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Signal Processing, IEEE Transactions on

Issue 10 • Date May15, 2015

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Displaying Results 1 - 22 of 22
  • Estimation of Overspread Scattering Functions

    Publication Year: 2015 , Page(s): 2451 - 2463
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3506 KB) |  | HTML iconHTML  

    In many radar scenarios, the radar target or the medium is assumed to possess randomly varying parts. The properties of the target are described by a random process known as the spreading function. Its second order statistics under the WSSUS assumption are given by the scattering function. Recent developments in operator sampling theory suggest novel channel sounding procedures that allow for the determination of the spreading function given complete statistical knowledge of the operator echo from a single sounding by a weighted pulse train. We construct and analyze a novel estimator for the scattering function based on these findings. Our results apply whenever the scattering function is supported on a compact subset of the time-frequency plane. We do not make any restrictions either on the geometry of this support set, or on its area. Our estimator can be seen as a generalization of the averaged periodogram estimator for the case of a non-rectangular geometry of the support set of the scattering function. View full abstract»

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  • A New Model for Array Spatial Signature for Two-Layer Imaging With Applications to Nondestructive Testing Using Ultrasonic Arrays

    Publication Year: 2015 , Page(s): 2464 - 2475
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2026 KB)  

    Imaging multilayer materials is a common challenge in seismology, medical diagnosis, and nondestructive testing. One of the applications of multilayer imaging is ultrasonic immersion test where the material under test and the transducer array are immersed in water. The main imaging challenge in immersion test (or in imaging any multilayer medium) is that since the sound wave propagates with different speeds in different layers of a multilayer medium, such a medium cannot be assumed homogenous. As a result, calculating the sound travel time for the received signal due to backscattering from such a nonhomogenous medium is not as straightforward as in the case of homogenous materials. In this paper, we propose a new model for the array spatial signature which can be used in frequency-domain algorithms that are used for imaging a two-layer medium when an array of transducers is utilized. To do so, we model the interface between the two layers as a spatially distributed source which consists of infinite number of point sources. Then, we use this model to develop a new array spatial signature for any point inside the second layer of a two-layer medium. This new array spatial signature can be used for multilayer ultrasonic imaging in frequency-domain imaging techniques including the conventional beamforming technique, the MUSIC method, and the Capon algorithm. Numerical simulations as well as experimental data are used to examine the accuracy of the proposed model. View full abstract»

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  • Polynomial-Time Algorithms for the Exact MMOSPA Estimate of a Multi-Object Probability Density Represented by Particles

    Publication Year: 2015 , Page(s): 2476 - 2484
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1964 KB)  

    In multi-object estimation, the traditional minimum mean squared error (MMSE) objective is unsuitable: a simple permutation of object identities can turn a very good estimate into what is apparently a very bad one. Fortunately, a criterion tailored to sets—minimization of the mean optimal sub-pattern assignment (MMOSPA)—has recently evolved. Aside from special cases, exact MMOSPA estimates have seemed difficult to compute. But in this work we present the first exact polynomial-time algorithms for calculating the MMOSPA estimate for probability densities that are represented by particles. The key insight is that the MMOSPA estimate can be found by means of enumerating the cells of a hyperplane arrangement, which is a traditional problem from computational geometry. Although the runtime complexity is still high for the general case, efficient algorithms are obtained for two special cases, i.e., (i) two targets with arbitrary state dimensions and (ii) an arbitrary number of one-dimensional targets. View full abstract»

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  • Joint Source Estimation and Localization

    Publication Year: 2015 , Page(s): 2485 - 2495
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2451 KB)  

    The estimation of directions of arrival is formulated as the decomposition of a 3-way array into a sum of rank-one terms, which is possible when the receive array enjoys some geometrical structure. The main advantage is that this decomposition is essentially unique under mild assumptions, if computed exactly. The drawback is that a low-rank approximation does not always exist. Therefore, a coherence constraint is introduced that ensures the existence of the latter best approximate, which allows to localize and estimate closely located or highly correlated sources. Then Cramér-Rao bounds are derived for localization parameters and source signals, assuming the others are nuisance parameters; some inaccuracies found in the literature are pointed out. Performances are eventually compared with unconstrained reference algorithms such as ESPRIT, in the presence of additive complex Gaussian noise, with possibly noncircular distribution. View full abstract»

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  • Actor Merging for Dataflow Process Networks

    Publication Year: 2015 , Page(s): 2496 - 2508
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1242 KB)  

    Dataflow process networks provide a versatile model of computation for specifying signal processing applications in a platform independent fashion. This attractive feature of dataflow has lately been realized in dataflow programming tools that allow synthesizing the same application specification as both fixed hardware circuits and as software for programmable processors. However, in practice, the specification granularity of the dataflow program remains an arbitrary choice of the designer. Dataflow specifications of the same application with equivalent I/O behaviour can range from a single dataflow actor to a very fine grained network composed of elementary processing operations. A very fine grained dataflow specification might result into a high performance implementation when synthesized as hardware, but might perform poorly when executed on a programmable processor. This article presents actor merging as one solution for this performance portability problem of dataflow programs. In contrast to previous work around actor merging, this article presents a methodology that can merge also dynamic dataflow actors. To support these claims, results of experiments on several processing platforms and application examples ranging from telecommunications to video compression are reported. View full abstract»

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  • Unified Codebook Design for Vector Channel Quantization in MIMO Broadcast Channels

    Publication Year: 2015 , Page(s): 2509 - 2519
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1948 KB)  

    In this paper, we propose a two-stage vector channel quantizer for multiple-input multiple-output (MIMO) broadcast channels with limited feedback. When the number of total users is larger than the number of transmit antennas, the users to be served are selected and then the selected users are supported by beamforming based on quantized feedback information. If channel gain information (CGI) and channel direction information (CDI) are independently quantized with a product codebook, as in conventional channel quantization, we show that the effect of CDI quantization errors is boosted by the CGI value, which becomes more pronounced as the number of users increases because the selected users are likely to have larger CGI than the others. Motivated by the analysis, we devise a new two-stage quantizer where CGI is quantized at the first stage, and then CDI is adaptively quantized at the second stage according to the quantized CGI value and total number of users. We optimize the two-stage quantizer for an arbitrarily given CGI quantizer and the number of total users. It is demonstrated that for the same feedback size, the proposed quantizer markedly improves conventional quantization with a product codebook in terms of average sum rate in MIMO BC. View full abstract»

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  • Performance Analysis of Antenna Selection in Two-Way Relay Networks

    Publication Year: 2015 , Page(s): 2520 - 2532
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3522 KB)  

    We investigate the performance of multi-antenna two-way relay networks, where both amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies are considered. First an antenna selection scheme among all nodes is proposed based on maximizing the worse received signal-to-noise ratio (SNR) of two end users. Then, we derive the probability density function (PDF) and cumulative distribution function (CDF) of the received SNRs of both users. We also obtain the closed-form expressions of average bit error rates (BER) and the outage probability of our system. Furthermore, we study the asymptotic behavior of our system when transmitting SNR or the number of antennas is large. The results show that the proposed antenna selection scheme achieves full diversity, and the simulation results closely match to our theoretical analysis. To further improve the spectrum efficiency of the system, a hybrid selection antenna scheme is proposed. Finally, the numerical results show that our scheme outperforms the state of art. View full abstract»

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  • Shallow Water Acoustic Channel Modeling Based on Analytical Second Order Statistics for Moving Transmitter/Receiver

    Publication Year: 2015 , Page(s): 2533 - 2545
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3509 KB)  

    Underwater acoustic channels are among the most challenging communication media. Time-varying multipath fading, long delay spread, significant Doppler spread, and frequency-dependent path loss are the main aspects of such channels. In this paper we present a statistical shallow water channel model for moving transmitter/receiver based on analytical second order statistics. To do so, we first propose a channel impulse response (CIR) model that captures most of the physical properties of shallow waters. Then we find the probability density function (PDF) of the angle of arrival (AoA) for paths with different number of surface and bottom reflections. To find closed form expressions for the second order statistics of the CIR, we approximate the PDFs of AoA with half-circular Rice PDF: a novel PDF introduced in this work. By mathematical tractability of this new PDF, analytical statistics including autocorrelation function, scattering function, and time-frequency correlation function are derived. The results are compared with experimental findings for verification. View full abstract»

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  • Low Complexity CFO Compensation in Uplink OFDMA Systems With Receiver Windowing

    Publication Year: 2015 , Page(s): 2546 - 2558
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2476 KB)  

    Orthogonal frequency division multiple access (OFDMA) systems in the uplink suffer from multiple access interference (MAI) due to their high sensitivity to frequency misalignments between different users. In this paper, we propose the application of time domain receiver windowing methods to confine the leakage caused by multiple carrier frequency offsets (CFOs) to a few neighboring subcarriers with almost no additional computational burden. The CFO effects can be translated into a linear system of equations with a coefficient matrix that is called interference matrix. As a result of receiver windowing, we can approximate the interference matrix with a quasi-banded one by neglecting its small elements outside a certain bandwidth. This allows us to propose a class of low complexity CFO compensation techniques. These techniques are applicable to the generalized subcarrier allocation scheme (G-CAS). The complexity reduction in the proposed solutions is substantial when compared to the existing ones in the literature. It is worth mentioning that this substantial complexity reduction is achieved in expense of some bandwidth efficiency loss. Hence, there exists a tradeoff between bandwidth efficiency and complexity. Solutions based on both zero forcing (ZF) and minimum mean squared error (MMSE) criteria are proposed and compared. Simulation results demonstrating the effectiveness of the proposed algorithms in approaching the optimal performance are also presented. View full abstract»

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  • Channel State Tracking for Large-Scale Distributed MIMO Communication Systems

    Publication Year: 2015 , Page(s): 2559 - 2571
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3224 KB)  

    This paper considers the problem of estimating and tracking channels in a distributed transmission system with N_{t} transmit nodes and N_{r} receive nodes. Since each node in the distributed transmission system has an independent local oscillator, the effective channel between each transmit node and each receive node has time-varying phase and frequency offsets which must be tracked and predicted to facilitate coherent transmission. A linear time-invariant state-space model is developed and is shown to be observable but nonstabilizable. To quantify the steady-state performance of a Kalman filter channel tracker, two methods are developed to efficiently compute the steady-state prediction covariance. The first method requires the solution of a 2(N_{t}+N_{r}-1) -dimensional discrete-time algebraic Riccati equation, but allows for nonhomogenous oscillator parameters. The second method requires the solution of four two-dimensional discrete-time algebraic Riccati equations but requires homogenous oscillator parameters for all nodes in the system. An asymptotic analysis is also presented for the homogenous oscillator case for systems with a large number of transmit and receive nodes with closed-form results for all of the elements in the asymptotic prediction covariance as a function of the carrier frequency, oscillator parameters, and channel measurement period. Numeric results confirm the analysis and demonstrate the effect of the oscillator parameters on the ability of the distributed transmission system to achieve coherent transmission. View full abstract»

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  • Signal Recovery from Random Measurements via Extended Orthogonal Matching Pursuit

    Publication Year: 2015 , Page(s): 2572 - 2581
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2365 KB)  

    Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP) are two well-known recovery algorithms in compressed sensing. To recover a d -dimensional m -sparse signal with high probability, OMP needs O(m\ln{d}) number of measurements, whereas BP needs only O\left(m\ln{{d\over m}}\right) number of measurements. In contrary, OMP is a practically more appealing algorithm due to its superior execution speed. In this piece of work, we have proposed a scheme that brings the required number of measurements for OMP closer to BP. We have termed this scheme as {\rm OMP}_{\alpha } , which runs OMP for (m+\lfloor \alpha {m}\rfloor ) -iterations instead of m -iterations, by choosing a value of \alpha \in[0,1] . It is shown that {\rm OMP}_{\alpha } guarantees a high probability signal recovery with O\left(m\ln{{d\over\lfloor \alpha {m}\rfloor +1}}\right) number of measurements. Another limitation of OMP unlike BP is that it requires the knowledge of m . In order to overcome this limitation, we have extended the idea of {\rm OMP}_{\alpha } to illustrate another recovery scheme called {\rm OMP}_{\infty } , which runs OMP until th- signal residue vanishes. It is shown that {\rm OMP}_{\infty } can achieve a close to \ell _{0} -norm recovery without any knowledge of m like BP. View full abstract»

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  • Sparsity-Aware Sensor Collaboration for Linear Coherent Estimation

    Publication Year: 2015 , Page(s): 2582 - 2596
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3199 KB)  

    In the context of distributed estimation, we consider the problem of sensor collaboration, which refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. While incorporating the cost of sensor collaboration, we aim to find optimal sparse collaboration schemes subject to a certain information or energy constraint. Two types of sensor collaboration problems are studied: minimum energy with an information constraint; and maximum information with an energy constraint. To solve the resulting sensor collaboration problems, we present tractable optimization formulations and propose efficient methods that render near-optimal solutions in numerical experiments. We also explore the situation in which there is a cost associated with the involvement of each sensor in the estimation scheme. In such situations, the participating sensors must be chosen judiciously. We introduce a unified framework to jointly design the optimal sensor selection and collaboration schemes. For a given estimation performance, we empirically show that there exists a trade-off between sensor selection and sensor collaboration. View full abstract»

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  • Estimation of Spatially Correlated Random Fields in Heterogeneous Wireless Sensor Networks

    Publication Year: 2015 , Page(s): 2597 - 2609
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (3055 KB)  

    We develop new algorithms for spatial field reconstruction, exceedance level estimation and classification in heterogeneous (mixed analog & digital sensors) Wireless Sensor Networks (WSNs). We consider spatial physical phenomena which are observed by a heterogeneous WSN, meaning that it consists partially of sparsely deployed high-quality sensors and partially of low-quality sensors. The high-quality sensors transmit their (continuous) noisy observations to the Fusion Centre (FC), while the low-quality sensors first perform a simple thresholding operation and then transmit their binary values over imperfect wireless channels to the FC. The resulting observations are mixed continuous and discrete (1-bit decisions) observations, and are combined in the FC to solve the inference problems. We first formulate the problem of spatial field reconstruction, exceedance level estimation and classification in such heterogeneous networks. We show that the resulting posterior predictive distribution, which is key in fusing such disparate observations, involves intractable integrals. To overcome this problem, we develop an algorithm that is based on a multivariate series expansion approach resulting in a Saddle-point type approximation. We then present comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using real data of insurance storm surge database known as the Extreme Wind Storms Catalogue. View full abstract»

    Open Access
  • On Decentralized Estimation With Active Queries

    Publication Year: 2015 , Page(s): 2610 - 2622
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3241 KB)  

    We consider the problem of decentralized 20 questions with noise for multiple players/agents under the minimum entropy criterion in the setting of stochastic search over a parameter space, with application to target localization. We propose decentralized extensions of the active query-based stochastic search strategy that combines elements from the 20 questions approach and social learning. We prove convergence to correct consensus on the value of the parameter. This framework provides a flexible and tractable mathematical model for decentralized parameter estimation systems based on active querying. We illustrate the effectiveness and robustness of the proposed decentralized collaborative 20 questions algorithm for random network topologies with information sharing. View full abstract»

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  • Asymptotically Efficient Estimation of a Nonlinear Model of the Heteroscedasticity and the Calibration of Measurement Systems

    Publication Year: 2015 , Page(s): 2623 - 2638
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4417 KB)  

    The measurement system calibration includes the estimation of the sensor error models in order to get an optimal estimation of the measured parameters. The paper is devoted to the estimation of a nonlinear parametric model of the heteroscedasticity and its application to the calibration of measurement systems. The heteroscedasticity occurs in regression when the measurement noise variance is nonconstant. The maximum likelihood (ML) estimation of variance function parameters leads to a system of nonlinear equations. The iterative solution of these nonlinear equations is based entirely on a successful choice of initial conditions which is intractable in practice. To overcome this difficulty, another linear quasi-ML estimator is proposed. It is strongly consistent, asymptotically Gaussian, and only slightly less efficient than the Cramér–Rao lower bound. By using this estimator as an initial condition, an asymptotically efficient estimation is obtained by using one-step noniterative Newton method. The theoretical findings have been applied to the calibration of the EGNOS/GPS positioning algorithm in the (sub-)urban environments. View full abstract»

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  • Robust Joint Source-Relay-Destination Design Under Per-Antenna Power Constraints

    Publication Year: 2015 , Page(s): 2639 - 2649
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2549 KB)  

    This paper deals with joint source-relay-destination beamforming (BF) design for an amplify-and-forward (AF) relay network. Considering the channel state information (CSI) from the relay to the destination is imperfect, we first aim to maximize the worst case received SNR under per-antenna power constraints. The associated optimization problem is then solved in two steps. In the first step, by revealing the rank-one property of the optimal relay BF matrix, we establish the semi-closed form solution of the joint optimal BF design that only depends on a vector variable. Based on this result, in the second step, we propose a low-complexity iterative algorithm to obtain the remaining unknown variable. We also study the problem for minimizing the maximum per-antenna power at the relay while ensuring a received signal-to-noise ratio (SNR) target, and show that it reduces to the SNR maximization problem. Thus the same methods can be applied to solve it. The differences between our result and the existing related work are also discussed in details. In particular, we show that in the perfect CSI case, our algorithm has the same performance but with much lower cost of computational complexity than the existing method. Finally, in the simulation part, we investigate the impact of imperfect CSI on the system performance to verify our analysis. View full abstract»

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  • Performance Analysis of Time-Reversal MUSIC

    Publication Year: 2015 , Page(s): 2650 - 2662
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5082 KB)  

    In this paper, we study the performance of multiple signal classification (MUSIC) in computational time-reversal (TR) applications. The analysis builds upon classical results on first-order perturbation of singular value decomposition. The closed form of mean-squared error (MSE) matrix of TR-MUSIC is derived for the single-frequency case in both multistatic co-located and non co-located scenarios. The proposed analysis is compared with Cramér–Rao lower-bound (CRLB), and it is exploited for comparison of TR-MUSIC when linear and (nonlinear) multiple-scattering is present. Finally, a numerical analysis is provided to confirm the theoretical findings. View full abstract»

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  • Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors

    Publication Year: 2015 , Page(s): 2663 - 2677
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3691 KB)  

    Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with “Big Data” analytics. However, increasingly noisy, heterogeneous, and incomplete datasets, as well as the need for real-time processing of streaming data, pose major challenges to this end. In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from incomplete streaming data. For low-rank matrix data, a subspace estimator is proposed based on an exponentially weighted least-squares criterion regularized with the nuclear norm. After recasting the nonseparable nuclear norm into a form amenable to online optimization, real-time algorithms with complementary strengths are developed, and their convergence is established under simplifying technical assumptions. In a stationary setting, the asymptotic estimates obtained offer the well-documented performance guarantees of the batch nuclear-norm regularized estimator. Under the same unifying framework, a novel online (adaptive) algorithm is developed to obtain multi-way decompositions of low-rank tensors with missing entries and perform imputation as a byproduct. Simulated tests with both synthetic as well as real Internet and cardiac magnetic resonance imagery (MRI) data confirm the efficacy of the proposed algorithms, and their superior performance relative to state-of-the-art alternatives. View full abstract»

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  • Constrained and Preconditioned Stochastic Gradient Method

    Publication Year: 2015 , Page(s): 2678 - 2691
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    We consider stochastic approximations that arise from such applications as data communications and image processing. We demonstrate why constraints are needed in a stochastic approximation and how a constrained approximation can be incorporated into a preconditioning technique to derive the preconditioned stochastic gradient method (PSGM). We perform convergence analysis to show that the PSGM converges to the theoretical best approximation under some simple assumptions on the preconditioner and on the independence of samples drawn from a stochastic process. Simulation results are presented to demonstrate the effectiveness of the constrained and preconditioned stochastic gradient method. View full abstract»

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  • Performance Tradeoffs for Networked Jump Observer-Based Fault Diagnosis

    Publication Year: 2015 , Page(s): 2692 - 2703
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2338 KB)  

    In this paper, we address the fault diagnosis problem for discrete-time multi-sensor systems over communication networks with measurement dropouts. We use the measurement outcomes to model the measurement reception scenarios. Based on this, we propose the use of a jump observer to diagnose multiple faults. We model the faults as slow time-varying signals and introduce this dynamic in the observer to estimate the faults and to generate a residual. The fault detection is assured by comparing the residual signal with a prescribed threshold. We design the jump observer, the residual and the threshold to attain disturbance attenuation, fault tracking and detection conditions and a given false alarm rate. The false alarm rate is upper bounded by means of Markov’s inequality. We explore the tradeoffs between the minimum detectable faults, the false alarm rate and the response time to faults of the fault diagnoser. By imposing the disturbances and measurement noises to be Gaussian, we tighten the false alarm rate bound which improves the time needed to detect a fault. A numerical example is provided to illustrate the effectiveness of the theory developed in the paper. View full abstract»

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  • Sensing and Recognition When Primary User Has Multiple Transmit Power Levels

    Publication Year: 2015 , Page(s): 2704 - 2717
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2725 KB)  

    In this paper, a new cognitive radio (CR) scenario called Multiple Primary Transmit Power (MPTP) is investigated where the primary user (PU) could possibly work on more than one discrete transmit power levels. Different from most existing literatures where PU is assumed to operate with a constant transmit power only, this new consideration well matches the practical standards, e.g., IEEE 802.11 Series, GSM, LTE, LTE-A, etc., as well as the adaptive powering concept that a user would vary its transmit power under different situations. The primary target of CR under MPTP may still be detecting the presence of PU. However, there emerges a secondary target as to identify the PU’s transmit power level. Compared to the existing sensing strategy where the secondary user (SU) only detects the “on-off” status of PU, recognizing the transmit power level of PU achieves more “cognition” and makes the CR more intelligent. Meanwhile, SU could utilize the power level information of PU and make the subsequent design. We derive quite many closed-form results for either the threshold expressions or the performance analysis in this new CR scenario, from which many interesting points and discussions are raised. We then study the cooperative sensing schemes under MPTP and demonstrate their significant differences from traditional cooperative algorithms. Lastly, numerical examples are provided to corroborate the proposed studies. View full abstract»

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  • Source Enumeration in Array Processing Using a Two-Step Test

    Publication Year: 2015 , Page(s): 2718 - 2727
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1618 KB)  

    We investigate the problem of enumerating source signals impinging on an array of sensors given noisy, limited observations. The assumption of a large sample size is indispensable for most of the existing approaches so that they suffer significant performance degradation when only a small number of samples is available. Random matrix theory can capture the asymptotic distributions of the eigenvalues accurately and provide good approximations even for a finite sample size. In this paper, we take into account the relationship and distributions of the noise and signal eigenvalues based on random matrix theory and derive a simple two-step test. Numerical simulations demonstrate that it is capable of correctly determining the number of sources in the case of small sample sizes. View full abstract»

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Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

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Meet Our Editors

Editor-in-Chief
Sergios Theodoridis
University of Athens