming that the textures are known, then we plug into the derived test suitable estimates of these parameters. Remarkably, the newly proposed detector has the constant false alarm rate (CFAR) property with respect to the texture statistical characterization. Moreover, even though it does not ensure the CFAR property with respect to the clutter covariance matrix, a sensitivity analysis shows that the probability of false alarm is only slightly affected by variations in the clutter correlation properties. Finally, the performance assessment, conducted via Monte Carlo simulations, confirms the capability of the receiver to operate in real radar scenarios. (C) 2002 Elsevier Science B.V. All rights reserved.
In this paper, we propose a new direction of arrival (DOA) estimator for sensor-arrayprocessing. The estimator is based on a linear algebraic connection between the standard subspace model of the array correlation ma...
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In this paper, we propose a new direction of arrival (DOA) estimator for sensor-arrayprocessing. The estimator is based on a linear algebraic connection between the standard subspace model of the array correlation matrix and a special signal-plus-interference model, which we develop in this paper. The estimator we propose is a signal subspace scaled MUSIC algorithm, which we call SSMUSIC. It is not a subspace weighted MUSIC, because the scaling depends on the eigenstructure of the estimated signal subspace. SSMUSIC has the advantage of simultaneously estimating the DOA and the power of each source. We employ a second-order perturbation analysis of the estimator and derive stochastic representations for its bias and squared-error. We compare the new DOA estimator with the MUSIC estimator, based on these representations. Numerical results demonstrate the superior performance of SSMUSIC relative to MUSIC and the validity of the perturbation results.
We develop methods for the automatic detection and localization of landmines using chemical sensorarrays and statistical signal processing techniques. The transport of explosive vapors emanating from buried landmines...
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ISBN:
(纸本)0819431842
We develop methods for the automatic detection and localization of landmines using chemical sensorarrays and statistical signal processing techniques. The transport of explosive vapors emanating from buried landmines is modeled as a diffusion process in a two layered system consisting of ground and air. The measurement and statistical models are derived by exploiting the associated concentration distribution. We derive a generalized likelihood ratio (GLR) detector and evaluate its performance in terms of the probabilities of detection and false alarm. To determine the unknown location of a landmine we derive a maximum likelihood (ML) estimation algorithm and evaluate its performance by computing the Cramer-Rao bound (CRB). The results are applied to the design of chemical sensorarrays, satisfying criteria specified in terms of detection and estimation performance measures, and to optimally select the number and positions of sensors and the number of time samples. To illustrate the potential of the proposed techniques in a realistic demining scenario, we derive a moving sensor algorithm in which the stationary sensorarray is replaced by a single moving sensor. Numerical examples are given to demonstrate the applicability of our results.
We consider the problem of estimating directions of arrival (DOAs) of multiple sources observed on the background of nonuniform white noise with an arbitrary diagonal covariance matrix. A new deterministic maximum lik...
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We consider the problem of estimating directions of arrival (DOAs) of multiple sources observed on the background of nonuniform white noise with an arbitrary diagonal covariance matrix. A new deterministic maximum likelihood (ML) DOA estimator is derived. Its implementation is based on an iterative procedure which includes a stepwise concentration of the log-likelihood (LL) function with respect to the signal and noise nuisance parameters and requires only a few iterations to converge. New closed-form expressions for the deterministic and stochastic direction estimation Cramer-Rao bounds (CRBs) are derived for the considered nonuniform model. Our expressions can be viewed as an extension of the well-known results by Stoica and Nehorai and Weiss and Friedlander to a more general noise model than the commonly used uniform one. In addition, these expressions extend the recent results by Matveyev et al. to the multiple source case. Comparisons with the above-mentioned earlier results help to discover several interesting properties of DOA estimation in the nonuniform noise case, To compare the estimation performance of the proposed ML, technique with the results of our CRB analysis and with the performance of conventional "uniform" ME, simulation results are presented. Additionally, we test our technique using experimental seismic array data. Our simulations and experimental results both validate essential performance improvements achieved by means of the approach proposed.
In this paper, we address polarimetric adaptive detection of targets embedded in Gaussian noise with unknown covariance matrix. To this end we assume that a set of secondary data, free of signal components and with th...
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In this paper, we address polarimetric adaptive detection of targets embedded in Gaussian noise with unknown covariance matrix. To this end we assume that a set of secondary data, free of signal components and with the same covariance matrix of the cell under test, is available. We resort to a two-step design procedure based upon the generalized likelihood ratio test (GLRT). It is shown that the newly proposed detector has the constant false alarm rate property with respect to the covariance matrix of the noise. More remarkably, it has the same performance, but a lower complexity, than the corresponding plain GLRT. (C) 2001 Elsevier Science B.V. All rights reserved.
In radio telescope arrays, the complex receiver gains and sensor noise powers are initially unknown and have to be calibrated. Gain calibration enhances the quality of astronomical sky images and moreover, improve the...
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ISBN:
(纸本)0780370112
In radio telescope arrays, the complex receiver gains and sensor noise powers are initially unknown and have to be calibrated. Gain calibration enhances the quality of astronomical sky images and moreover, improve the effectiveness of certain radio telescope phased-array data processing techniques, such as radio interference (RFI) mitigation and beamforming. In this paper we present several closed form and iterative complex gain estimation methods. These methods are analyzed and compared to the Cramer-Rao lower bound for the variance of the estimated gain. The models are tested both on simulated data and on observed telescope data.
This paper investigates a novel closed-form estimation class, so-called weighted estimator (WE). for blind source separation in the basic two-signal problem. Proper combination of previously proposed estimators yields...
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ISBN:
(纸本)0780370112
This paper investigates a novel closed-form estimation class, so-called weighted estimator (WE). for blind source separation in the basic two-signal problem. Proper combination of previously proposed estimators yields consistent estimates of the separation parameters under general conditions. In the real-mixture case, we determine analytic expressions for the WE asymptotic (large-sample) variance and the source-dependent weight value of the most efficient estimator in the class. By means of the bicomplex-number formalism. the WE is extended to the complex-mixture scenario, for which Cramer-Rao bounds are also derived. Simulations compare the WE with other methods, demonstrating its potential.
In this paper, we present a Bayesian approach for DOA and frequency estimation of narrow band signals in additive generalized Gaussian noise. Using Bayesian techniques, the posterior probability densities for DOA (Dir...
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ISBN:
(纸本)0780370112
In this paper, we present a Bayesian approach for DOA and frequency estimation of narrow band signals in additive generalized Gaussian noise. Using Bayesian techniques, the posterior probability densities for DOA (Direction Of Arrival) and frequency parameters are derived from the signal and noise models. These posterior probabilities are then used in the Metropolis-Hastings (M-H) algorithm to derive the samples for the DOA and frequency parameters. The performances of our algorithms are studied by plotting the MSEs (Mean Square Errors) of the parameters for various SNRs. The MSEs of the parameters are compared with the CRLBs (Cramer Rao Lower Bound) for the generalized Gaussian models.
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