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IEEE Journal of Selected Topics in Signal Processing

Issue 3 • Date June 2013

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Displaying Results 1 - 25 of 27
  • Table of Contents

    Publication Year: 2013, Page(s):C1 - C4
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  • IEEE Journal of Selected Topics in Signal Processing publication information

    Publication Year: 2013, Page(s): C2
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  • Introduction to the issue on multitarget tracking

    Publication Year: 2013, Page(s):373 - 375
    Cited by:  Papers (4)
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  • “Statistics 102” for Multisource-Multitarget Detection and Tracking

    Publication Year: 2013, Page(s):376 - 389
    Cited by:  Papers (25)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2568 KB) | HTML iconHTML

    This tutorial paper summarizes the motivations, concepts and techniques of finite-set statistics (FISST), a system-level, “top-down,” direct generalization of ordinary single-sensor, single-target engineering statistics to the realm of multisensor, multitarget detection and tracking. Finite-set statistics provides powerful new conceptual and computational methods for dealing with mul... View full abstract»

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  • Calibration of Multi-Target Tracking Algorithms Using Non-Cooperative Targets

    Publication Year: 2013, Page(s):390 - 398
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2302 KB) | HTML iconHTML

    Tracking systems are based on models, in particular, the target dynamics model and the sensor measurement model. In most practical situations the two models are not known exactly and are typically parametrized by an unknown random vector θ. The paper proposes a Bayesian algorithm based on importance sampling for the estimation of the static parameter θ. The input are measurements col... View full abstract»

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  • Robust Multi-Bernoulli Filtering

    Publication Year: 2013, Page(s):399 - 409
    Cited by:  Papers (20)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3316 KB) | HTML iconHTML

    In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and detection probability profile are of critical importance. Significant mismatches in clutter and detection model parameters results in biased estimates. In this paper we propose a multi-target filtering solution that can accommodate non-linear target models and an unknown non-homogeneous clutter and detection p... View full abstract»

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  • Computationally-Tractable Approximate PHD and CPHD Filters for Superpositional Sensors

    Publication Year: 2013, Page(s):410 - 420
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2519 KB) | HTML iconHTML

    In this paper we derive computationally-tractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary particle filter approximations. As an example, we present simulation experiments that involve tracking multiple target... View full abstract»

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  • An Efficient Multi-Frame Track-Before-Detect Algorithm for Multi-Target Tracking

    Publication Year: 2013, Page(s):421 - 434
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2856 KB) | HTML iconHTML

    This paper considers the multi-target tracking (MTT) problem through the use of dynamic programming based track-before-detect (DP-TBD) methods. The usual solution of this problem is to adopt a multi-target state, which is the concatenation of individual target states, then search the estimate in the expanded multi-target state space. However, this solution involves a high-dimensional joint maximiz... View full abstract»

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  • Histogram-PMHT Unfettered

    Publication Year: 2013, Page(s):435 - 447
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2430 KB) | HTML iconHTML

    The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to track-before-detect. The original implementations of H-PMHT dealt with Gaussian shaped targets with fixed or known extent. More recent applications have addressed other special cases of the target shape. This article reviews these recent extensions and consolidates them into a new unified fram... View full abstract»

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  • A Multiple Hypothesis Tracker for Multitarget Tracking With Multiple Simultaneous Measurements

    Publication Year: 2013, Page(s):448 - 460
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2542 KB) | HTML iconHTML

    Typical multitarget tracking systems assume that in every scan there is at most one measurement for each target. In certain other systems such as over-the-horizon radar tracking, the sensor can generate resolvable multiple detections, corresponding to different measurement modes, from the same target. In this paper, we propose a new algorithm called multiple detection multiple hypothesis tracker (... View full abstract»

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  • A Multiple-Detection Joint Probabilistic Data Association Filter

    Publication Year: 2013, Page(s):461 - 471
    Cited by:  Papers (22)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2555 KB) | HTML iconHTML

    Most conventional target tracking algorithms assume that a target can generate at most one measurement per scan. However, there are tracking problems where this assumption is not valid. For example, multiple detections from a target in a scan can arise due to multipath propagation effects as in the over-the-horizon radar (OTHR). A conventional multitarget tracking algorithm will fail in these scen... View full abstract»

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  • An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation

    Publication Year: 2013, Page(s):472 - 483
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2879 KB) | HTML iconHTML

    This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has been derived by Mahler, and different implementations have been proposed recently. To achieve better estimation performance this work relaxes the Poisson assumptions of the ex... View full abstract»

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  • Mean-Field PHD Filters Based on Generalized Feynman-Kac Flow

    Publication Year: 2013, Page(s):484 - 495
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4055 KB) | HTML iconHTML

    We discuss a connection between spatial branching processes and the PHD recursion based on conditioning principles for Poisson Point Processes. The branching process formulation gives a generalized Feynman-Kac systems interpretation of the PHD filtering equations, which enables the derivation of mean-field implementations of the PHD filter. This approach provides a principled means for obtaining t... View full abstract»

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  • A CPHD Filter for Tracking With Spawning Models

    Publication Year: 2013, Page(s):496 - 507
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2607 KB) | HTML iconHTML

    In some applications of multi-target tracking, appearing targets are suitably modeled as spawning from existing targets. However, in the original formulation of the cardinalized probability hypothesis density (CPHD) filter, this type of model is not supported; instead appearing targets are modeled by spontaneous birth only. In this paper we derive the necessary equations for a CPHD filter for the ... View full abstract»

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  • Consensus CPHD Filter for Distributed Multitarget Tracking

    Publication Year: 2013, Page(s):508 - 520
    Cited by:  Papers (21)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3379 KB) | HTML iconHTML

    The paper addresses distributed multitarget tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The contribution has been to develop a novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filter that provides a fully distributed, scalable and computationally efficient solution to the ... View full abstract»

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  • Distributed Fusion of PHD Filters Via Exponential Mixture Densities

    Publication Year: 2013, Page(s):521 - 531
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2561 KB) | HTML iconHTML

    In this paper, we consider the problem of Distributed Multi-sensor Multi-target Tracking (DMMT) for networked fusion systems. Many existing approaches for DMMT use multiple hypothesis tracking and track-to-track fusion. However, there are two difficulties with these approaches. First, the computational costs of these algorithms can scale factorially with the number of hypotheses. Second, consisten... View full abstract»

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  • Multitarget Tracking With Multiscan Knowledge Exploitation Using Sequential MCMC Sampling

    Publication Year: 2013, Page(s):532 - 542
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2551 KB) | HTML iconHTML

    Exploitation of external knowledge through constrained filtering guarantees improved performance. In this paper we show how multiscan processing of such information further enhances the track accuracy. This can be achieved using a Fixed-Lag Smoothing procedure, and a proof of improvement is given in terms of entropy reduction. Such multiscan algorithm, i.e., named KB-Smoother (“Fixed-lag sm... View full abstract»

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  • SLAM With Dynamic Targets via Single-Cluster PHD Filtering

    Publication Year: 2013, Page(s):543 - 552
    Cited by:  Papers (22)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2147 KB) | HTML iconHTML

    This paper presents the first algorithm for simultaneous localization and mapping (SLAM) that can estimate the locations of both dynamic and static features in addition to the vehicle trajectory. We model the feature-based SLAM problem as a single-cluster process, where the vehicle motion defines the parent, and the map features define the daughter. Based on this assumption, we obtain tractable fo... View full abstract»

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  • Asymptotic Efficiency of the PHD in Multitarget/Multisensor Estimation

    Publication Year: 2013, Page(s):553 - 564
    Cited by:  Papers (20)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3213 KB) | HTML iconHTML

    Tracking an unknown number of objects is challenging, and often requires looking beyond classical statistical tools. When many sensors are available the estimation accuracy can reasonably be expected to improve, but there is a concomitant rise in the complexity of the inference task. Nowadays, several practical algorithms are available for multitarget/multisensor estimation and tracking. In terms ... View full abstract»

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  • IEEE Journal of Selected Topics in Signal Processing information for authors

    Publication Year: 2013, Page(s):565 - 566
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  • J-STSP call for special issue proposals

    Publication Year: 2013, Page(s): 567
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  • Call for papers: IEEEE Signal Processing Society, IEEE Journal of Selected Topics in Signal Processing Special Issue on Perception Inspired Video Processing

    Publication Year: 2013, Page(s): 568
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  • Special issue on signal processing for large-scale mimo communications

    Publication Year: 2013, Page(s): 569
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  • Special issue on signal processing for social networks

    Publication Year: 2013, Page(s): 570
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  • IEEE Member Digital Library

    Publication Year: 2013, Page(s): 571
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Aims & Scope

The Journal of Selected Topics in Signal Processing (J-STSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society including the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief

Shrikanth (Shri) S. Narayanan
Viterbi School of Engineering 
University of Southern California
Los Angeles, CA 90089 USA
shri@sipi.usc.edu