P. Stoica

Uppsala University, Uppsala, Uppsala, Sweden

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Publications (650)958.6 Total impact

  • Marcus Björk, Petre Stoica
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    ABSTRACT: Two methods for temporal phase correction are presented and analysed.•Show superior performance in simulation compared to the previous method.•Versatile, computationally efficient, and easy to implement and use.•Phase correction can significantly reduce the bias in multi-component relaxometry.
    Journal of Magnetic Resonance. 10/2014;
  • M. Soltanalian, P. Stoica
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    ABSTRACT: In this paper, perfect root-of-unity sequences (PRUS) with entries in $alpha_p = {x in {BBC} ,vert, x^p =1}$ (where $p$ is a prime) are studied. A lower bound on the number of distinct phases that are used in PRUS over $alpha_p$ is derived. We show that PRUS of length $L geq p(p-1)$ must use all phases in $alpha_p$. Certain conditions on the lengths of PRUS are derived. Showing that the phase values of PRUS must follow a given difference multiset property, we derive a set of equations (which we call the principal equations) that give possible lengths of a PRUS over $alpha_p$ together with their phase distributions. The usefulness of the principal equations is discussed, and guidelines for efficient construction of PRUS are provided. Through numerical results, contributions also are made to the current state-of-knowledge regarding the existence of PRUS. In particular, a combination of the developed ideas allowed us to numerically settle the problem of existence of PRUS with $(L, p)=(28, 7)$ within about two weeks—a problem whose solution (without using the ideas in this paper) would likely take more than three million years on a standard PC.
    IEEE Transactions on Signal Processing 10/2014; 62(20):5458-5470. · 2.81 Impact Factor
  • Mojtaba Soltanalian, Heng Hu, Petre Stoica
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    ABSTRACT: MIMO radar beamforming algorithms usually consist of a signal covariance matrix synthesis stage, followed by signal synthesis to fit the obtained covariance matrix. In this paper, we propose a radar beamforming algorithm (called Beam-Shape) that performs a single-stage radar transmit signal design; i.e. no prior covariance matrix synthesis is required. Beam-Shape׳s theoretical as well as computational characteristics, include (i) the possibility of considering signal structures such as low-rank, discrete-phase or low-PAR, and (ii) the significantly reduced computational burden for beampattern matching scenarios with large grid size. The effectiveness of the proposed algorithm is illustrated through numerical examples.
    Signal Processing 09/2014; 102:132–138. · 2.24 Impact Factor
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    ABSTRACT: In this paper, we deal with cognitive design of the transmit signal and receive filter optimizing the radar detection performance without affecting spectral compatibility with some licensed overlaid electromagnetic radiators. We assume that the radar is embedded in a highly reverberating environment and exploit cognition provided by Radio Environmental Map (REM), to induce spectral constraints on the radar waveform, by a dynamic environmental database, to predict the actual scattering scenario, and by an Electronic Support Measurement (ESM) system, to acquire information about hostile active jammers. At the design stage, we develop an optimization procedure which sequentially improves the Signal to Interference plus Noise Ratio (SINR). Moreover, we enforce a spectral energy constraint and a similarity constraint between the transmitted signal and a known radar waveform. At the analysis stage, we assess the effectiveness of the proposed technique to optimizing SINR while providing spectral coexistence.
    2014 IEEE Radar Conference (RadarCon); 05/2014
  • ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: In this paper, we study the problem of unimodular code design to improve the detection performance of statistical multiple-input multiple-output (MIMO) radar systems. To this end, we consider a system transmitting arbitrary unimodular signals and a discrete-time formulation of the problem. Due to the complicated form of the performance metric of the optimal detector, we resort to the Bhattacharyya distance for code design. We devise a novel method based on the majorization of matrix functions to obtain solutions to the constrained design problem. Simulation results show the effectiveness of the proposed method.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: In this paper, we study the problem of approaching peak periodic or aperiodic correlation bounds for complex-valued sets of sequences. In particular, novel algorithms based on alternating projections are devised to approach a given peak periodic or aperiodic correlation bound. Several numerical examples are presented to assess the tightness of the known correlation bounds as well as to illustrate the effectiveness of the proposed methods for meeting these bounds.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Mojtaba Soltanalian, Petre Stoica
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    ABSTRACT: In this paper, we propose a recursive method for finding Costas arrays that relies on a particular formation of Costas arrays from similar patterns of smaller size. By using such an idea, the proposed algorithm is able to dramatically reduce the computational burden (when compared to the exhaustive search), and at the same time, still can find all possible Costas arrays of given size. Similar to exhaustive search, the proposed method can be conveniently implemented in parallel computing. The efficiency of the method is discussed based on theoretical and numerical results.
    04/2014;
  • Prabhu Babu, Petre Stoica
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    ABSTRACT: In this note we show that the sparse estimation technique named Square-Root LASSO (SR-LASSO) is connected to a previously introduced method named SPICE. More concretely we prove that the SR-LASSO with a unit weighting factor is identical to SPICE. Furthermore we show via numerical simulations that the performance of the SR-LASSO changes insignificantly when the weighting factor is varied. SPICE stands for sparse iterative covariance-based estimation and LASSO for least absolute shrinkage and selection operator.
    Signal Processing. 02/2014; 95:10-14.
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    ABSTRACT: In this paper, we study the problem of meeting peak periodic or aperiodic correlation bounds for complex-valued sets of sequences. To this end, the Welch, Levenstein, and Exponential bounds on the peak inner-product of sequence sets are considered and used to provide compound peak correlation bounds in both periodic and aperiodic cases. The peak aperiodic correlation bound is further improved by using the intrinsic dimension deficiencies associated with its formulation. In comparison to the compound bound, the new aperiodic bound contributes an improvement of more than 35% for some specific values of the sequence length $n$ and set cardinality $m$ . We study the tightness of the provided bounds by using both analytical and computational tools. In particular, novel algorithms based on alternating projections are devised to approach a given peak periodic or aperiodic correlation bound. Several numerical examples are presented to assess the tightness of the provided correlation bounds as well as to illustrate the effectiveness of the proposed methods for meeting these bounds.
    IEEE Transactions on Signal Processing 01/2014; 62(5):1210-1220. · 2.81 Impact Factor
  • Source
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    ABSTRACT: In this paper, we study the joint design of Doppler robust transmit sequence and receive filter to improve the performance of an active sensing system dealing with signal-dependent interference. The signal-to-noise-plus-interference (SINR) of the filter output is considered as the performance measure of the system. The design problem is cast as a max-min optimization problem to robustify the system SINR with respect to the unknown Doppler shifts of the targets. To tackle the design problem, which belongs to a class of NP-hard problems, we devise a novel method (which we call DESIDE) to obtain optimized pairs of transmit sequence and receive filter sharing the desired robustness property. The proposed method is based on a cyclic maximization of SINR expressions with relaxed rank-one constraints, and is followed by a novel synthesis stage. We devise synthesis algorithms to obtain high quality pairs of transmit sequence and receive filter that well approximate the behavior of the optimal SINR (of the relaxed problem) with respect to target Doppler shift. Several numerical examples are provided to analyze the performance obtained by DESIDE.
    IEEE Transactions on Signal Processing 01/2014; 62(4):772-785. · 2.81 Impact Factor
  • Junli Liang, Luzhou Xu, Jian Li, Petre Stoica
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    ABSTRACT: Multistatic continuous active sonar (MCAS) systems involve the transmission and reception of multiple continuous probing sequences and can achieve significantly enhanced target detection and parameter estimation performance through exploiting the advantages of continuous illumination and spatial diversity. The main focuses and contributions of this paper are: 1) spectrally-contained continuous sequence sets with low correlation sidelobe levels are designed for the MCAS transmission so that the so-generated sequences meet the spectral containment restrictions and the weak correlations among the received echoes can be exploited to improve the target detection performance; and 2) a decentralized target parameter (position and velocity) determination method is investigated since its conventional centralized counterpart lacks robustness if there is no fusion center (FC) or the FC fails. This paper casts the target position determination problem based on the range measurements and the directions-of-arrival information (RMDI) as a set of decentralized optimization subproblems with consensus constraints imposed on the target position estimates of the receivers. Based on the alternating-direction method of multipliers (ADMM), we introduce the distributed position estimation algorithm to improve the local estimates of each receiver via local computation and information exchange with its neighbors. A similar method is also applied to obtain enhanced target velocity estimation. The effectiveness of the proposed MCAS signal processing techniques is verified using numerical examples.
    IEEE Transactions on Aerospace and Electronic Systems 01/2014; 50(1):285-299. · 1.30 Impact Factor
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    ABSTRACT: In this paper, we study the problem of code design to improve the detection performance of multi-static radar in the presence of clutter (i.e., a signal-dependent interference). To this end, we briefly present a discrete-time formulation of the problem as well as the optimal detector in the presence of Gaussian clutter. Due to the lack of analytical expression for receiver operation characteristic (ROC), code design based on ROC is not feasible. Therefore, we consider several popular information-theoretic criteria including Bhattacharyya distance, Kullback-Leibler (KL) divergence, J-divergence, and mutual information (MI) as design metrics. The code optimization problems associated with different information-theoretic criteria are obtained and cast under a unified framework. We propose two general methods based on Majorization-Minimization to tackle the optimization problems in the framework. The first method provides optimal solutions via successive majorizations whereas the second one consists of a majorization step, a relaxation, and a synthesis stage. Moreover, derivations of the proposed methods are extended to tackle the code design problems with a peak-to-average ratio power (PAR) constraint. Using numerical investigations, a general analysis of the coded system performance, computational efficiency of the proposed methods, and the behavior of the information-theoretic criteria is provided.
    IEEE Transactions on Signal Processing 11/2013; 61(21):5401-5416. · 2.81 Impact Factor
  • Petre Stoica, Prabhu Babu
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    ABSTRACT: This paper introduces a novel rule for model order estimation based on penalizing adatively the likelihood (PAL). The penalty term of PAL, which is data adaptive (as the name suggests), has several unique features: it is “small” (e.g. comparable to AIC penalty) for model orders, let us say n, less than or equal to the true order, denoted by n0, and it is “large” (e.g. of the same order as BIC penalty) for n>n0n>n0; furthermore this is true not only as the data sample length increases (which is the case most often considered in the literature) but also as the signal-to-noise ratio (SNR) increases (the harder case for AIC, BIC and the like); and this “oracle-like” behavior of PAL's penalty is achieved without any knowledge about n0. The paper presents a number of simulation examples to show that PAL has an excellent performance also in non-asymptotic regimes and compare this performance with that of AIC and BIC.
    Signal Processing. 11/2013; 93(11):2865–2871.
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    ABSTRACT: The balanced steady-state free precession (bSSFP) pulse sequence has shown to be of great interest due to its high signal-to-noise ratio efficiency. However, bSSFP images often suffer from banding artifacts due to off-resonance effects, which we aim to minimize in this article. We present a general and fast two-step algorithm for 1) estimating the unknowns in the bSSFP signal model from multiple phase-cycled acquisitions, and 2) reconstructing band-free images. The first step, linearization for off-resonance estimation (LORE), solves the nonlinear problem approximately by a robust linear approach. The second step applies a Gauss-Newton algorithm, initialized by LORE, to minimize the nonlinear least squares criterion. We name the full algorithm LORE-GN. We derive the Cramér-Rao bound, a theoretical lower bound of the variance for any unbiased estimator, and show that LORE-GN is statistically efficient. Furthermore, we show that simultaneous estimation of T1 and T2 from phase-cycled bSSFP is difficult, since the Cramér-Rao bound is high at common signal-to-noise ratio. Using simulated, phantom, and in vivo data, we illustrate the band-reduction capabilities of LORE-GN compared to other techniques, such as sum-of-squares. Using LORE-GN we can successfully minimize banding artifacts in bSSFP. Magn Reson Med, 2013. © 2013 Wiley Periodicals, Inc.
    Magnetic Resonance in Medicine 10/2013; · 3.27 Impact Factor
  • Petre Stoica, Prabhu Babu
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    ABSTRACT: Exponential signals occur in extremely diverse applications and estimation of their parameters is one of the basic problems in applied sciences. Nevertheless there are only a handful of methods for exponential analysis that are recommended in the literature, and even those methods have relatively mediocre performance in more difficult scenarios. In this paper we attempt to correct this situation by making use of a system identification approach. The proposed methodology, which we call EASI (Exponential Analysis via System Identification), is shown to have a satisfactory performance (i.e., high resolution and small statistical variability) for practical data lengths, and this not only for white measurement noise but also in cases with highly correlated noise (which were rarely considered in the previous literature).
    Digital Signal Processing 09/2013; 23(5):1565-1577. · 1.92 Impact Factor
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    ABSTRACT: In this paper, we introduce a fast computational frequency-domain approach for designing complementary sets of sequences. Following the basic idea of CAN-based algorithms, we propose an extension of the CAN algorithm to complementary sets of sequences (which we call CANARY). Moreover, modified versions of the proposed algorithm are derived to tackle the complementary set design problems in which low peak-to-average-power ratio (PAR), unimodular or phase-quantized sequences are of interest. Several numerical examples are provided to show the performance of CANARY.
    Signal Processing. 07/2013; 93(7):2096–2102.
  • Mojtaba Soltanalian, Petre Stoica
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    ABSTRACT: The NP-hard problem of optimizing a quadratic form over the unimodular vector set arises in radar code design scenarios as well as other active sensing and communication applications. To tackle this problem (which we call unimodular quadratic programming (UQP)), several computational approaches are devised and studied. A specialized local optimization scheme for UQP is introduced and shown to yield superior results compared to general local optimization methods. Furthermore, a \textbf{m}onotonically \textbf{er}ror-bound \textbf{i}mproving \textbf{t}echnique (MERIT) is proposed to obtain the global optimum or a local optimum of UQP with good sub-optimality guarantees. The provided sub-optimality guarantees are case-dependent and generally outperform the $\pi/4$ approximation guarantee of semi-definite relaxation. Several numerical examples are presented to illustrate the performance of the proposed method. The examples show that for cases including several matrix structures used in radar code design, MERIT can solve UQP efficiently in the sense of sub-optimality guarantee and computational time.
    IEEE Transactions on Signal Processing 03/2013; · 2.81 Impact Factor
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    ABSTRACT: Due to its long-standing importance, the problem of designing the receive filter and transmit sequence for clutter/interference rejection in active sensing has been studied widely in the last decades. In this letter, we propose a cyclic optimization of the transmit sequence and the receive filter. The proposed approach can handle arbitrary peak-to-average-power ratio (PAR) constraints on the transmit sequence, and can be used for large dimension designs (with ~ 103 variables) even on an ordinary PC.
    IEEE Signal Processing Letters 01/2013; 20(5):423-426. · 1.67 Impact Factor
  • Petre Stoica, Prabhu Babu
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    ABSTRACT: The Bayesian Information Criterion (BIC) is often presented in a form that is only valid in large samples and under a certain condition on the rate at which the Fisher Information Matrix (FIM) increases with the sample length. This form has been improperly used previously in situations in which the conditions mentioned above do not hold. In this correspondence, we describe the proper forms of BIC in several practically relevant cases that do not satisfy the above assumptions. In particular, we present a new form of BIC for high signal-to-noise ratio (SNR) cases. The conclusion of this study is that BIC remains one of the most successful existing rules for model order selection, if properly used.
    IEEE Transactions on Signal Processing 09/2012; 60(9):4956-4961. · 2.81 Impact Factor

Publication Stats

14k Citations
958.60 Total Impact Points

Institutions

  • 1970–2014
    • Uppsala University
      • • Department of Information Technology
      • • Division of Systems and Control
      Uppsala, Uppsala, Sweden
  • 2008–2011
    • KTH Royal Institute of Technology
      • School of Electrical Engineering (EE)
      Tukholma, Stockholm, Sweden
    • CTTC Catalan Telecommunications Technology Centre
      Barcino, Catalonia, Spain
  • 2010
    • Polytechnic University of Catalonia
      Barcino, Catalonia, Spain
  • 1994–2010
    • University of Florida
      • Department of Electrical and Computer Engineering
      Gainesville, FL, United States
    • University of Minnesota Duluth
      • Department of Electrical Engineering
      Duluth, Minnesota, United States
  • 2006
    • California Institute of Technology
      • Department of Electrical Engineering
      Pasadena, CA, United States
  • 2005
    • George Washington University
      • Department of Electrical & Computer Engineering
      Washington, D. C., DC, United States
  • 2004
    • University of California, Los Angeles
      Los Angeles, California, United States
    • KU Leuven
      • Department of Electrical Engineering (ESAT)
      Leuven, VLG, Belgium
  • 1999–2002
    • McMaster University
      • Department of Electrical and Computer Engineering
      Hamilton, Ontario, Canada
    • Stevens Institute of Technology
      • Department of Electrical & Computer Engineering
      Hoboken, NJ, United States
    • Ruhr-Universität Bochum
      Bochum, North Rhine-Westphalia, Germany
  • 1992–2002
    • Stanford University
      • Information Systems Laboratory
      Stanford, CA, United States
  • 2001
    • University of Illinois at Chicago
      • Department of Electrical and Computer Engineering
      Chicago, IL, United States
    • Tampere University of Technology
      • Signaalinkäsittelyn laitos
      Tampere, Western Finland, Finland
  • 1997–2001
    • Brigham Young University - Provo Main Campus
      • Department of Electrical and Computer Engineering
      Provo, UT, United States
  • 1994–1998
    • Chalmers University of Technology
      • Department of Signals and Systems
      Göteborg, Vaestra Goetaland, Sweden
  • 1987–1996
    • The Ohio State University
      • Department of Electrical and Computer Engineering
      Columbus, Ohio, United States
  • 1993–1995
    • University of Texas at Dallas
      • Department of Electrical Engineering
      Richardson, Texas, United States
    • Tel Aviv University
      • School of Electrical Engineering
      Tel Aviv, Tel Aviv, Israel
  • 1977–1994
    • Polytechnic University of Bucharest
      Bucureşti, Bucureşti, Romania
  • 1987–1990
    • Yale University
      • Department of Electrical Engineering
      New Haven, CT, United States
  • 1988
    • Technische Universiteit Eindhoven
      • Department of Electrical Engineering
      Eindhoven, North Brabant, Netherlands