The Localization of distributed sources has attracted significant interest recently. There mainly are two types of localization methods which are able to estimate distributed source positions: two-step methods and direct localization methods. Unfortunately, both fail to exploit the location information and so suffer a loss in localization accuracy. By utilizing the information not used in the above, a direct localization method of multiple distributed sources is proposed in this paper that offers improved location accuracy. We construct a direct localization model of multiple distributed sources and develop a direct localization estimator with the theory of multiple signal classification. The distributed source positions are estimated via a three-dimensional grid search. We also provide Cramer-Rao Bound, computational complexity analysis and Monte Carlo simulations. The simulations demonstrate that the proposed method outperforms the localization methods above in terms of accuracy and resolution.

In this paper, we consider a coherently distributed (CD) source model. Since the CD source is characterized by four parameters: central azimuth direction-of-arrival (DOA), azimuth angular spread, central elevation DOA and elevation angular spread, the parameter estimation is normally complex. We propose an algorithm that combines the rotational invariance techniques (ESPRIT) and the generalized ESPRIT algorithm for the 2-dimensional (2D) central DOA estimation of CD sources. Using a pair of uniform circular arrays (UCAs), the proposed solution is able to obtain the central DOAs with both high accuracy and low computational complexity. The central elevation DOAs are estimated by using the rotational invariance relation between the two uniform circular sub-arrays. Based on the centrosymmetric structure of UCA, the generalized ESPRIT algorithm is then applied to estimate the central azimuth DOAs through one-dimensional searching. It is noteworthy that the central DOAs are estimated without any information of the deterministic angular distribution function (DADF). The performance of the proposed algorithm is demonstrated via computer simulations.

In this paper, a new parameter estimator for coherently distributed (CD) noncircular (NC) signals is proposed, and can estimate both the central direction-of-arrivals (DOAs) and the angular spreads. It can also be considered as an extended version of the generalized Capon method by using both covariance matrix and an elliptic covariance matrix. The central DOAs and angular spreads are obtained by two-dimensional spectrum-peak searching. Numerical examples illustrate that the proposed method can estimate the central DOAs and the angular spreads when the number of signals is greater than the number of sensors. The proposed method also offers better performance than the methods against which it is compared.

Wireless patient monitoring is an active research area with the goal of ubiquitous health care services. This study presents a novel means of exploiting the distributed source coding (DSC) in low-complexity compression of ECG signals. We first convert the ECG data compression to an equivalent channel coding problem and exploit a linear channel code for the DSC construction. Performance is further enhanced by the use of a correlation channel that more precisely characterizes the statistical dependencies of ECG signals. Also proposed is a modified BCJR algorithm which performs symbol decoding of binary convolutional codes to better exploit the source's a priori information. Finally, a complete setup system for online ambulatory ECG monitoring via mobile cellular networks is presented. Experiments on the MIT-BIH arrhythmia database and real-time acquired ECG signals demonstrate that the proposed system outperforms other schemes in terms of encoder complexity and coding efficiency.

In this letter, distributed source coding with one distortion criterion and correlated messages is considered. This problem can be regarded as “Berger-Yeung problem with correlated messages”. It corresponds to the source coding part of the graph-based framework for transmission of a pair of correlated sources over the multiple-access channel where one is lossless and the other is lossy. As a result, the achievable rate-distortion region for this problem is provided. A rigorous proof of both achievability and converse part is also given.

In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.

Applying Distributed Video Coding (DVC) to mobile devices that have limited computation and power resources can be a very challenging problem due to its high-complexity decoding. To address this, this paper proposes a DVC bitstream organizer. The proposed DVC bitstream organizer reduces the complexity associated with repetitive channel decoding and SI generation in a flexible manner. It allows users to choose a means of minimizing the computational complexity of the DVC decoder according to their preferences and the device's resource limitations. An experiment shows that the proposed method increases decoding speeds by up to 25 times.

In this letter, functional duality between distributed source coding (DSC) with correlated messages and broadcast channel coding (BCC) with correlated messages is considered. It is shown that under certain conditions, for a given DSC problem with correlated messages, a functional dual BCC problem with correlated messages can be obtained, and vice versa. That is, the optimal encoder-decoder mappings for one problem become the optimal decoder-encoder mappings for the dual problem. Furthermore, the correlation structure of the messages in the two dual problems and the source distortion and channel cost measure for this duality are specified.

The problem of estimating the nominal angles and angular spreads of multiple coherently distributed (CD) sources in a symmetric uniform linear array (ULA) is considered. Based on structure of the subarrays consisting of two opposite sensors relative to the center of a ULA and the rank reduction (RARE) concept, the proposed algorithm is able to estimate the nominal angles without any angular signal density model assumptions of the sources. Using the estimated nominal angles, the angular spread of each source is then obtained using a one-dimensional (1-D) distributed source parameter estimator (DSPE).

Multimedia multicast with two servers based on the multiterminal source coding is studied in some previous researches. Due to the possibility of providing an approach for practical code design for more than two correlated sources in IMTSC/CEO setup, in this paper, the framework of Slepian-Wolf coded quantization is extended and a practical code design is presented for IMTSC/CEO with the number of encoders greater than two. Then the multicast system based on the IMTSC/CEO is applied to the cases with three, four and five servers. Since the underlying code design approach for the IMTSC/CEO problem has the capability of applying to an arbitrary number of active encoders, the proposed MMBMSC method can also be used with an arbitrary number of servers easily. Also, explicit expressions of the expected distortion with an arbitrary number of servers in the MMBMSC system are presented. Experimental results with data, image and video signals show the superiority of our proposed method over the conventional solutions and over the MMBMSC system with two servers.

Wyner and Ziv characterized the rate distortion function for lossy source coding with side information at the decoder. It is well known that for the quadratic Gaussian case, the Wyner-Ziv rate-distortion function coincides with the conditional rate-distortion function. In this paper, we extend the problem to the coding of multivariate Gaussian source with multiple Gaussian side information at the decoder. The achievable region is obtained, and it is easily extended to the case that the difference between the source and the side information is multivariate Gaussian, no matter what distributions the source and the side information are. We apply this theoretical model to Distributed Video Coding (DVC) by considering the difference of the Distributed frame (D frame) and the Side-information frame (S frame) to be multivariate Gaussian distributed. This introduces rate allocation problem into DVC, which can be solved by a reverse water-filling method. Simulation results show that around 1.5-2 dB coding gain benefits from the multivariate Gaussian Wyner-Ziv coding model.

We show that distributed source coding of multi-view images in camera sensor networks (CSNs) using adaptive modules can come close to the Slepian-Wolf bound. In a systematic scenario with limited node abilities, work by Slepian and Wolf suggest that it is possible to encode statistically dependent signals in a distributed manner to the same rate as with a system where the signals are jointly encoded. We considered three nodes (PN, CN and CNs), which are statistically depended. Different distributed architecture solutions are proposed based on a parent node and child node framework. A PN sends the whole image whereas a CNs/CN only partially, using an adaptive coding based on adaptive module-operation at a rate close to theoretical bound - H(CNs|PN)/H(CN|PN,CNs). CNs sends sub-sampled image and encodes the rest of image, however CN encodes all image. In other words, the proposed scheme allows independent encoding and jointly decoding of views. Experimental results show performance close to the information-theoretic limit. Furthermore, good performance of the proposed architecture with adaptive scheme shows significant improvement over previous work.

In this paper, we consider the problem of bearing estimation for spatially distributed sources in unknown spatially-correlated noise. Assumed that the noise covariance matrix is centro-Hermitian, a differential denoising scheme is developed. Combined it with the classic DSPE algorithm, a differential denoising estimator is formulated. Its modified version is also derived. Exactly, the differential processing is first imposed on the covariance matrix of array outputs. The resulting differential signal subspace (DSS) is then utilized to weight array outputs. The noise components orthogonal to DSS are eliminated. Based on eigenvalue decomposition of the covariance matrix of weighted array outputs, the DSPE null spectrum is constructed. The asymptotic performance of the proposed bearing estimator is evaluated in a closed form. Moreover, in order to improve the performance of bearing estimation in case of low signal-to-noise ratio, a modified differential denoising estimator is proposed. Simulation results show the effectiveness of the proposed estimators under the low SNR case. The impacts of angular spread and number of sensors are also investigated.

A number of results on the estimation of direction of arrival have been obtained based on the assumption that the signal sources are point sources. Recently, it has been shown that signal source localization can be accomplished more adequately with distributed source models in some real surroundings. In this paper, we consider modeling of three-dimensional distributed signal sources, in which a source location is represented by the center angles and degrees of dispersion. We address estimation of the elevation and azimuth angles of distributed sources based on the proposed distributed source modeling in the three-dimensional space using two linear arrays. Some examples are included to more explicitly show the estimation procedures under the model: numerical results obtained by a MUSIC-based method with two uniform linear arrays are discussed.

Coupling in time domain between two non-parallel transmission lines of finite length is analyzed by using a circuit concept. Coupling equations based on the Maxwell's equations for lossless transmission lines in a homogeneous medium are written by a set of non-homogeneous differential equations including distributed source terms produced by external electromagnetic fields. The forcing terms are expressed by vector potentials generated by currents in the line section and at the transitions. A set of solutions in frequency domain is obtained by a four-port network expression with regard to the terminal voltages and currents, and can be applied to estimation of the frequency-domain crosstalk. Utilizing the inverse fast Fourier transform (FFT), the crosstalk responses between the lines is studied in time domain. Comparison of theoretical and experimental results shows the validity of the method.

Most research on the estimation of direction of arrival (DOA) has been performed based on the assumption that the signal sources are point sources. In some real surroundings, signal source localization can more adequately be accomplished with distributed source models. When the signal sources are distributed over an area, we cannot directly use well-known DOA estimation methods, because these methods are established based on the point source assumption. In this paper, we propose a 3-dimensional distributed signal source model, in which a source is represented by two parameters, the center angle and degree of dispersion. Then, we address the estimation of the elevation and azimuth angles of distributed sources based on the parametric distributed source modeling in the 3-dimensional space.