Search

Abstract: This paper considers a scenario in which signals from an emitter at an unknown location are received at a number of different collinear locations. The receiver can determine the received signal strength, but no other parameters of the signal. Postulating a log-normal transmission model with a constant but unknown path loss exponent and, also, an unknown transmit power and known noise variance, the paper shows how the localization problem can be solved, along with estimating the parameters appearing in the log-normal transmission model, given enough measurements at different points. The log-normal transmission model parameters can be determined first. An algorithm based on construction of a Gram matrix is proposed to estimate the path loss exponent and transmit power parameters from the received noisy power measurements. Since the estimated parameters are biased due to the nonlinearity of the model and constraints, a pattern-matching algorithm is also proposed to remove the bias in the estimates. The distances corresponding to the different received signal strength measurements can then be bounded, and finally the location estimation is formulated as a convex optimization problem where the estimated distances are used as the new measurements. Simulation results are finally provided to assess the efficacy of the proposed methods in the parameter and location estimation. [Copyright &y& Elsevier]

Highlights • Approximately achieve tradeoff between capacity and Jain's fairness that cannot increase fairness while not decrease capacity. • Analog beamforming and user grouping are designed to suppress interference. • Complexity is comparable to most of the existing scheduling approaches. Abstract This paper investigates user scheduling in millimeter-wave multiple-input multiple-output systems that can achieve a tradeoff between capacity and Jain's fairness index. By decomposing the original problem into digital beamforming design, duration allocation, and analog beamforming design, each subproblem is solvable. It is proved that the duration allocation problem possesses a property such that the convex optimization algorithm can be employed for the near optimal result. Moreover, the analog beamforming vectors and the user grouping are designed to avoid interference caused by the reuse of the same subspace. Finally, the computational complexity of the proposed approach is analyzed and is compared with that of other approaches. The simulations verify that the proposed approach can achieve the highest fairness and a higher capacity than approaches of close fairness. [ABSTRACT FROM AUTHOR]

Abstract: For two-dimensional (2-D) directions-of-arrival (DOA) estimation problem, both the mutual coupling and the failure in pairing can cause severe performance degradation. In this paper, a new elevation and azimuth direction finding algorithm is developed to overcome the above-mentioned two difficulties in the L-shaped array configuration. The key points of this paper are: (i) constructing several correlation matrices to blindly compensate the effect of unknown mutual coupling using the outputs of properly chosen sensors and (ii) deriving a rank-reduction propagator method to estimate elevation and azimuth angles so as to avoid pairing parameters. Simulation results are presented to validate the performance of the proposed method. [Copyright &y& Elsevier]

Highlights • We propose an improved diffusion total least-squares (DTLS) algorithm, where the estimated value at each node is passed through a first order recursive filter with adjustable parameter in order to enhance the identification performance. • We develop robust adaptive strategies to update various internal system parameters of the proposed DTLS algorithm. • The convergence behavior of the adaptive weight vector and related system parameters of the new algorithm are analyzed by means of Lyapunov stability theory. • Simulation results in the context of distributed system identification demonstrate the superiority of the proposed DTLS algorithm. Abstract The errors-in-variables (EIV) system model has been widely studied under the assumption that both input and output signals are contaminated with noise. For the in-network distributed linear system identification problem under the EIV model, the total least-squares (TLS) approach which has the ability to minimize perturbations in both input and output signals offers an efficient solution. In this paper, we propose an improved diffusion total least-squares algorithm, where the estimated (i.e. filtered) value at each node is passed through a first order recursive filter with adjustable parameter in order to enhance the identification performance. The resulting outputs from all the nodes are subsequently used to adapt the unknown linear system weight vector in real-time through a cooperative diffusion scheme based on the adapt-then-combine (ATC) policy. We also present robust adaptive strategies to tune various internal system parameters, such as the steps sizes, normalization factors, etc., under practical conditions of operation. The convergence behavior of the adaptive weight vector and related system parameters is analyzed by employing Lyapunov stability theory. Simulation results for various distributed system identification scenarios demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Recently, block-sparse signals, whose nonzero coefficients appearing in blocks, have received much attention. A corresponding block-based orthogonal greedy algorithm (OGA) was proved by Eldar to successfully recover ideal noiseless block-sparse signals under a certain condition on block-coherence. In this paper, the stability problem of block OGA used to recover the noisy block-sparse signals is studied and the corresponding approximation bounds are derived. The theoretical bounds presented in this paper are more general and are proven to include those reported by Donoho and Tseng. Numerical experimental results are presented to support the validity and correctness of theoretical derivation. The simulation results also show that in the noisy case, the block OGA can be proved to achieve better reconstruction performance than the OGA when the conventional sparse signals are represented in block-sparse forms. [Copyright &y& Elsevier]

Abstract: Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become very popular in signal processing over the last years. However, in many problems of practical interest these techniques demand procedures for sampling from probability distributions with non-standard forms, hence we are often brought back to the consideration of fundamental simulation algorithms, such as rejection sampling (RS). Unfortunately, the use of RS techniques demands the calculation of tight upper bounds for the ratio of the target probability density function (pdf) over the proposal density from which candidate samples are drawn. Except for the class of log-concave target pdf''s, for which an efficient algorithm exists, there are no general methods to analytically determine this bound, which has to be derived from scratch for each specific case. In this paper, we introduce new schemes for (a) obtaining upper bounds for likelihood functions and (b) adaptively computing proposal densities that approximate the target pdf closely. The former class of methods provides the tools to easily sample from a posteriori probability distributions (that appear very often in signal processing problems) by drawing candidates from the prior distribution. However, they are even more useful when they are exploited to derive the generalized adaptive RS (GARS) algorithm introduced in the second part of the paper. The proposed GARS method yields a sequence of proposal densities that converge towards the target pdf and enable a very efficient sampling of a broad class of probability distributions, possibly with multiple modes and non-standard forms. We provide some simple numerical examples to illustrate the use of the proposed techniques, including an example of target localization using range measurements, often encountered in sensor network applications. [Copyright &y& Elsevier]

Abstract: This paper proposes a processing method to extend monostatic imaging algorithms for the azimuth invariant bistatic SAR (BISAR) data where the transmitter and the receiver move on parallel tracks with equal velocities. The bistatic range history is approached by a polynomial of the azimuth time. Based on this model, an analytic 2-D signal spectrum derived by the method of series reversion is utilized and a simple one-to-one mapping between the transmitter and the receiver closest ranges is established. In this way, efficient monostatic imaging algorithms which operate in the 2-D frequency domain or the range-Doppler domain are easily modified to handle the BISAR data. In this paper, a chirp scaling algorithm for the BISAR is developed as an application of the new method. By implementing two key operations, range cell migration correction by a chirp scaling, and azimuth compression by a curve fitting technique, this algorithm is able to process the azimuth invariant BISAR data. Simulation results are presented to demonstrate the validity and the efficiency of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents a new and simple approach to analyzing the limiting behavior of the normalized LMS algorithm under weak assumptions. No restrictions are made on the dependence between successive regressors, the dependence among the regressor elements, the length of the adaptive filter, or the distribution types of the filter input and the noise. The analysis holds for all values of the algorithm step-size in the range between 0 and 2. The analysis is carried out using a new performance measure, based on the time evolution of the component of the weight deviation vector in the direction of the regressor. This component is termed as the effective weight deviation since it is the only component that contributes to the excess estimation error at the output of the adaptive filter. The paper derives upper bounds for the long-term averages of the mean-square effective weight deviation, mean absolute excess estimation error, and of the mean-square excess estimation error. The analytical results of the paper are supported by simulations. [Copyright &y& Elsevier]

Abstract: In this paper, a new high resolution direction-of-arrival (DOA) estimation technique is proposed for wideband signals in the presence of spatially correlated noise with unknown covariance matrix. The proposed technique is based on the (matrix) difference between the forward–backward averaged covariance matrix of the observations and the Hermitian of the backward covariance matrix of the observations. This differencing operation eliminates the noise covariance matrix from the difference matrix. The propagator method is then applied to the difference matrix to find the DOA. This proposed technique does not require any initial estimate of the DOAs and is effective for both correlated and partially correlated sources. In this paper, the proposed technique is applied to a uniform linear array and an L-shaped array for both one- and two-dimensional DOA estimation. Simulations results comparing the performance of the proposed method with that of the coherent propagator method are presented. [Copyright &y& Elsevier]

Work of Pezeshki, Calderbank and others [1, 2] has shown that choice of transmission order of Golay complementary waveforms in radar pulse trains can significantly improve Doppler resilience in detecting nonzero Doppler targets. For multiple targets a Weighted average Doppler (WD) algorithm [3] based on our earlier work has been shown to provide improvements. In this paper, we demonstrate that the Weighted Sidelobe Minimization procedure, developed in our recent work that combines the outputs of WD and the existing Binominal Design algorithms for ordering transmitted complementary waveform sequences using a point-wise minimization process (PMP), is also effective for the elimination of false target returns arising from sea clutter. A detailed analysis based on a Swerling II target model in terms of target detection probability is presented to validate the use of PMP. Our validation is strengthened by numerical simulations for both fixed and randomized scenarios. [ABSTRACT FROM AUTHOR]

• This paper summarizes several variable kernel width maximum correntropy criterion (MCC) algorithms, and discusses the basic principles of these algorithms. A close relationship between this algorithms and LMS algorithm is analyzed and established. • Then a new statistics variable kernel width MCC algorithm is proposed (SVKW-MCC) on the basis of previous variable kernel width algorithms. • SVKW-MCC algorithm is proposed to address the shortcomings of some well-known variable kernel width algorithm. The SVKW-MCC algorithm use statistics method to compute the kernel width and eliminates the abnormal errors caused by impulsive noise by statistical method. • The stability and steady-state mean-square performance of the proposed algorithm is analyzed and verified by experiments. Since the maximum correntropy criterion (MCC) algorithm with a constant kernel width leads to the trade-off problem between the convergence rate and steady-state misalignment, various adaptive kernel width MCC algorithms were derived to solve this problem. However, the superior performances of these algorithms depend mainly on specific data range, or have complicated calculation and parameter setting. Thus, this paper proposes a statistics variable kernel width MCC (SVKW-MCC) algorithm to overcome these problems. Specifically, the proposed algorithm calculates the mean and variances of the errors signal, and then the proposed algorithm removes these data that significantly deviate from the mean value of errors signal, moreover, the new mean and variance are recalculated after removing these abnormal data, subsequently, the new kernel width is calculated by the new variance and mean. Simulation results in system identification and echo cancellation scenarios show that the proposed algorithm outperforms the existing variable kernel width methods. Moreover, the stability and steady-state mean-square performance of the proposed algorithm is analyzed and verified by experiments. More importantly, the new method involves no extra free parameters and does not depend on the specific application data range, so the proposed algorithm has a very good application prospect. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of spectrum sensing in cognitive radios (CRs) by exploiting inherent polarization characteristics of signal. Since polarization vector can completely describe the radiation's polarization characteristics, we first derive the probability density function (PDF) of received polarization vector and its moments. Then we find the fact that both component and serial correlations of received polarization vector (i.e., the mixed polarization vector of primary signal and noise) are different from those of noise with high probability. This distinctive difference can be used to decide whether the primary signal exists or not. Therefore, component correlation sensing (CCS) algorithm and serial correlation sensing (SCS) algorithm are proposed respectively. Furthermore the closed-form expressions of probabilities of false alarm and detection are available for CCS and SCS algorithms. Simulations show that both CCS and SCS detectors achieve better performance with higher cross-polar discrimination (XPD) and polarization channel correlation coefficients. We also show that, if channel is highly depolarized, CCS performs better than SCS. Otherwise, the latter shows better performance. Compared with existing polarization based detectors, both CCS and SCS detectors perform better in the presence of noise power uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: The bin-normalized frequency-domain block LMS (FBLMS) algorithm has low computational burden and potential fast convergence; however, it suffers from a biased steady-state solution when the reference signal lags behind the desired signal or the adaptive filter is of insufficient length. This paper proposes a unified framework for the FBLMS algorithm, which can be used to comprehensively analyze its steady-state behavior. Furthermore, a modified FBLMS algorithm with guaranteed optimal steady-state performance is proposed based on the framework. Simulations are carried out to demonstrate the benefit of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents a stochastic model for the individual-activation-factor proportionate normalized least-mean-square (IAF-PNLMS) adaptive algorithm operating under correlated Gaussian input data. The proposed approach uses the contragredient transformation to obtain an analytical solution for the normalized autocorrelation-like matrices arising from the model development. Model expressions describing the learning curve and the second-order moment of the weight-error vector for the IAF-PNLMS algorithm are derived taking into account the time-varying characteristic of the gain distribution matrix. As a consequence, the obtained model predicts very well the algorithm behavior for both transient and steady-state phases. Through simulation results, considering different operating scenarios, the accuracy of the proposed model is attested (via learning curve) for both complex- and real-valued input data. [Copyright &y& Elsevier]

Abstract: Direction-of-arrival (DOA) estimation is a central problem in array processing and has a variety of applications. In this paper, a new algorithm for finding DOAs of multiple temporally correlated signals is devised. The proposed approach is based on the joint diagonalization structure of a set of spatio-temporal correlation matrices. Unlike the subspace-based DOA estimators, it is not necessary to estimate the noise or signal subspace explicitly. Moreover, the proposed method can provide the spatial spectrum and estimate the DOAs even when the number of sources is not known a priori. Interestingly, it is revealed that the well-known MUSIC method is a special case of our algorithm. Simulation results validate that the developed approach is superior to conventional DOA estimators in terms of resolution capability, estimation accuracy, and robustness against array model errors. [Copyright &y& Elsevier]

Abstract: This paper presents a parametric approach to classify the radiation pattern of an acoustic source given the signals captured by multiple microphones. The radiation pattern influences the way the acoustic waves propagate within an enclosure, with direct implications on the behavior of most audio processing algorithms. In particular, the Generalized Cross-Correlation PHAse Transform is affected by the emission pattern as well as by the orientation of the source. A Maximum Likelihood estimator is introduced by using descriptors of the acoustic characteristics of the environment, e.g. wall absorption coefficients and room dimensions, from which models of the observed Generalized Cross-Correlation PHAse Transform are derived for a specific emission pattern. A generic unimodal source directivity is modeled using a parameterized cardioid function. A sub-band implementation is proposed to account for the frequency dependence of the source emission pattern. Experiments on simulated and real data show that the acoustic radiation pattern can be estimated in an effective way under noisy and reverberant conditions. [Copyright &y& Elsevier]

Abstract: This paper addresses pattern classification in dynamical systems, where the underlying algorithms are formulated in the symbolic domain and the patterns are constructed from symbol strings as probabilistic finite state automata (PFSA) with (possibly) diverse algebraic structures. A combination of Dirichlet and multinomial distributions is used to model the uncertainties due to the (finite-length) string approximation of symbol sequences in both training and testing phases of pattern classification. The classifier algorithm follows the structure of a Bayes model and has been validated on a simulation test bed. The results of numerical simulation are presented for several examples. [Copyright &y& Elsevier]

Abstract: A computationally efficient propagator and two-stage separated virtual steering vector based algorithm (SVSV) is recently developed by D. Liu and J. Liang for two-dimensional (2D) frequency estimation. SVSV has introduced a novel electric angle z to jointly estimate the 2D components (frequencies or scatterers'' locations) and to avoid the incorrect mismatch. However, SVSV cannot resolve the multiple 2D components having equivalent value of z. This problem limits the use of SVSV in ISAR imaging application. Hence, in this paper, we have proposed a new modified SVSV method called MSVSV for 2D ISAR imaging. Main contributions of our work are: (a) use of the propagator based subspace methods (SVSV and MSVSV) for 2D high resolution ISAR imaging. (b) We have suggested a technique to estimate the number of unknown 2D components prior to calculating the propagator operator from the covariance matrix. (c) We have incorporated a technique, due to which MSVSV can successfully resolve the multiple scatterers sharing the same electric angles, where SVSV fails. Radar images are generated for simulated and experimental target data (collected at AERO, Pakistan) by using SVSV, MSVSV and conventional Fourier Transform methods. [Copyright &y& Elsevier]

Abstract: In this paper, we present an evaluation of the use of split Bregman iterative algorithm for the L 1-norm regularized inverse problem of electrical impedance tomography. Simulations are performed to validate that our algorithm is competitive in terms of the imaging quality and computational speed in comparison with several state-of-the-art algorithms. Results also indicate that in contrast to the conventional -norm regularization method and total variation (TV) regularization method, the L 1-norm regularization method can sharpen the edges and is more robust against data noises. [Copyright &y& Elsevier]

Abstract: In this paper, new signal-selective methods for the estimation of time-difference-of-arrival in the presence of interfering signals and non-Gaussian symmetric α-stable impulsive noise are introduced. First, the performance degradation of the conventional approaches based on second-order cyclic statistics is presented. Then, two new classes of robust algorithms are developed using the theory of stable distributions and the cyclostationary property, including the pth-order cyclostationarity methods and the fractional lower-order cyclostationarity methods. It is shown that these new methods are tolerant to interference and robust in both Gaussian and non-Gaussian impulsive noise environments. The improved performance is demonstrated through detailed theoretical analysis and simulations. [Copyright &y& Elsevier]

Abstract: Variable order affine projection algorithms have been recently presented to be used when not only the convergence speed of the algorithm has to be adjusted but also its computational cost and its final residual error. These kind of affine projection (AP) algorithms improve the standard AP algorithm performance at steady state by reducing the residual mean square error. Furthermore these algorithms optimize computational cost by dynamically adjusting their projection order to convergence speed requirements. The main cost of the standard AP algorithm is due to the matrix inversion that appears in the coefficient update equation. Most efforts to decrease the computational cost of these algorithms have focused on the optimization of this matrix inversion. This paper deals with optimization of the computational cost of variable order AP algorithms by recursive calculation of the inverse signal matrix. Thus, a fast exact variable order AP algorithm is proposed. Exact iterative expressions to calculate the inverse matrix when the algorithm projection order either increases or decreases are incorporated into a variable order AP algorithm leading to a reduced complexity implementation. The simulation results show the proposed algorithm performs similarly to the variable order AP algorithms and it has a lower computational complexity. [Copyright &y& Elsevier]

Abstract: Due to the limited sensing range of sensors, moving target tracking has to be realized by relaying from one sensor to the other in wireless sensor networks. Therefore, the tracking procedure can be modelled as a Markov chain system. By reconstructing the innovation equation, the relaying Kalman filter (RKF) algorithm is designed in light of Bayesian theory. To deal with nonlinear cases, the interacting multiple sensor filter (IMSF) is proposed in this paper by using the unscented Kalman filter (UKF), the extended Kalman filter (EKF) or the particle filter (PF). Then, the posterior Cramér–Rao lower bound (PCRLB) is derived for multisensor collaborative tracking. Finally, simulation results show the effectiveness of the proposed IMSF algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents and investigates a fast adaptive null steering system employing infinite impulse response (IIR) array processing. The new algorithm is of considerable interest in communications where the desired signal is weak compared with the interfering signals or where the desired signal can be easily separated out. With the proposed algorithm, the nulls of the array system are repetitively updated one by one in a cyclical manner through a sequence of adjustment cycles. In each adjustment cycle, a particular null is updated by using the least mean square (LMS) algorithm and the update of each particular null will not affect other null positions. The proposed method is very effective and useful in the sense that it will result in a nearly flat gain in the antenna pattern, except zero gains at the null directions. Moreover, as will be demonstrated later from both analysis and simulation results, the convergence behavior of the new algorithm is significantly faster than the linearly constrained minimum variance (LCMV) method and is almost independent of the external noise environment. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a strategy to combine fast discrete curvelet transform (FDCT) and wave atom (WA) with multiscale variance stabilizing transform (MS-VST); our objective is to develop algorithms for Poisson noise removal from images. Applying variance stabilizing transform (VST) on a Poisson noisy image results in a nearly Gaussian distributed image. The noise removal can be subsequently done assuming a Gaussian noise model. MS-VST has been recently proposed in the literature (i) to improve the denoising performance of Anscombe''s VST at low intensity regions of the image and (ii) to facilitate the use of multiscale-multidirectional transforms like the curvelet transform for Poisson image denoising. Since the MS-VST has been implemented in the space-domain, it is not clear how it can be extended to FDCT and WA, which are incidentally implemented in the frequency-domain. We propose a simple strategy to achieve this without increasing the computational complexity. We also extend our approach to handle the recently developed mirror-extended versions of FDCT and WA. We have carried out simulations to validate the performance of the proposed approach. The results demonstrate that the MS-VST combined with FDCT and WA are promising candidates for Poisson denoising. [Copyright &y& Elsevier]

Abstract: Sparse linear arrays provide better performance than the filled linear arrays in terms of direction estimation and resolution with reduced size and low cost. However, they are subject to manifold ambiguity. A method based on the Multiple Signal Classification (MUSIC) algorithm to solve the manifold ambiguity of uncorrelated sources for sparse array is proposed in this paper. The method consists of two steps. The first step is to obtain all the directions of arrivals (DOAs), including true and spurious DOAs, using traditional MUSIC. The second step is to estimate the power values of the all DOAs by substituting all the DOAs to a cost function. The well-known Davidson Fletcher Powell (DFP) and Broyden Fletcher Goldfarb Shanno (BFGS) algorithms are used to estimate the power values. The power values of spurious DOAs are very small or tend to zero compared with the values of the true DOAs. The true DOAs are then discriminated easily from the spurious DOAs with the power values. Simulation results demonstrate the effectiveness and the feasibility of the method. [Copyright &y& Elsevier]

Abstract: Parameter estimation of noisy damped sinusoidal signals in the frequency domain is presented in this paper. The advantage of the frequency domain approach is having the spectral energy concentrated in frequency domain samples. However, the least squares criterion for frequency estimation using frequency domain samples is nonlinear. A low complexity three-sample estimation algorithm (TSEA) for solving the nonlinear problem is proposed. Using the TSEA for initialization, a frequency domain nonlinear least squares (FD-NLS) estimation algorithm is then proposed. In the case of white Gaussian noise, it yields maximum likelihood estimates, verified by simulation results. A time domain NLS (TD-NLS) estimation algorithm is also proposed for comparison. The Cramer–Rao lower bound (CRLB) of the frequency domain estimation algorithms is derived. The theoretical analysis shows that the FD-NLS can yield a near-optimal performance with few energy-concentrated samples. On the other hand, the TD-NLS does not have the energy concentration property and requires more time domain samples to perform satisfactory estimation. Simulation results verify that the frequency domain estimation algorithms provide better tradeoff between computational complexity and estimation accuracy than time domain algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, we show that the joint blind source separation (JBSS) problem can be solved by jointly diagonalizing cumulant matrices of any order higher than one, including the correlation matrices and the fourth-order cumulant matrices. We introduce an efficient iterative generalized joint diagonalization algorithm such that a series of orthogonal procrustes problems are solved. We present simulation results to show that the new algorithms can reliably solve the permutation ambiguity in JBSS and that they offer superior performance compared with existing multiset canonical correlation analysis (MCCA) and independent vector analysis (IVA) approaches. Experiment on real-world data for separation of fetal heartbeat in electrocardiogram (ECG) data demonstrates a new application of JBSS, and the success of the new algorithms for a real-world problem. [Copyright &y& Elsevier]

Abstract: Orthogonal frequency division multiplexing (OFDM) is sensitive to carrier frequency offset (CFO) that causes inter-carrier interference (ICI). In this paper, a particle filter (PF) method augmented with ant colony optimization (ACO) is developed to estimate the CFO. The ACO for continuous domains is incorporated into PF to optimize the sampling process. Unlike the standard PF, resampling is not required in the method. Moreover, it does not require the noise distribution. Simulation results show that the proposed method is effective when estimating the CFO and can effectively combat the effect of ICI in OFDM systems. [Copyright &y& Elsevier]

Abstract: Heat transfer problems in homogeneous medium obey to diffusion phenomenon. In this paper, we study heat conduction through a sphere which can be modelled with the help of fractional systems. We use models based on an original fractional integrator whose order acts only over a limited spectral band. Simulations of front face thermal characterization consisting in measuring the inner surface temperature of the sphere where a random heat flux is applied are carried out. These simulations allow to validate the ability of the proposed estimation algorithms not only to rebuild the inner surface temperature but also to identify with a good accuracy the heat conductivity and diffusivity. Monte-Carlo simulations are also performed in order to provide statistical properties of the thermo-physical parameter estimates. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents improvements on the fractional Kalman filter (FKF) based on the infinite dimensional form of a linear discrete fractional order state-space system. Furthermore, taking into account the considerable interest in estimation over networks with packet losses, the application and extension of the improved FKF are included. Some simulation cases are given in order to demonstrate the effectiveness of the proposed algorithms, with significant improvements in terms of estimation and smoothing results. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address the problem of interference cancellation in global positioning system (GPS) receiver using a two-step approach: subspace projection technique and maximum signal-to-noise ratio (MSNR) beamforming. The interference signals can be effectively suppressed by projecting the received signal on the noise subspace. Here noise subspace tracking algorithm is employed to estimate the noise subspace directly. We then apply a beamformer to maximize the signal-to-noise ratio of the interference-free signal. Simulation results show that our approach can effectively eliminate the strong interference and enhance the performance of the GPS receiver. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a multistatic radar system with n transmitters and one receiver is considered and several constant false alarm rate (CFAR) algorithms for detection are introduced. The decision statistics of the proposed detectors are the sum of the n largest returning signals in an array of N+n range cells. It is shown that the proposed decision statistic satisfies the CFAR condition and it is justified that the sum of the largest returning signals is the optimal statistic. The proposed CFAR detectors are simulated both in homogenous and non-homogenous backgrounds and their performances are compared with the performance of a monostatic radar of higher power. It is shown that a multistatic radar outperforms a monostatic radar under equal transmit power condition. [Copyright &y& Elsevier]

Abstract: In this paper, we present an improved algorithm for wideband direction finding of far-field sources impinging on an array with a known arbitrary geometry. This algorithm is based on the test of orthogonality of projected subspaces (TOPS), so that it can be considered as an extension of TOPS algorithm (ETOPS). Due to using more information from several reference frequencies, the performance of the proposed method outperforms that of TOPS with arbitrarily chosen reference frequency, and varies with the number of the reference frequencies used. Although ETOPS needs more computation loads, each TOPS spectrum can be calculated individually. So, it is possible to obtain them at the same time in practice, which makes the proposed algorithm parallelizable easily and further speeds up the processing in practical implementation. Simulation results validate the effectiveness of our method. [Copyright &y& Elsevier]

Abstract: In this paper, the recent bi-iterative second-order cone program (BI-SOCP) to design one-dimensional (1D) DFT modulated filter banks is extended to the 2D case to design two-dimensional (2D) linear phase nonseparable DFT modulated filter banks. Some essential properties of 2D DFT modulated filter banks are presented and a permissibility of the configuration of filter banks is given. The design problem is formulated into a non-convex optimization, which minimizes the maximal amplitude of the transfer function distortion and aliasing transfer functions of the filter bank subject to predefined bounds on the stopband energy, the transition-band energy, and a passband flatness measure of the prototype filters (PFs). The optimization problem is solved by the BI-SOCP algorithm, where 2D linear phase analysis and synthesis PFs are alternately optimized via second-order cone programs (SOCPs). Numerical examples are included to show that the BI-SOCP can yield 2D linear phase nonseparable DFT modulated filter banks with good overall performance. [Copyright &y& Elsevier]

Abstract: In system identification when a secondary path follows the adaptive filter, the FxLMS algorithm is usually applied for updating the adaptive filter. Although several FxLMS convergence analyses have been conducted in detail, only a few have intended to derive a convergence condition. In fact, available FxLMS convergence conditions are only accurate for simplified cases with pure delay secondary paths or multi-sinusoidal input signals. This paper studies the FxLMS convergence behavior for moving average secondary paths and stochastic input signals. A novel model for predicting the FxLMS convergence behavior is developed. Based on this model, a necessary and sufficient condition for the convergence of the FxLMS is derived. Also, the condition leading to the fastest convergence is proposed. Compared to previously derived convergence conditions, the proposed condition applies to more general secondary paths. Results obtained from this study are found to correspond very well to those obtained from simulation experiments. [Copyright &y& Elsevier]

Abstract: Blind noise variance and SNR estimators based on information theoretic criteria for orthogonal frequency division multiplexing (OFDM) systems are proposed in this paper. By using the redundancy induced by the cyclic-prefix, the unknown channel order is estimated via minimum length description (MDL) or Akaike information criterion (AIC), and the noise variance and SNR can be obtained subsequently. The proposed methods do not require any pilot symbols and therefore improve the spectrum efficiency. Simulation results are presented to highlight the benefit of the proposed algorithms compared with the other existing blind methods. [Copyright &y& Elsevier]

Abstract: Based on self-adaptive wave transmission, a novel image encryption algorithm is given in this paper. The advantages of our proposed approach are that it can be realized easily and is computationally simple while achieving high security level, high speed, high sensitivity and other properties simultaneously. Wave transmission encryption, as the name suggests, is a way to change the gray-level value of pixels by simulating waves’ transmission. The self-adaptive encryption is carried out by using one half of image data to encrypt the other half of the image mutually. Our algorithm can encrypt image in parallel and be also applied to color image encryption. Simulation results for a gray-level and a color image have demonstrated the high performance on the sensitivity, speed, and security of the proposed algorithm. Even in the first round the change rate of the number of pixels in the cipher-image when only one pixel of the plain-image is modified (NPCR) and the unified average changing intensity (UACI) are already very high (NPCR>0.995, UACI>0.334), only 2 rounds encryption can satisfy the performance and security requirement. Besides, our algorithm is faster than state of the art techniques, as far as parallel encryption is concerned, the time consumption will be much less. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new technique to transform the 2-D direction finding in the bistatic MIMO radar into a double 1-D direction finding procedure. Firstly, a search based 2-D MUSIC method to estimate the joint DOA (direction of arrival) and DOD (direction of departure) in multi-target situation is presented. Then, we propose an algorithm based on double polynomial root finding procedure to estimate the DOA and DOD. The proposed method allows an efficient estimation of the target DOA and DOD with automatic pairing. The simulation results of the proposed algorithm are presented and the performances are investigated and discussed. [Copyright &y& Elsevier]

Abstract: The Constant Modulus Algorithm (CMA) is very popular as far as the extraction of a source from a convolutive mixture is concerned. Though originally designed for stationary data, we have noticed in simulations the ability of the CMA to achieve the separation even in non-stationary contexts such as mixtures of linearly modulated communication signals having arbitrary symbol periods. In this paper, we address the theoretical aspect of this point. We provide sufficient conditions on the statistics of the sources under which the use of the CMA is theoretically justified. This condition is semi-analytically worked out in different scenarios of digital communications. [Copyright &y& Elsevier]

Abstract: Recently distributed adaptive estimation algorithms have been proposed as a solution to the issue of linear estimation over distributed networks. In all previous works, the performance of such algorithms is considered only for infinite-precision arithmetic implementation. In this paper we study the performance of distributed incremental least mean square (DILMS) estimation algorithm when it is implemented in finite-precision arithmetic. To this aim, we first derive the quantized version of the DILMS algorithm. Then a spatial–temporal energy conservation argument is used to derive theoretical expressions that evaluate the steady-state performance of individual nodes in the network. Simulation results show that there is a good match between the theory and simulation. [Copyright &y& Elsevier]

Abstract: Follower partial-band jamming is an efficient strategy to degrade the performance of frequency hopping (FH) systems with M-ary frequency shift keying (MFSK) modulation. In this paper, a volumetric-based algorithm that uses a multi-element array is proposed to reject multi-follower jamming signals and carry out symbol detection in slow FH/MFSK systems over quasi-static flat fading channels. Specifically, based on using the proposed algorithm that can provide an estimate of the unknown spatial correlation of the received jamming components at the receiver antennas, jamming can be removed in the symbol detection process. The jamming rejection capability of this scheme is analyzed under a jamming dominant and a signal dominant environment. The much improved bit error rate (BER) performance that can be obtained is also verified in simulation studies. [Copyright &y& Elsevier]

Abstract: This paper presents a distributed variational Bayesian (DVBA) algorithm for density estimation and clustering in sensor networks. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. The variational approach allows the simultaneous estimate of the component parameters and the model complexity. The DVBA algorithm produces an estimate of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, DVBA can be viewed as a distributed processing approach for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the proposed DVBA is also investigated. The proposed method is then used for environmental monitoring and also distributed target classification. Simulation results approve promising performance of this algorithm. [Copyright &y& Elsevier]

Abstract: Active control of wide-band noise presents certain unique challenges many of which can be addressed using delayless subband adaptive filtering techniques. The performance of a delayless subband active noise control (DSANC) system depends on a complex interplay between the (1) choice of adaptation algorithm, (2) number of subbands, (3) weight stacking scheme, (4) input noise spectrum, and (5) primary, secondary paths. This interplay is studied in this paper for two different kinds of broadband noise. Distortion introduced by the weight stacking methods is investigated and quantified. It is shown that the computational complexity decreases and the stacking distortion increases with the number of subbands. The performance limiting effect of the non-minimum phase property of secondary path on the system performance is evaluated and analytically formulated. An upper bound for the obtainable noise attenuation level (NAL) is derived. A step by step optimal design procedure for the best performance is developed taking computational complexity into consideration. Simulation results support the analytical development and the proposed approach for optimal design of DSANC systems. [Copyright &y& Elsevier]

Abstract: For a large-scale adaptive array, heavy computational load and high-rate data transmission are two challenges in the implementation of an adaptive digital beamforming system. Moreover, the large-scale array becomes extremely sensitive to array imperfections. First, based on a restructured recursive linearly constrained minimum variance algorithm and a gradient-based optimization method, a new robust recursive linearly constrained minimum variance (RRLCMV) algorithm is proposed in this paper. The computational load of the RRLCMV algorithm is on the order of o(N), which is less than that of the conventional gradient-based robust adaptive algorithm. Then, a new efficient parallel robust recursive linearly constrained minimum variance (PRRLCMV) adaptive algorithm is proposed by appropriately partitioning the RRLCMV algorithm into a number of operational modules. It can be easily executed in a distributed-parallel-processing fashion, sequentially and in parallel. As a result, the PRRLCMV algorithm provides an effective solution that can alleviate the bottleneck of high-rate data transmission and reduce the computational cost. Finally, an implementation scheme of the PRRLCMV algorithm based on a distributed-parallel-processing system is also proposed. The simulation results demonstrate that the new PRRLCMV algorithm can significantly reduce the degradation due to various array errors. [Copyright &y& Elsevier]

Abstract: In this paper we present a general formalism for the establishment and mean-square performance analysis of the family of selective partial update affine projection (SPU-AP), selective regressor affine projection (SR-AP), and selective partial update subband adaptive filter (SPU-SAF) algorithms. This analysis is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. We demonstrate through simulations that the results are useful in predicting the performance of these adaptive filter algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, we present a fast algorithm for joint estimation of the two-dimensional (2-D) directions of arrival (DOAs) and frequencies of the incoming signals in wireless communications using a hierarchical space–time decomposition (HSTD) technique. Based on the HSTD, the proposed algorithm makes use of a sequence of one-dimensional (1-D) Unitary estimation of signal parameters via rotational invariance technique (ESPRIT) algorithms to estimate these parameters alternatively in a hierarchical tree structure. Also, in between every other 1-D Unitary ESPRIT algorithm, a temporal filtering process or a spatial beamforming process is invoked to partition the incoming signals into finer groups stage by stage to enhance the estimation accuracy as well as to alleviate the contaminated noise. Furthermore, with such a tree-structured estimation scheme, the pairing of these parameters is automatically determined without extra computational overhead. Simulation results show that the new algorithm provides satisfactory performance but with drastically reduced computations compared with previous works. [Copyright &y& Elsevier]

Abstract: In this paper, estimation of carrier frequency offset (CFO) in orthogonal frequency division multiplexing (OFDM) systems with I/Q imbalance is studied. By exploiting the mirror spectrum induced by I/Q imbalance, we proposed a novel algorithm for blind CFO estimation without training sequences. Simulation results show that the proposed algorithm outperforms the existing one, which is well accepted as a guideline of blind CFO estimation algorithms in OFDM systems. [Copyright &y& Elsevier]

Abstract: In this paper, two algorithms for multiplicative noise reduction, using the undecimated separable wavelet transform, are extended to the nonsubsampled contourlet (NSCT) domain. Such algorithms use either a maximum a posteriori (MAP) or a linear minimum mean square error (LMMSE) filtering approach, and involve the estimation of the moments of the NSCT coefficients up to the fourth order. The MAP filter relies on the conjecture that the NSCT coefficients follow a generalized Gaussian distribution (GGD), whose parameters locally vary. The extension of the denoising algorithms to the NSCT domain is not trivial, because several issues, related to the nonseparable implementation of the NSCT and the estimation of the moments, are to be considered to obtain viable solutions. Simulation results show that the MAP filter always outperforms the LMMSE one, confirming that the nonstationary GGD model is suitable for describing NSCT coefficients. Both denoising algorithms benefit from the multidirectional domain. However, the improvements on the LMMSE filter are greater than those on the MAP filter, showing that denoising in the NSCT domain is less effective when the nonstationary MAP estimator is used. [Copyright &y& Elsevier]

Abstract: For pseudo-linear regression identification models corresponding output error systems with colored measurement noises, a difficulty of identification is that there exist unknown inner variables and unmeasurable noise terms in the information vector. This paper presents an auxiliary model based multi-innovation extended stochastic gradient algorithm by using the auxiliary model method and by expanding the scalar innovation to an innovation vector. Compared with single innovation extended stochastic gradient algorithm, the proposed approach can generate highly accurate parameter estimates. The simulation results confirm this conclusion. [Copyright &y& Elsevier]

Abstract: Most of the existing direction of arrival (DOA) estimation algorithms with mutual coupling are generally based on the second-order statistics while assuming that the noise is additive white Gaussian noise. In this paper, a method based on fourth-order cumulants (FOC) is proposed for DOA estimation with uniform linear array (ULA) in the presence of unknown mutual coupling. The method can be applied when non-Gaussian signals coexist with unknown colored Gaussian noise. The unknown mutual coupling can be blindly compensated by the inherent mechanism of the proposed method and the DOA of signals can be accurately estimated without any calibration sources. Since we use the FOC, the Gaussian noise is greatly suppressed and we can obtain a good estimation performance in low SNR condition. Simulation results demonstrate the effectiveness of our method. [Copyright &y& Elsevier]