IEEE Journal of Selected Topics in Signal Processing (IEEE J-STSP)

Publisher: Institute of Electrical and Electronics Engineers; IEEE Signal Processing Society, Institute of Electrical and Electronics Engineers

Current impact factor: 2.37

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 2.373
2013 Impact Factor 3.629
2012 Impact Factor 3.297
2011 Impact Factor 2.88
2010 Impact Factor 2.571
2009 Impact Factor 1.2

Impact factor over time

Impact factor
Year

Additional details

5-year impact 3.68
Cited half-life 4.60
Immediacy index 0.38
Eigenfactor 0.01
Article influence 1.87
Other titles IEEE journal of selected topics in signal processing, Selected topics in signal processing
ISSN 1932-4553
OCLC 158906070
Material type Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Institute of Electrical and Electronics Engineers

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on Author's personal website, employers website or publicly accessible server
    • Author's post-print on Author's server or Institutional server
    • Author's pre-print must be removed upon publication of final version and replaced with either full citation to IEEE work with a Digital Object Identifier or link to article abstract in IEEE Xplore or replaced with Authors post-print
    • Author's pre-print must be accompanied with set-phrase, once submitted to IEEE for publication ("This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible")
    • Author's pre-print must be accompanied with set-phrase, when accepted by IEEE for publication ("(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")
    • IEEE must be informed as to the electronic address of the pre-print
    • If funding rules apply authors may post Author's post-print version in funder's designated repository
    • Author's Post-print - Publisher copyright and source must be acknowledged with citation (see above set statement)
    • Author's Post-print - Must link to publisher version with DOI
    • Publisher's version/PDF cannot be used
    • Publisher copyright and source must be acknowledged
  • Classification
    green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Signal processing in light-microscopy and cell imaging is concerned with reconstructing latent ground truth from imperfect images. This typically requires assuming prior knowledge about the latent ground truth. While this assumption regularizes the problem to an extent where it can be solved, it also biases the result toward the expected. It thus often remains unclear what prior to use for a given practical problem. We argue here that the gradient distribution of natural-scene images may provide a versatile and well-founded prior for light-microscopy images that does not impose assumptions about the geometry of the ground-truth signal, but only about its gradient spectrum. We provide motivation for this choice from different points of view, and we illustrate the resulting regularizer for use on light-microscopy images. We provide a simple parametric model for the resulting prior, leading to efficiently solvable variational problems. We demonstrate the use of these models and solvers in a variety of common image-processing tasks, including contrast enhancement, noise-level estimation, denoising, blind deconvolution, and dehazing. We conclude by discussing the limitations and possible interpretations of the prior.
    No preview · Article · Feb 2016 · IEEE Journal of Selected Topics in Signal Processing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Millimeter-wave MIMO systems have gained increasing traction towards the goal of meeting the high data-rate requirements in next-generation wireless systems. The focus of this work is on low-complexity beamforming approaches for initial UE discovery in such systems. Towards this goal, we first note the structure of the optimal beamformer with per-antenna gain and phase control and the structure of good beamformers with per-antenna phase-only control. Learning these beamforming structures in mmW systems is fraught with considerable complexities such as the need for a non-broadcast system design, the sensitivity of the beamformer approximants to small path length changes, etc. To overcome these issues, we establish a physical interpretation between these beamformer structures and the angles of departure/arrival of the dominant path(s). This physical interpretation provides a theoretical underpinning to the emerging interest on directional beamforming approaches that are less sensitive to small path length changes. While classical approaches for direction learning such as MUSIC have been well-understood, they suffer from many practical difficulties in a mmW context such as a non-broadcast system design and high computational complexity. A simpler broadcast solution for mmW systems is the adaptation of directional codebooks for beamforming at the two ends. We establish fundamental limits for the best beam broadening codebooks and propose a construction motivated by a virtual subarray architecture that is within a couple of dB of the best tradeoff curve at all useful beam broadening factors. We finally provide the received SNR loss-UE discovery latency tradeoff with the proposed constructions. Our results show that users with a reasonable link margin can be quickly discovered by the proposed design with a smooth roll-off in performance as the link margin deteriorates.
    No preview · Article · Jan 2016 · IEEE Journal of Selected Topics in Signal Processing

  • No preview · Article · Jan 2016 · IEEE Journal of Selected Topics in Signal Processing

  • No preview · Article · Jan 2016 · IEEE Journal of Selected Topics in Signal Processing

  • No preview · Article · Jan 2016 · IEEE Journal of Selected Topics in Signal Processing

  • No preview · Article · Jan 2016 · IEEE Journal of Selected Topics in Signal Processing
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper deals with Gibbs samplers that include high dimensional conditional Gaussian distributions. It proposes an efficient algorithm that avoids the high dimensional Gaussian sampling and relies on a random excursion along a small set of directions. The algorithm is proved to converge, i.e. the drawn samples are asymptotically distributed according to the target distribution. Our main motivation is in inverse problems related to general linear observation models and their solution in a hierarchical Bayesian framework implemented through sampling algorithms. It finds direct applications in semi-blind / unsupervised methods as well as in some non-Gaussian methods. The paper provides an illustration focused on the unsupervised estimation for super-resolution methods.
    Preview · Article · Dec 2015 · IEEE Journal of Selected Topics in Signal Processing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Microscopy imaging, including fluorescence microscopy and electron microscopy, has taken a prominent role in life science research and medicine due to its ability to investigate the 3D interior of live cells and organisms. A long-term research in bio-imaging at the sub-cellular and cellular scales consists then in inferring the relationships between the dynamics of macromolecules and their functions. In this area, image processing and analysis methods are now essential to understand the dynamic organization of groups of interacting molecules inside molecular machineries and to address issues in fundamental biology driven by advances in molecular biology, optics and technology. In this paper, we present recent advances in fluorescence and electron microscopy and we focus on dedicated image processing and analysis methods required to quantify phenotypes for a limited number but typical studies in cell imaging.
    No preview · Article · Dec 2015 · IEEE Journal of Selected Topics in Signal Processing
  • [Show abstract] [Hide abstract]
    ABSTRACT: It was recently shown that MIMO radars with sparse sensing and matrix completion (MC) can significantly reduce the volume of data required by MIMO radars for accurate target detection and estimation. In MIMO-MC radars, the subsampled target returns are forwarded by the receive antennas to a fusion center, partially filling a matrix, referred to as the data matrix. The data matrix is first completed via MC techniques and then used to estimate the target parameters via standard array processing methods. This paper studies the applicability of MC theory on the data matrix arising in colocated MIMO radars using uniform linear arrays. It is shown that the data matrix coherence, and consequently the performance of MC, is directly related to the transmit waveforms. Among orthogonal waveforms, the optimum choices are those for which, any snapshot across the transmit array has a flat spectrum. The problem of waveform design is formulated as an optimization problem on the complex Stiefel manifold, and is solved via the modified steepest descent method, or the modified Newton algorithm with nonmonotone line search. Although the optimal waveforms are designed for the case of targets falling in the same range bin, sensitivity analysis is conducted to assess the performance degradation when those waveforms are used in scenarios in which the targets fall in different range bins.
    No preview · Article · Dec 2015 · IEEE Journal of Selected Topics in Signal Processing
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper examines moving target detection in distributed multi-input multi-output radar with sensors placed on moving platforms. Unlike previous works which were focused on stationary platforms, we consider explicitly the effects of platform motion, which exacerbate the location-induced clutter non-homogeneity inherent in such systems and thus make the problem significantly more challenging. Two new detectors are proposed. The first is a sparsity based detector which, by exploiting a sparse representation of the clutter in the Doppler domain, adaptively estimates from the test signal the clutter subspace, which is in general distinct for different transmit/receive pairs and, moreover, may spread over the entire Doppler bandwidth. The second is a fully adaptive parametric detector which employs a parametric autoregressive clutter model and offers joint model order selection, clutter estimation/mitigation, and target detection in an integrated and fully adaptive process. Both detectors are developed within the generalized likelihood ratio test (GLRT) framework, obviating the need for training signals that are indispensable for conventional detectors but are difficult to obtain in practice due to clutter non-homogeneity. Numerical results indicate that the proposed training-free detectors offer improved detection performance over covariance matrix based detectors when the latter have a moderate amount of training signals.
    No preview · Article · Dec 2015 · IEEE Journal of Selected Topics in Signal Processing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Modern wideband radar systems with long integration time, equipped with arbitrarily waveform generators, raise a demand for advanced signaling transmission schemes. In this paper, we propose two algorithms to select transmit waveforms and receiver filters. The techniques are based on a clutter suppression criterion. For the first algorithm, we employ an optimized filter bank, and for the second algorithm, we use a matched filter bank. Clutter suppression is achieved by minimizing the correlation between receiver filters and interfering clutter echoes. The algorithm, for the optimized filter bank, is extended to adapt the transmission scheme and receiver filters to a time-evolving scenario. Adaptation parameters are based on estimates of a clutter map and detected target characteristics. To estimate the clutter map we propose a Kalman filter, whereas target parameters are calculated using a least-squares fit to data. The efficiency of the algorithms and the adaptation scheme are visualized through a numerical simulation.
    No preview · Article · Dec 2015 · IEEE Journal of Selected Topics in Signal Processing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Assuming unknown target Doppler shift, we focus on robust joint design of the transmit radar waveform and receive Doppler filter bank in the presence of signal-dependent interference. We consider the worst case signal-to-interference-plus-noise-ratio (SINR) at the output of the filter bank as the figure of merit to optimize under both a similarity and an energy constraint on the transmit signal. Based on a suitable reformulation of the original non-convex max-min optimization problem, we develop an optimization procedure which monotonically improves the worst-case SINR and converges to a stationary point. Each iteration of the algorithm, involves both a convex and a generalized fractional programming problem which can be globally solved via the generalized Dinkelbach’s procedure with a polynomial computational complexity. Finally, at the analysis stage, we assess the performance of the new technique versus some counterparts which are available in open literature.
    No preview · Article · Dec 2015 · IEEE Journal of Selected Topics in Signal Processing
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we develop a novel structured Bayesian compressive sensing algorithm with location dependence for high-resolution imaging in ultra-narrowband passive synthetic aperture radar (SAR) systems. The proposed technique exploits wide-angle and/or multi-angle observations for image resolution enhancement. We first introduce a forward model based on sparse synthetic apertures. The problem of sparse scatterer imaging is formulated as an optimization problem of reconstructing group sparse signals. A logistic Gaussian kernel model, which involves a logistic function and location-dependent Gaussian kernel, and takes the correlation between entire scatterers into account, is then used to encourage the underlying continuity structure of illuminated target scene in a nonparametric Bayesian learning framework. The posterior inference of the proposed method is then provided in the Markov Chain Monte Carlo (MCMC) sampling scheme. The proposed technique enables high-resolution SAR imaging in wide-angle as well as multi-angle observation systems, and the imaging performance is improved by exploiting the underlying structure of the target scene. Simulation and experiment results demonstrate the superiority of the proposed algorithm in preserving the continuous structure and suppressing isolated components over existing state-of-the-art compressive sensing methods.
    No preview · Article · Dec 2015 · IEEE Journal of Selected Topics in Signal Processing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Inspired by the fixational movements of the human eye, fast-time spatial modulation was recently demonstrated as a particular physically realizable form of a multiple-input multiple-output (MIMO) radar emission. The attendant coupling of the delay and angle dimensions has been shown to provide a modest improvement in spatial separation, even when using non-adaptive pulse compression and beamforming. Here this continuous emission paradigm is appropriately discretized and a joint delay-angle adaptive filtering strategy is developed that exploits the physical waveform-diverse emission structure to realize significant enhancement in target separability.
    No preview · Article · Dec 2015 · IEEE Journal of Selected Topics in Signal Processing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Subarray partition is indispensable in large phased array radar system for reducing the manufacturing cost as well as realizing the system potentiality. The optimization of subarray partition for large planar phased array radar according to weighted K-means clustering method is mainly investigated in this paper. Based on the excitation matching technique, the optimization of subarray partition in monopulse application can be reformulated as a clustering of reference gain ratios to minimize the excitation matching error. However, when the element weights are non-uniform for specific intentions such as low sidelobes, the matching error could not be minimized completely by traditional K-means clustering. Therefore, in this paper, a weighted K-means clustering method is proposed to reduce the matching error by modifying the membership rule and cluster center of K-means clustering. The proposed method can provide smaller matching error compared with conventional clustering methods, especially when the elements are weighted non-uniformly. The effectiveness of proposed method is validated by numerical simulations and compared with several classical clustering methods.
    No preview · Article · Dec 2015 · IEEE Journal of Selected Topics in Signal Processing
  • [Show abstract] [Hide abstract]
    ABSTRACT: We present a novel image formation method for passive synthetic aperture radar (SAR) imaging. The method is an alternative to widely used time difference of arrival (TDOA) or correlation-based backprojection method. These methods work under the assumption that the scene is composed of a single or a few widely separated point targets. The new method overcomes this limitation and can reconstruct heterogeneous scenes with extended targets. We assume that the scene of interest is illuminated by a stationary transmitter of opportunity with known illumination direction, but unknown location. We consider two airborne receivers and correlate the fast-time bistatic measurements at each slow-time. This correlation process maps the tensor product of the scene reflectivity with itself to the correlated measurements. Since this tensor product is a rank-one positive semi-definite operator, the image formation lends itself to low-rank matrix recovery techniques. Taking into account additive noise in bistatic measurements, we formulate the estimation of the rank-one operator as a convex optimization with rank constrain. We present a gradient-descent based iterative reconstruction algorithm and analyze its computational complexity. Extensive numerical simulations show that the new method is superior to correlation-based backprojection in reconstructing extended and distributed targets with better geometric fidelity, sharper edges, and better noise suppression.
    No preview · Article · Dec 2015 · IEEE Journal of Selected Topics in Signal Processing