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This paper introduces an innovative methodology to establish consensus among distributed information processing nodes (DIPNs) in the context of multi-target tracking (MTT) within environments characterized by resource constraints and communication uncertainties. Through the integration of the event-triggered (ET) strategy with a consensus-based algorithm, existing approaches foster consensus among DIPNs while simultaneously conserving communication resources. Nonetheless, a systematic investigation into the comprehensive analysis of data reliability from each node has not been conducted. Combining anomalous data resulting from communication uncertainties with other data on an equal footing leads to inaccurate results. To address this issue, we apply the multiple-model algorithm, assigning consensus weight to each DIPN based on the motion model distribution of the same target observed by different DIPN. Additionally, we introduce an auxiliary ET marker, considering the divergence in the motion model distribution between two consecutive moments of a certain target. This marker assists in determining whether local information must be transmitted to other DIPNs. The proposed approach yields more accurate and congruent output results from each DIPN in comparison to conventional methods, given the same triggering frequency. Numerical simulations demonstrate the efficacy of the suggested approaches in a distributed MTT scenario. • We apply the multiple-model generalized labeled multi-Bernoulli filter. • We combine event-triggered strategy with consensus approach to reduce communication. • We check data reliability from each node to improve multi-target tracking accuracy. • We suggest an additional event-triggered mark to locate the message for transmitting. [ABSTRACT FROM AUTHOR]

To locate multiple sources through time-difference-of-arrival (TDOA) measurements, existing algorithms generally require the matching relationship between measurements and the corresponding sources. In this paper, we propose a new Bayesian learning method for cases where the matching relationship is not given and off-grid error is considered. To achieve this, first we propose a new basis generator, which casts the localization problem within the Bayesian learning scheme. Then, we modify the existing sparse Bayesian inference (SBI) approaches and explore the priors on fingerprinting weights, resulting in two intermediate algorithms. On these foundations, a subspace-based robust SBI (SRSBI) algorithm is proposed as the core of this paper. SRSBI is highlighted by its ability to work free from iteration when estimating off-grid targets. What' more, SRSBI offers considerable robustness against initial guesses of hyper-parameters. Numerical simulations demonstrate the superiority of SRSBI in terms of accuracy, robustness and speed, compared to the other reported ones. [ABSTRACT FROM AUTHOR]

In this paper, we study the regularization l p -norm minimization problem to recover the sparse signals. We first prove that every global optimal solution to the regularization l p -norm minimization problem also solves the l 0 -norm minimization problem if the certain conditions are satisfied, and then generate a truncated approximated difference algorithm to recover the sparse signals. At last, we provide some numerical simulations to test the performance of the truncated approximated difference algorithm, and the numerical results show that the proposed algorithm performs effectively in recovering the sparse signals compared with some state-of-art methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel method based on the entropy estimation of the observation space eigenvalues is proposed to estimate the number of independent sources impinging on a sensor array. In this method we do not need to know a priori information about the noise model and we can use it in any Gaussian or non-Gaussian model of observations and noise. Our analytical results show that the proposed algorithm is consistent and an approximation for probability of false alarm and an upper bound for probability of missed detection are derived analytically. The performance of the proposed algorithm is compared with the existing methods in the presence of Gaussian and non-Gaussian noise via the simulations. It is shown that this information theoretic method called EEE, has a better performance than those methods in the literature, especially in non-Gaussian noise environment. [ABSTRACT FROM AUTHOR]

Determining the frequencies of multiple resolvable exponentials is an important problem due to its application in diverse areas in science and engineering. In this paper, frequency estimation of two-dimensional (2-D) sinusoids is addressed. With the use of the periodogram in frequency domain, the required harmonics are first located coarsely. The characteristics of the 2-D spectrum is then analyzed, and the accurate estimates of the parameters are retrieved using an interpolation method iteratively. It is proved that at sufficiently high signal-to-noise ratio conditions, the harmonic estimates are asymptotically unbiased, and their variances are also analyzed. Furthermore, when only part of the data is observed, the proposed algorithm is tailored to get fast and accurate estimation results. Computer simulations are also included to compare the proposed approach with conventional 2-D harmonic retrieval schemes in terms of root mean square error performance and computational complexity. [ABSTRACT FROM AUTHOR]

Abstract: In practical cases for active noise control (ANC), the secondary path has usually a time varying behavior. For these cases, an online secondary path modeling method that uses a white noise as a training signal is required to ensure convergence of the system. The modeling accuracy and the convergence rate are increased when a white noise with a larger variance is used. However, the larger variance increases the residual noise, which decreases performance of the system and additionally causes instability problem to feedback structures. A sudden change in the secondary path leads to divergence of the online secondary path modeling filter. To overcome these problems, this paper proposes a new approach for online secondary path modeling in feedback ANC systems. The proposed algorithm uses the advantages of white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the algorithm and to prevent the instability effect of the white noise. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to correct the secondary path estimation. In addition, the proposed method models the secondary path without the need of using off-line estimation of the secondary path. Considering the above features increases the convergence rate and modeling accuracy, which results in a high system performance. Computer simulation results shown in this paper indicate effectiveness of the proposed method. [Copyright &y& Elsevier]

This paper proposes a novel diffusion subband adaptive filtering algorithm for distributed networks. To achieve a fast convergence rate and small steady-state errors, a variable step size and a new combination method is developed. For the adaptation step, the upper bound of the mean-square deviation (MSD) of the algorithm is derived and the step size is adaptive by minimizing it in order to attain the fastest convergence rate on every iteration. Furthermore, for a combination step realized by a convex combination of the neighbor-node estimates, the proposed algorithm uses the MSD, which contains information on the reliability of the estimates, to determine combination coefficients. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of the convergence rate and the steady-state errors. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel algorithm is proposed to achieve robust high resolution detection in sparse multipath channels. Currently used sparse reconstruction techniques are not immediately applicable in multipath channel modeling. Performance of standard compressed sensing formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this off-grid problem, we make use of the particle swarm optimization (PSO) to perturb each grid point that reside in each multipath component cluster. Orthogonal matching pursuit (OMP) is used to reconstruct sparse multipath components in a greedy fashion. Extensive simulation results quantify the performance gain and robustness obtained by the proposed algorithm against the off-grid problem faced in sparse multipath channels. [Copyright &y& Elsevier]

Abstract: Blind source separation (BSS) has attained much attention in signal processing society due to its ‘blind’ property and wide applications. However, there are still some open problems, such as underdetermined BSS, noise BSS. In this paper, we propose a Bayesian approach to improve the separation performance of instantaneous mixtures with non-stationary sources by taking into account the internal organization of the non-stationary sources. Gaussian mixture model (GMM) is used to model the distribution of source signals and the continuous density hidden Markov model (CDHMM) is derived to track the non-stationarity inside the source signals. Source signals can switch between several states such that the separation performance can be significantly improved. An expectation-maximization (EM) algorithm is derived to estimate the mixing coefficients, the CDHMM parameters and the noise covariance. The source signals are recovered via maximum a posteriori (MAP) approach. To ensure the convergence of the proposed algorithm, the proper prior densities, conjugate prior densities, are assigned to estimation coefficients for incorporating the prior information. The initialization scheme for the estimates is also discussed. Systematic simulations are used to illustrate the performance of the proposed algorithm. Simulation results show that the proposed algorithm has more robust separation performance in terms of similarity score in noise environments in comparison with the classical BSS algorithms in determined mixture case. Additionally, since the mixing matrix and the sources are estimated jointly, the proposed EM algorithm also works well in underdetermined case. Furthermore, the proposed algorithm converges quickly with proper initialization. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of despeckling SAR images when the input data is either an intensity or an amplitude signal is revisited. State-of-the-art despeckling methods based on Bayesian estimators in the wavelet domain, recently proposed in the literature, are taken into consideration. First, how these methods proposed for one format (e.g., intensity) can be adapted to the other format (e.g., amplitude) is investigated. Second, the performance of such algorithms in both cases is analyzed. Experimental results carried out on simulated speckled images and on true SAR data are presented and discussed in order to assess the best strategy. From these results, it can be observed that filtering in the amplitude domain yields better performances in terms of objective quality indexes, such as preservation of structural details, as well as in terms of visual inspection of the filtered SAR data. [Copyright &y& Elsevier]

• DOA estimation method based on gridless idea is provided for sparse linear array with gain-phase errors. • A novel atomic norm for the received signal of difference coarray is defined by introducing error parameters. • The process of DOA estimation is described as a dual formulation of constrained ANM approach. • A rigorous derivation and proof on how to convert dual problem into an SDP problem is given. Sparse arrays have attracted great attention in the field of direction-of-arrival (DOA) estimation due to the extended degrees of freedom (DOFs). Nevertheless, the traditional DOA estimation methods for sparse arrays suffer from degraded performance when sensor elements are uncalibrated. This paper presents a novel atomic norm-based algorithm for source localization with arbitrary sparse linear array (SLA) in the scenario with gain-phase uncertainties. Our proposed approach defines a new atomic norm for second order virtual signal by taking model errors into consideration. Then, the dual problem corresponding to original optimization problem is formulated to recover the DOAs by defining the dual atomic norm. We further present the corresponding semidefinite program characteristic that can be solved. The proposed method avoids iterations and restrictions on array configuration. It makes full use of all the DOFs provided by difference coarray of arbitrary SLA to estimate more sources and to provide high accuracy. Besides, compared with the existing coarray-based calibrated algorithms, the proposed algorithm does not need discretization on spatial domain. Computer simulations are carried out to demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an encryption algorithm for gray images using a secret key of 128-bits size is proposed. Initially, visual quality of image is degraded by the mixing process. Resultant image is partitioned into key dependent dynamic blocks and, further, these blocks are passed through key dependent diffusion and substitution processes. Total sixteen rounds are used in the encryption algorithm. Proposed technique is simple to implement and has high encryption rate. Simulation experiment results have been given to validate the high security features and effectiveness of proposed system. [Copyright &y& Elsevier]

Abstract: Automated segmentation of images has been considered an important intermediate processing task to extract semantic meaning from pixels. In general, the fuzzy c-means approach (FCM) is highly effective for image segmentation. But for the conventional FCM image segmentation algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and the spatial distribution of pixels in an image is not taken into consideration. In this paper, we present a novel FCM image segmentation scheme by utilizing local contextual information and the high inter-pixel correlation inherent. Firstly, a local spatial similarity measure model is established, and the initial clustering center and initial membership are determined adaptively based on local spatial similarity measure model. Secondly, the fuzzy membership function is modified according to the high inter-pixel correlation inherent. Finally, the image is segmented by using the modified FCM algorithm. Experimental results showed the proposed method achieves competitive segmentation results compared to other FCM-based methods, and is in general faster. [Copyright &y& Elsevier]

Abstract: This paper presents the development and implementation of a variable rate time-scaling expansion system for speech signals, based on the pitch information, in which only the voiced segments are expanded, keeping the unvoiced and silence segments unchanged. The proposed system was first evaluated by computer simulation and then implemented on a digital signal processor (DSP). Time-domain, frequency-domain, mean opinion score (MOS) and diagnostic rhyme test (DRT) evaluations were done to test the actual performance of developed algorithm, which show that the proposed system allows improving the learning level of foreign language students as well as the understanding ability of elderly people. Objective tests also were carried out in order to probe similarity between the original and the expanded signals. Applying an iterative refinement of the C source code it was possible to obtain a real-time implementation. The current implemented algorithm requires 11 kwords program memory and about 9 million of floating point operations per second (MFLOPS). [Copyright &y& Elsevier]

This paper presents an adaptive algorithm for unbiased estimation of autoregressive Rayleigh flat fading parameters in OFDM systems. To this end, a variable leaky steepest descent algorithm is proposed. The authors use a variable leak parameter in the algorithm to remove the noise induced bias from the autoregressive estimates. Theoretical performance analyses of the proposed method are conducted. Moreover, computer simulations are presented to evaluate the performance of the proposed algorithm and to compare it with other existing methods. [ABSTRACT FROM AUTHOR]

Abstract: Simple linear independent component analysis (ICA) algorithms work efficiently only in linear mixing environments. Whereas, a nonlinear ICA model, which is more complicated, would be more practical for general applications as it can work with both linear and nonlinear mixtures. In this paper, we introduce a novel method for nonlinear ICA problem. The proposed method follows the post nonlinear approach to model the mixtures, and exploits the difference between a linear mixture and a nonlinear one from their nature of distributions in a multidimensional space to develop a separation scheme. The nonlinear mixture is represented by a nonlinear surface while the linear mixture is represented by a plane. A geometric learning algorithm named as post nonlinear geometric ICA (pnGICA) is developed by geometrically transforming the nonlinear surface to a plane, i.e., to a linear mixture. Computer simulations of the algorithm provide promising performance on different data sets. [Copyright &y& Elsevier]