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A problem of image denoising, when images are corrupted by a non-stationary noise, is considered in this paper. Since, in practice, no a priori information on noise is available, noise statistics should be pre-estimated prior to image denoising. In this paper, deep convolutional neural network (CNN) based method for estimation of a map of local, patch-wise, standard deviations of noise (so-called sigma-map) is proposed. It achieves the state-of-the-art performance in accuracy of estimation of sigma-map for the case of non-stationary noise, as well as estimation of a noise variance for the case of an additive white Gaussian noise. Extensive experiments on image denoising using estimated sigma-maps demonstrate that our method outperforms recent CNN-based blind image denoising methods by up to 6 dB in PSNR, as well as other state-of-the-art methods based on sigma-map estimation by up to 0.5 dB, providing, at the same time, better usage flexibility. A comparison with the ideal case, when denoising is applied using ground-truth sigma-map, shows that a difference of corresponding PSNR values for the most of noise levels is within 0.1-0.2 dB, and does not exceed 0.6 dB. [ABSTRACT FROM AUTHOR]

We consider Direction of Arrival (DoA) estimation in the case of two closely spaced sources. In this case, most high resolution techniques fail to estimate the two DoAs if the waveforms are highly correlated. Maximum Likelihood Estimators (MLE) are known to be more robust, but their excessive computational load limits their use in practice. In this paper, we propose an asymptotic approximation of the Unconditional Maximum Likelihood (UML) procedure in the case of a Uniform Linear Array (ULA) and two closely spaced targets. This approximation is based on an asymptotically (in the number of observations) equivalent formulation of the UML criterion, and on its Taylor series approximation for small DoA separation. This simplified procedure, which requires solving a 1D-optimization problem only, is shown to be accurate for source separation lower than half the mainlobe. Furthermore, it outperforms conventional high resolution algorithms in the case of two correlated sources. [ABSTRACT FROM AUTHOR]

After revealing that both late reverberation and noise are additive interference components in the residual domain, this paper proposes to suppress these additive interference components by using a constrained minimum mean square error linear prediction (LP) residual estimator, where the optimal filter can be obtained by the generalized singular value decomposition. We propose to estimate the LP residuals for both late reverberation and noise continuously, which is based on the non-VAD related noise power spectral density estimator and the incessant late reverberant spectral variance estimator. The non-intrusive objective measure and the PESQ show that the proposed algorithm is better than traditional LP residual-based algorithms and spectral subtraction-based algorithms. [ABSTRACT FROM AUTHOR]

Any regularization method requires the selection of a penalty parameter and many model selection criteria have been developed based on various discrepancy measures. Most of the attention has been focused on prediction mean squared error. In this paper we develop a model selection criterion based on regression parameter mean squared error via SURE (Stein's unbiased risk estimator). We then apply this to the l1 penalized least squares problem with grouped variables on over-determined systems. Simulation results based on topology identification of a sparse network are presented to illustrate and compare with alternative model selection criteria. [ABSTRACT FROM AUTHOR]

The step-size in well-known variable step-size least mean square (VSSLMS) is updated as μn+1 = αμn + γen2 with , γ > 0, and μn+1 is set to μmin or μmax when it falls below or above these lower and upper bounds, respectively. It provides fast convergence at early stages of adaptation while ensuring small steady-state misalignment. This paper considers the steady-state performance of the VSSLMS in non-Gaussian noise environments. The contribution of the paper to the VSSLMS is threefold; (1) when γ ≪ 1 - α, the VSSLMS has low steady-state misalignment. (2) when α ≪ 1, the VSSLMS achieves different steady-state misalignments for different noise distributions. (3) In theory, there are different optimal values α for different noise distributions, i.e., 0.17 (Gaussian distribution), 0.21 (Student distribution), 0.38 (Laplace distribution), 0 (Binary and Uniform distributions). Analytical results are compared with simulations and are shown to agree well. [ABSTRACT FROM AUTHOR]

Considering that spectral components of one random process are not necessarily independent for all types of signals, this paper defines a generalized auto-spectral coherence function (GAS-CF) to measure this spectral correlation. The GAS-CF is a generalization of the temporal coherence function and the spectral coherence function, where they have already been successfully applied to detect howling components and transient noise components, respectively. After defining the GAS-CF, this paper studies its statistical properties in detail. Simulation results show that the proposed GAS-CF can be applied to detect different types of signals, including transient noise, howling frequency and chirp signal, in a simple way. [ABSTRACT FROM AUTHOR]

In this paper, an empirical characteristic function (ECF) based estimator of the line-of-sight (LOS) power for the near user in an uplink non-orthogonal multiple access (NOMA) system with unknown impulsive noise is proposed. The channels between the users and the base station (BS) are assumed to have Rician fading. The performance of the proposed estimator is analytically evaluated, which demonstrates the asymptotic unbiasedness and consistency of the estimator. Furthermore, the approximate expression derived for the variance is confirmed through computer simulations. Numerical results show that the proposed estimator outperforms the previous estimator for the LOS power in the mixture of Gaussian and $\alpha$ -stable noise. [ABSTRACT FROM AUTHOR]

Filtering additive white Gaussian noise in images using the best linear unbiased estimator (BLUE) is technically sound in a sense that it is an optimal average filter derived from the statistical estimation theory. The BLUE filter mask has the theoretical advantage in that its shape and its size are formulated in terms of the image signals and associated noise components. However, like many other noise filtering problems, prior knowledge about the additive noise needs to be available, which is often obtained using training data. This paper presents the sequential Gaussian simulation in geostatistics for measuring signal and noise variances in images without the need of training data for the BLUE filter implementation. The simulated signal variance and the BLUE average can be further used as parameters of the adaptive Wiener filter for image restoration. [ABSTRACT FROM PUBLISHER]

Algorithms for signal denoising that combine wavelet-domain sparsity and total variation (TV) regularization are relatively free of artifacts, such as pseudo-Gibbs oscillations, normally introduced by pure wavelet thresholding. This paper formulates wavelet-TV (WATV) denoising as a unified problem. To strongly induce wavelet sparsity, the proposed approach uses non-convex penalty functions. At the same time, in order to draw on the advantages of convex optimization (unique minimum, reliable algorithms, simplified regularization parameter selection), the non-convex penalties are chosen so as to ensure the convexity of the total objective function. A computationally efficient, fast converging algorithm is derived. [ABSTRACT FROM AUTHOR]

Sparse representation has been popular in visual tracking recently for its robustness and accuracy. However, for most conventional sparse coding based trackers, the target candidates are considered independently between consecutive frames. This paper shows that the temporal correlation of these frames can be exploited to improve the performance of tracking and makes the tracker more robust to noise. Furthermore, to improve the tracking speed, we revisit a more efficient method for \ell _1 norm problem, marginal regression, which can solve the sparse coding problem more efficiently. Consequently we can realize real-time tracking based on the temporal smooth sparse representation. Extensive experiments have been done to demonstrate the effectiveness and efficiency of our method. [ABSTRACT FROM AUTHOR]

Maximum Voiced Frequency (MVF) is used in various speech models as the spectral boundary separating periodic and aperiodic components during the production of voiced sounds. Recent studies have shown that its proper estimation and modeling enhance the quality of statistical parametric speech synthesizers. Contrastingly, these same methods of MVF estimation have been reported to degrade the performance of singing voice synthesizers. This paper proposes a new approach for MVF estimation which exploits both amplitude and phase spectra. It is shown that phase conveys relevant information about the harmonicity of the voice signal, and that it can be jointly used with features derived from the amplitude spectrum. This information is further integrated into a maximum likelihood criterion which provides a decision about the MVF estimate. The proposed technique is compared to two state-of-the-art methods, and shows a superior performance in both objective and subjective evaluations. Perceptual tests indicate a drastic improvement in high-pitched voices. [ABSTRACT FROM AUTHOR]

In modern communication systems, power amplifiers (PAs) are important components and inherently nonlinear. The nonlinearity of the PA causes bandwidth expansion of the communication signal, often referred to as spectral regrowth, at the PA output. Conventionally, spectral regrowth is treated as a distortion, and a range of compensation and filtering techniques have been considered to mitigate its effect. In this paper, we propose to exploit spectral regrowth to enhance channel identification accuracy. Our approach is motivated by the fact that the nonlinearly amplified communication signal carries more bandwidth and allows better probing of the channel. We introduce an iterative algorithm which jointly estimates the PA characteristics and the channel impulse response. The effectiveness of the proposed algorithm is illustrated by computer simulation. [ABSTRACT FROM AUTHOR]

Image compression evidence has been utilized as an important forensic feature to justify image authenticity. However, some recent studies showed that the compression evidence of block transform-based image coding, e.g., JPEG and JPEG2000, can be effectively erased by adding designed dither noise in the transform domain. In this paper, we demonstrate that it is also feasible to hide the compression evidence of lossy predictive image coding, a class of compression paradigm widely employed in critical scenarios. To tackle the challenging issue of error propagation inherent to predictive coding, we design a prediction-direction preserving strategy, allowing us to add dither noise in the prediction error (PE) domain, while minimizing the incurred distortion. Extensive experimental results are provided to verify the effectiveness of the proposed anti-forensic algorithm for lossy predictive image coding. [ABSTRACT FROM PUBLISHER]

Motivated by application of Gaussian sum filters (GSF) and multiple model adaptive estimation (MMAE) approaches in scenarios where assumption of proper (circular) Gaussian signals is not valid, the letter proposes a novel complex-valued Gaussian sum filter (C/GSF) for non-linear filtering of non-Gaussian/non-circular measurement noise. Although the literature on recursive state estimation using GSF is rich, its complex-valued counterpart which incorporates the full second-order statistics of the system and can cope with non-Gaussian/non-circular measurements, has not yet been investigated in the literature. The paper addresses this gap. The C/GSF is a computationally attractive adaptive filter where the number of non-circular Gaussian components is controlled utilizing a modified Bayesian learning technique which is used to collapse the resulting non-Gaussian sum mixture into an equivalent complex-valued Gaussian term. Simulation results indicate that the C/GSF provides significant performance improvement over its traditional counterparts. [ABSTRACT FROM AUTHOR]

Numerous audio signal processing and analysis techniques using non-negative matrix factorization (NMF) have been developed in the past decade, particularly for the task of source separation. NMF-based algorithms iteratively optimize a cost function. However, the correlation between cost functions and application-dependent performance metrics is less known. Furthermore, to the best of our knowledge, no formal heuristic to compute a stopping criterion tailored to a given application exists in the literature. In this paper, we examine this problem for the case of supervised and semi-supervised NMF-based source separation and show that iterating these algorithms to convergence is not optimal for this application. We propose several heuristic stopping criteria that we empirically found to be well correlated with source separation performance. Moreover, our results suggest that simply integrating the learning of an appropriate stopping criterion in a sweep for model size selection could lead to substantial performance improvements with minimal additional effort. [ABSTRACT FROM AUTHOR]

The scene dynamics can provide useful statistical information for adjusting parameters of Gaussian mixture models (GMMs) in video surveillance. The contributions of this paper are twofold. First, an adaptive scene dynamics estimation approach is proposed. Second, we propose a scene-dynamics based method to adjust two types of GMMs' parameters, i.e., the learning rates and number of Gaussian components. For the learning rates, the scene dynamics are integrated into different kinds of pixel-type feedback schemes to control different kinds of learning rates. Experimental results demonstrate that the proposed method can effectively improve the performance of GMMs in surveillance scenes with complex dynamic backgrounds. [ABSTRACT FROM AUTHOR]

A variable tap-length LMS algorithm based on adaptive parameters for tapped-delay-line (TDL) structure adaption is proposed in this paper. The objective of this work is to overcome some drawbacks of previous variable tap-length adaptive filter algorithms, including slow convergence speed and high sensitivity to noise. Novel tap-length iteration equation based on adaptive parameters and arctangent-limited value is proposed. The values of adaptive parameters can automatically change according to the filter state, which not only improves the convergence speed but also reduces the steady-state error. The arctangent-limited value can effectively reduce the instantaneous estimated error, which enhances the robustness to noise. Computer simulations are conducted to verify the performance of proposed algorithm. [ABSTRACT FROM AUTHOR]

Unique word-orthogonal frequency division multiplexing (UW-OFDM) is known to feature an excellent bit error ratio performance when compared to conventional OFDM using cyclic prefixes (CP). In recent papers classical and Bayesian linear data estimators as well as the non-linear sphere decoding (SD) approach have been investigated in the UW-OFDM context. In general, the remaining error samples after a linear data estimation in UW-OFDM are correlated. In this work, noise interpolation (NI) is proposed, which uses preliminary data decisions together with Wiener interpolation to exploit these correlations. It turns out that the selection of samples to be used for NI is crucial. With the sample selection policy suggested in this work, NI clearly outperforms LMMSE data estimation, and due to its lower complexity compared to SD, it can be regarded as an attractive compromise for UW-OFDM reception. [ABSTRACT FROM AUTHOR]

The discrete Fourier slice theorem is an important tool for signal processing, especially in the context of the exact reconstruction of an image from its projected views. This paper presents a digital reconstruction algorithm to recover a two dimensional (2-D) image from sets of discrete one dimensional (1-D) projected views. The proposed algorithm has the same computational complexity as the 2-D fast Fourier transform and remains robust to the addition of significant levels of noise. A mapping of discrete projections is constructed to allow aperiodic projections to be converted to projections that assume periodic image boundary conditions. Each remapped projection forms a 1-D slice of the 2-D Discrete Fourier Transform (DFT) that requires no interpolation. The discrete projection angles are selected so that the set of remapped 1-D slices exactly tile the 2-D DFT space. This permits direct and mathematically exact reconstruction of the image via the inverse DFT. The reconstructions are artefact free, except for projection inconsistencies that arise from any additive and remapped noise. We also present methods to generate compact sets of rational projection angles that exactly tile the 2-D DFT space. The improvement in noise suppression that comes with the reconstruction of larger sized images needs to be balanced against the corresponding increase in computation time. [ABSTRACT FROM AUTHOR]

Multivariate regression is one of the most widely applied multivariate statistical methods with many uses across a range of disciplines. But the number of parameters increases exponentially with dimension and reduced-rank regression (RRR) is a well known approach to dimension reduction. But traditional RRR applies only to an overdetermined system. For increasingly common undetermined systems this issue can be managed by regularization, e.g., with a quadratic penalty. A significant problem is then the choice of the two tuning parameters: one discrete i.e., the rank; the other continuous i.e., the Tikhonov penalty parameter. In this paper we resolve this problem via Stein's unbiased risk estimator (SURE). We compare SURE to cross-validation and apply it on both simulated and real data sets. [ABSTRACT FROM AUTHOR]

Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. Many approaches have been proposed when both primary user and secondary user employ a single antenna. Recently several techniques have also been proposed assuming that the the secondary user employs multiple antennas. In this paper, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary user's signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. Through analysis we exhibit the role of the eigenvalues in characterizing the signal+noise and noise subspaces in the received data. Simulation results are presented in terms of the receiver operating characteristics and detection probabilities for several cases of interest. [ABSTRACT FROM PUBLISHER]

This paper presents a low-complexity one-dimensional (1-D) Unitary Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)-based algorithm for multidimensional harmonic retrieval (MHR) problems based on an HIerarchical Signal Separation (HISS) technique, which interleaves the parameter estimation and filtering processes. The filtering process not only progressively partitions the signals with close parameters into separate groups, but also reduces the power of the additive noise, both of which entail higher parameter estimation accuracy. The pairing of the estimated parameters is also automatically achieved. Simulations show that the new algorithm provides satisfactory performance compared with previous works but with drastically reduced computations. [ABSTRACT FROM PUBLISHER]

The parameterized multichannel Wiener filter (PMWF) is known to allow for a flexible tuning of speech distortion and noise reduction. In addition, the output signal-to-noise ratio (SNR) is a natural metric that shows the true effect of such a filter on both residual noise and filtered speech. Earlier contributions have only shown that the output SNR of the Wiener filter is larger than the input SNR (as a proof of its effectiveness). However, the effect of the tuning parameter (denoted as following the notation of [1]) on this metric is not yet understood. In this paper, we prove that the global (fullband) output SNR of the PMWF is an increasing function of but remains below an asymptotic value. As a byproduct, a very simplified proof of the global output SNR improvement is provided. [ABSTRACT FROM AUTHOR]

Spatial smoothing is a widely used preprocessing scheme for direction-of-arrival (DOA) estimation of more than one Source from a single snapshot, although the effective array aperture gets reduced by this process. In this paper we propose a preprocessing scheme applicable for DOA estimation algorithms that exploit the shift invariance property of the array steering matrix and call it spatial smoothing with improved aperture (SSIA). SSIA, when applied to a noise corrupted data vector, improves the effective array aperture significantly as opposed to conventional spatial smoothing. Simulations confirm the significant performance gain provided by SSI in conjunction with Unitary ESPRIT. [ABSTRACT FROM AUTHOR]

In this paper, we study the restoration of blurred images corrupted by impulse noise or mixed impulse plus Gaussian noises. In the proposed method, we use the nodified total variation minimization scheme to regularize the deblurred image and fill in suitable values for noisy image pixels where these are detected by median-type filters. An alternating minimization algorithm is employed to solve the proposed total variation minimization problem. Our experimental results show the proposed algorithm is very efficient and the quality of restored images I y the proposed method is competitive with those restored by the existing variational image restoration methods. [ABSTRACT FROM AUTHOR]

In this paper, the alignment of noisy high-resolution radar signals using the Maximum Position method is studied. The relationship between the shift estimation and the signal-to-noise ratio is considered. As a result, two analytical expressions are obtained that approximate the root-mean-square error of the difference in the shift estimation with and without noise. These two expressions allow us to improve the understanding of the sensitivity to noise of the Maximum Position alignment method. [ABSTRACT FROM AUTHOR]

Under Gaussian assumptions, the sample covariance matrix (SCM) is encountered in many covariance based processing algorithms. In case of impulsive noise, this estimate is no more appropriate. This is the reason why when the noise is modeled by spherically invariant random vectors (SIRV), a natural extension of the SCM is extensively used in the literature: the well-known normalized sample covariance matrix (NSCM), which estimates the covariance of SIRV. Indeed, this estimate gets rid of a fluctuating noise power and is widely used in radar applications. The aim of this paper is to derive closed-form expressions of the first- and second-order moments of the NSCM. [ABSTRACT FROM AUTHOR]

This paper concerns the unscented Kalman filtering (UKF) for the nonlinear dynamic systems with additive process and measurement noises. It is widely accepted for such a case that the system state needs not to be augmented with noise vectors and the resultant nonaugmented UKF yields similar, if not the same, results to the augmented UKF. In this letter, we find that under the condition of n + k = const, the basic difference between them is that the augmented UKF draws a sigma set only once within a filtering recursion, while the nonaugmented UKF has to redraw a new set of sigma points to incorporate the effect of additive process noise. This difference generally favors the augmented UKF in that the odd-order moment information is partly captured by the nonlinearly transformed sigma points and propagated throughout the recursion. The simulation results agree well with the analyses. [ABSTRACT FROM AUTHOR]

A new filter for impulsive noise removal is presented here. The problem of impulsive noise elimination is closely connected with the problem of maximal preservation of image edges. To avoid smoothing of the image during filtering, all noisy pixels must be detected. We consider here an approach, which is based on threshold Boolean filtering, where the binary slices of an image, obtained by the threshold decomposition, are processed by the impulse-detecting Boolean functions proposed in the paper. These functions provide a possibility of single-pass filtering, because they detect and replace impulses at the same time. [ABSTRACT FROM AUTHOR]

This paper proposes a two-stage iterative method for removing random-valued impulse noise. In the first phase, we use the adaptive center-weighted median filter to identify pixels which are likely to be corrupted by noise (noise candidates). In the second phase, these noise candidates are restored using a detail-preserving regularization method which allows edges and noise-free pixels to be preserved. These two phases are applied alternatively. Simulation results indicate that the proposed method is significantly better than those using just nonlinear filters or regularization only. [ABSTRACT FROM AUTHOR]

This correspondence shows that some statements from the letter in the title, which are given without any theoretical analysis, are in conflict with the literature on active noise control systems. This correspondence also shows that the traditional, broadband ANC systems deal well with a mixture of broadband and narrowband noise. [ABSTRACT FROM AUTHOR]

Unknown Noise-MUSIC (UN-MUSIC) is a promising method of direction of arrival (DOA) estimation in unknown spatially correlated noise using sparse arrays composed of two widely separated sub-arrays. The conventional UN-MUSIC estimator only utilizes information from one calibrated sub-array. If two sub-arrays are calibrated, a joint estimator that equally weights two sub-arrays' conventional estimators has been found in literature. But no theoretical study has been reported. To compare and improve performance of different UN-MUSIC estimators, this paper proposes a unified framework of sub-array weighting to investigate the UN-MUSIC estimators, including the conventional one and the joint ones. The closed-form expression of the sub-array weighting estimator's variance is derived which has not been done before. With the asymptotic results, different weighting strategies are compared and optimal weighting strategy to minimize estimation variance is proposed. Numerical simulations demonstrate the theoretical analysis and the validity of optimal weighting estimator. [ABSTRACT FROM AUTHOR]

Global motion estimation (GME) from motion vector (MV) field in compressed domain greatly reduces the complexity of conventional pixel-based GME. However, outlier MVs, caused by noise or foreground objects, may reduce the accuracy of MV-based GME. In this paper, we propose a cascade-of-rejectors approach for removing MV outliers to achieve efficient and accurate GME. Experimental results show that the proposed MV outlier rejection cascade significantly lowers the complexity MV-based GME, with an accuracy close to or better than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper presents a method of scheduling stepsizes for the normalized least-mean-squares (SS-NLMS) algorithm. Geometrically interpreting the mean square deviation (MSD) learning curve leads to establishing an objective curve and to constructing a lookup table of stepsizes in order for the MSD to follow the curve. The SS-NLMS shows not only good performance but also robustness with respect to different signal-to-noise ratio (SNR) in measurement noise and different correlation in input signals with a very small number of online computations. Moreover, the scalability of the tabled stepsize with respect to the number of taps is described. For the efficient memory usage in practice, a modified version replaces the tabled stepsizes by down-sampled stepsizes with no performance degradation. [ABSTRACT FROM AUTHOR]

In this paper, we present a polynomial-based noise variance estimator for multiple-Input multiple-output single-carrier block transmission (MIMO-SCBT) systems. It is shown that the optimal pilots for noise variance estimation satisfy the same condition as that for channel estimation. Theoretical analysis indicates that the proposed estimator is statistically more efficient than the conventional sum of squared residuals (SSR) based estimator. Furthermore, we obtain an efficient implementation of the estimator by exploiting its special structure. Numerical results confirm our theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper studies the performance of the frequency maximum likelihood (ML) estimator of a single tone in Gaussian noise. Two mean-square error (MSE) approximations are first applied and then compared in the case of a known phase offset. The first is proposed by Van Trees [1] and corresponds to the method of interval errors (MIE). The second MSE approximation is proposed by Rife and Boorstyn in [4]. The problem is formulated in the data-aided frequency synchronization frame. [ABSTRACT FROM AUTHOR]

Traditional multiwavelet shrinkage denoising techniques require a priori knowledge of noise variance that may not be obtained in some practical situations. By using generalized cross validation (GCV), we propose in this paper a new level-dependent risk estimator for multiwavelet shrinkage that does not require such a priori information. Simulation results verify that the resulted risk estimator gives better indication on threshold selection comparing with the traditional GCV method. Improved denoising performance is then achieved particularly for higher multiplicity multiwavelet shrinkage. [ABSTRACT FROM AUTHOR]

This letter considers the problem of recovering a positive stream of Diracs on a sphere from its projection onto the space of low-degree spherical harmonics, namely, from its low-resolution version. We suggest recovering the Diracs via a tractable convex optimization problem. The resulting recovery error is proportional to the noise level and depends on the density of the Diracs. We validate the theory by numerical experiments. [ABSTRACT FROM PUBLISHER]

A time-multiplexed double strobe scheme for unambiguous tracking of time-multiplexed binary offset carrier (TMBOC) modulated signal in GPS is presented. In the code tracking loop, two kinds of time-multiplexed strobe pulses are generated to track the BOC(1,1) and BOC(6,1) components, respectively. The strobe based on BOC(1,1) subcarrier eliminates the false lock points in the code discriminator, and the bump-jumping algorithm is not required. The other strobe employs the BOC(6,1) component to prevent additional signal-to-noise ratio loss. Thus, the proposed method simplifies the hardware structure in comparison with conventional strobe techniques, and improves the tracking performance in the presence of thermal noise and multipath. [ABSTRACT FROM PUBLISHER]

In this letter, we propose a global method for registering color images with respect to translation. Our approach is based on the idea of representing translations as convolutions with unknown shifted delta functions, and performing Wiener deconvolution in order to recover the shift between two images. We then derive a quaternionic version of the Wiener deconvolution filter in order to register color images. The use of Wiener filter also allows us to explicitly take into account the effect of noise. We prove that the well-known algorithm of phase correlation is a special case of our method, and we experimentally demonstrate the advantages of our approach by comparing it to other known generalizations of the phase correlation algorithm. [ABSTRACT FROM AUTHOR]

The letter considers a multi-sensor state estimation problem configured in a decentralized architecture where local complex statistics are communicated to the central processing unit for fusion instead of the raw observations. Naive adaptation of the augmented complex statistics to develop a decentralized state estimation algorithm results in increased local computations, and introduces extensive communication overhead, making it practically unattractive. The letter proposes a structure-induced complex Kalman filter framework with reduced communication overhead. In order to further reduce the local computations, the letter proposes a non-circularity criterion which allows each node to examine the non-circularity of its local observations. A local sensor node disregards its extra second-order statistical information when the non-circularity coefficient is small. In cases where the local observations are highly non-circular, an intuitively pleasing circularization approach is proposed to avoid computation and communication of the pseudo-covariance matrices. Simulation results indicate that the proposed structured-induced complex Kalman filter (SCKF) provides significant performance improvements over its traditional counterparts. [ABSTRACT FROM AUTHOR]

We consider the problem of sparse signal recovery from one-bit measurements. Due to the noise present in the acquisition and transmission process, some quantized bits may be flipped to their opposite states. These bit-flip errors, also referred to as the sign-flip errors, may result in severe performance degradation. To address this issue, we introduce a robust Bayesian compressed sensing framework to account for sign flip errors. Specifically, sign-flip errors are considered as a result of a sparse noisecorrupted model in which original (unquantized) observations are corrupted by sparse (impulse) noise. A Gaussian-inverse Gamma hierarchical prior is assigned to the noise vector to promote sparsity. Based on the modified hierarchical model, we develop a variational expectation-maximization (EM) algorithm to identify the sign-flip errors and recover the sparse signal simultaneously. Numerical results are provided to illustrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

An unscented Kalman filter-based constant modulus adaptation algorithm (UKF-CMA) is proposed for blind uniform linear beamforming. The proposed algorithm is obtained by first developing a model of the constant modulus (CM) criterion and then fitting that model into the Kalman filter-style state space model by using an auxiliary parameter. The proposed algorithm does not require a priori information about the process noise and measurement noise covariance matrices and hence it can be applied readily. Simulation results demonstrate that the proposed algorithm offers improved performance compared to the recursive least square-based CM (RLS-CMA) and least-mean square-based CM (LMS-CMA) algorithms for adaptive blind beamforming. [ABSTRACT FROM AUTHOR]

The generalized likelihood ratio test (GLRT) is a very widely used technique for detecting signals of interest amongst noise, when some of the parameters describing the signal (and possibly the noise) are unknown. The threshold of such a test is set from a desired probability of false alarm Pfa and hence this threshold depends on the statistical assumptions made about noise. In practice however, the noise statistics are seldom known and it becomes crucial to characterize Pfa under a mismatched distribution. In this letter, we address this problem in the case of a simple binary composite hypothesis testing problem (matched filter) when the threshold is designed under a Gaussian assumption while the noise actually follows an elliptically contoured distribution. We also consider the inverse situation. Generic expressions for the assumed and actual Pfa are derived and illustrated on the particular case of Student distributions for which simple, closed-form expressions are obtained. The latter show that the GLRT based on Gaussian assumption is not robust while that based on Student assumption is. [ABSTRACT FROM AUTHOR]

The problem of adaptive detection of a signal of interest embedded in elliptically distributed noise with unknown scatter matrix R is addressed, in the specific case where the number of training samples T is less than the dimension M of the observations. In this under-sampled scenario, whenever R is treated as an arbitrary positive definite Hermitian matrix, one cannot resort directly to the generalized likelihood ratio test (GLRT) since the maximum likelihood estimate (MLE) of R is not well-defined, the likelihood function being unbounded. Indeed, inference of R can only be made in the subspace spanned by the observations. In this letter, we present a modification of the GLRT which takes into account the specific features of under-sampled scenarios. We come up with a test statistic that, surprisingly enough, coincides with a subspace detector of Scharf and Friedlander: the detector proceeds in the subspace orthogonal to the training samples and then compares the energy along the signal of interest to the total energy. Moreover, this detector does not depend on the density generator of the noise elliptical distribution. Numerical simulations illustrate the performance of the test and compare it with schemes based on regularized estimates of R. [ABSTRACT FROM AUTHOR]

We propose the use of a separable coded aperture to estimate depth from a single defocused image, and derive a criterion for evaluating aperture patterns with respect to depth discrimination. With a separable coded aperture, two-dimensional (2D) point spread functions (PSFs) are separated into the product of horizontal and vertical one-dimensional (1D) PSFs. Depth is recovered by finding the scale of the 1D PSFs at each pixels based on the space-frequency analysis. Benefiting from 1D PSFs and the optimal code, our approach obtains more accurate depth especially on depth boundaries, and is faster since 2D computation is reduced to 1D. Experimental results on synthetic and real-captured data demonstrates the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Minimum-variance filters and smoothers exhibit performance degradations when they are designed with inexact models and noise statistics. Filter and smoother estimation errors are assumed herein to be generated by a first-order moving-average system. This assumed system is identified and used to design a frequency weighting function to improve mean square error performance. It is shown under prescribed conditions that the sequence of frequency-weighted estimation error variances are nonincreasing. An example is presented which demonstrates the efficacy of repeated frequency weighting iterations. [ABSTRACT FROM AUTHOR]

In this letter, we propose control theoretic smoothing splines with L1 optimality for reducing the number of parameters that describes the fitted curve as well as removing outlier data. A control theoretic spline is a smoothing spline that is generated as an output of a given linear dynamical system. Conventional design requires exactly the same number of base functions as given data, and the result is not robust against outliers. To solve these problems, we propose to use L1 optimality, that is, we use the L1 norm for the regularization term and/or the empirical risk term. The optimization is described by a convex optimization, which can be efficiently solved via a numerical optimization software. A numerical example shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Denoising methods require some assumptions about the signal of interest and the noise. While most denoising procedures require some knowledge about the noise level, which may be unknown in practice, here we assume that the signal expansion in a given dictionary has a distribution that is more heavy-tailed than the noise. We show how this hypothesis leads to a stopping criterion for greedy pursuit algorithms which is independent from the noise level. Inspired by the success of ensemble methods in machine learning, we propose a strategy to reduce the variance of greedy estimates by averaging pursuits obtained from randomly subsampled dictionaries. We call this denoising procedure Blind Random Pursuit Denoising (BIRD). We offer a generalization to multidimensional signals, with a structured sparse model (S-BIRD). The relevance of this approach is demonstrated on synthetic and experimental MEG signals where, without any parameter tuning, BIRD outperforms state-of-the-art algorithms even when they are informed by the noise level. Code is available to reproduce all experiments. [ABSTRACT FROM AUTHOR]

In the context of compressed sensing, the nonconvex ℓq minimization with 0 < q < 1 has been studied in recent years. In this letter, by generalizing the sharp bound for ℓ1 minimization of Cai and Zhang, we show that the condition δ(sq+1)k < 1/√(sq-2+1) in terms of restricted isometry constant (RIC) can guarantee the exact recovery of k-sparse signals in the noiseless case and the stable recovery of approximately k-sparse signals in the noisy case by ℓq minimization. This result is more general than the sharp bound for ℓ1 minimization when the order of RIC is greater than 2k and illustrates the fact that a better approximation to ℓ0 minimization is provided by ℓq minimization than that provided by ℓ1 minimization. [ABSTRACT FROM PUBLISHER]