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Selected Topics in Signal Processing, IEEE Journal of

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 (1)
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  • “Statistics 102” for Multisource-Multitarget Detection and Tracking

    Publication Year: 2013 , Page(s): 376 - 389
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 multisensor-multitarget detection and tracking problems. The paper describes how “multitarget integro-differential calculus” is used to extend conventional single-sensor, single-target formal Bayesian motion and measurement modeling to general tracking problems. Given such models, the paper describes the Bayes-optimal approach to multisensor-multitarget detection and tracking: the multisensor-multitarget recursive Bayes filter. Finally, it describes how multitarget calculus is used to derive principled statistical approximations of this optimal filter, such as PHD filters, CPHD filters, and multi-Bernoulli filters. 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 (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 collected by the tracking system, with non-cooperative targets present in the surveillance volume during the data acquisition. The algorithm relies on the particle filter implementation of the probability density hypothesis (PHD) filter to evaluate the likelihood of θ. Thus, the calibration algorithm, as a byproduct, also provides a multi-target state estimate. An application of the proposed algorithm to translational sensor bias estimation is presented in detail as an illustration. The resulting sensor-bias estimation method is applicable to asynchronous sensors and does not require prior knowledge of measurement-to-target associations. View full abstract»

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  • Robust Multi-Bernoulli Filtering

    Publication Year: 2013 , Page(s): 399 - 409
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 profile. Our solution is based on the multi-target multi-Bernoulli filter that adaptively learns non-homogeneous clutter intensity and detection probability while filtering. 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 (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 targets using acoustic amplitude sensors and a radio-frequency tomography sensor system. Our simulation study indicates that the CPHD filter provides promising tracking accuracy with reasonable computational requirements. 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 (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 maximization which is computationally intractable for most realistic problems. Additionally, the dimension of the multi-target state has to be determined before implementing the DP search. This is problematic when the number of targets is unknown. We make two contributions towards addressing these problems. Firstly, by factorizing the joint posterior density using the structure of MTT, an efficient DP-TBD algorithm is developed to approximately solve the joint maximization in a fast but accurate manner. Secondly, we propose a novel detection procedure such that the dimension of the multi-target state no longer needs be to pre-determined before the DP search. Our analysis indicates that the proposed algorithm could achieve a computational complexity which is almost linear to the number of processed frames and independent of the number of targets. Simulation results show that this algorithm can accurately estimate the number of targets and reliably track multiple targets even when targets are in proximity. View full abstract»

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  • Histogram-PMHT Unfettered

    Publication Year: 2013 , Page(s): 435 - 447
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 framework for targets with arbitrary appearance. The framework adopts a stochastic appearance model that describes the sensor response to each target and describes filters and smoothers for several example models. The article also demonstrates that H-PMHT can be interpreted as the decomposition of multi-target track-before-detect into decoupled single target track-before-detect using the notion of associated images. 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 (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 (MD-MHT) to effectively track multiple targets in such multiple-detection systems. The challenge for this tracker, which follows the multiple hypothesis framework, is to jointly resolve the measurement origin and measurement mode uncertainties. The proposed tracker solves this data association problem via an extension to the multiframe assignment algorithm. Its performance is demonstrated on a simulated over-the-horizon-radar multitarget tracking scenario, which confirms the effectiveness of this algorithm. 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 (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 scenarios, since it cannot handle multiple target-originated measurements per scan. The Joint Probabilistic Data Association Filter (JPDAF) uses multiple measurements from a single target per scan through a weighted measurement-to-track association. However, its fundamental assumption is still one-to-one. In order to rectify this shortcoming, this paper proposes a new algorithm, called the Multiple-Detection Joint Probabilistic Data Association Filter (MD-JPDAF) for multitarget tracking, which is capable of handling multiple detections from targets per scan in the presence of clutter and missed detection. The multiple-detection pattern, which can account for many-to-one measurement set-to-track association rather than one-to-one measurement-to-track association, is used to generate multiple detection association events. The proposed algorithm exploits all the available information from measurements by combinatorial association of events that are formed to handle the possibility of multiple measurements per scan originating from a target. The MD-JPDAF is applied to a multitarget tracking scenario with an OTHR, where multiple detections occur due to different propagation paths as a result of scattering from different ionospheric layers. Experimental results show that multiple-detection pattern based probabilistic data association improves the state estimation accuracy. Furthermore, the tracking performance of the proposed filter is compared against the Posterior Cramér-Rao Lower Bound (PCRLB), which is explicitly derived for the multiple-detection scenario with a single- target. 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 (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 extended target PHD filter in target and measurement numbers. A gamma Gaussian inverse Wishart mixture implementation, which is capable of estimating the target extents and measurement rates as well as the kinematic state of the target, is proposed, and it is compared to its PHD counterpart in a simulation study. The results clearly show that the CPHD filter has a more robust cardinality estimate leading to smaller OSPA errors, which confirms that the extended target CPHD filter inherits the properties of its point target counterpart. 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 (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 target tracks and alleviates the need for pruning, merging and clustering for the estimation of multi-target states. View full abstract»

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  • A CPHD Filter for Tracking With Spawning Models

    Publication Year: 2013 , Page(s): 496 - 507
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 case when the process model also includes target spawning. For this generalized filter, the cardinality prediction formula might become computationally intractable for general spawning models. However, when the cardinality distribution of the spawning targets is either Bernoulli or Poisson, we derive expressions that are practical and computationally efficient. Simulations show that the proposed filter responds faster to a change in target number due to spawned targets than the original CPHD filter. In addition, the performance of the filter, considering the optimal subpattern assignment (OSPA), is improved when having an explicit spawning model. View full abstract»

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  • Consensus CPHD Filter for Distributed Multitarget Tracking

    Publication Year: 2013 , Page(s): 508 - 520
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 problem. The effectiveness of the proposed approach is demonstrated via simulation experiments on realistic scenarios. 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 (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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, consistent optimal fusion, which does not double count information, can only be guaranteed for highly constrained network architectures which largely undermine the benefits of distributed fusion. In this paper, we develop a consistent approach for DMMT by combining a generalized version of Covariance Intersection, based on Exponential Mixture Densities (EMDs), with Random Finite Sets (RFS). We first derive explicit formulae for the use of EMDs with RFSs. From this, we develop expressions for the probability hypothesis density filters. This approach supports DMMT in arbitrary network topologies through local communications and computations. We implement this approach using Sequential Monte Carlo techniques and demonstrate its performance in simulations. 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 (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 smoothing for Bayes optimal exploitation of external knowledge,” F. Papi , Proc. 15th Int. Conf. Inf. Fusion, 2012) can be implemented by means of a SIR-PF. In practice, the SIR-PF suffers from depletion problems, which are further amplified by the Smoothing technique. Sequential MCMC methods represent an efficient alternative to the standard SIR-PF approach. Furthermore, by borrowing techniques from genetic algorithms, a fully parallelizable multitarget tracker can be defined. Such approach, i.e., named Interacting Population (IP)-MCMC-PF, was first introduced in “Multitarget tracking with interacting population-based MCMC-PF” (M Bocquel , Proc. 15th Int. Conf. Inf. Fusion, 2012). In this paper, we propose and analyze a combination of the KB-Smoother along with the IP-MCMC-PF. As will be shown, the combination of the two methods yields an improved track accuracy while mitigating the loss of particles diversity. Simulation analyses for single and multitarget tracking scenarios confirm the benefits of the proposed approach. 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 (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 formulae that define a Bayesian filter recursion. The novelty in this filter is that it provides a robust multi-object likelihood which is easily understood in the context of our starting assumptions. We present a particle/Gaussian mixture implementation of the filter, taking into consideration the challenges that SLAM presents over target tracking with stationary sensors, such as changing fields of view and a mixture of static and dynamic map features. Monte Carlo simulation results are given which demonstrate the filter's effectiveness with high measurement clutter and non-linear vehicle motion. 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 (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 of current research activity one of the most popular is the probability hypothesis density, commonly referred to as the PHD, in which the goal is estimation of object locations (unlabeled estimation) without concern for object identity (which is which). While it is relatively well understood in terms of its implementation, little is known about its performance and ultimate limits. This paper is focused on the characterization of PHD estimation performance for the static multitarget case, in the limiting regime where the number of sensors goes to infinity. It is found that the PHD asymptotically behaves as a mixture of Gaussian components, whose number is the true number of targets, and whose peaks collapse in the neighborhood of the classical maximum likelihood estimates, with a spread ruled by the Fisher information. Similar findings are obtained with reference to a naïve, two-step algorithm which first detects the number of targets, and then estimates their positions. 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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    Freely Available from IEEE
  • J-STSP call for special issue proposals

    Publication Year: 2013 , Page(s): 567
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    Freely Available from IEEE
  • 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
    Save to Project icon | Request Permissions | PDF file iconPDF (523 KB)  
    Freely Available from IEEE
  • Special issue on signal processing for large-scale mimo communications

    Publication Year: 2013 , Page(s): 569
    Save to Project icon | Request Permissions | PDF file iconPDF (406 KB)  
    Freely Available from IEEE
  • Special issue on signal processing for social networks

    Publication Year: 2013 , Page(s): 570
    Save to Project icon | Request Permissions | PDF file iconPDF (331 KB)  
    Freely Available from IEEE
  • IEEE Member digital library

    Publication Year: 2013 , Page(s): 571
    Save to Project icon | Request Permissions | PDF file iconPDF (1637 KB)  
    Freely Available from IEEE

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
Fernando Pereira
Instituto Superior Técnico