A PMHT algorithm for active sonar

Hempel, Christian; Doran, Sheri L.

doi:10.1117/12.542112

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Sensor model (9) over the targets, the maximization problem decomposes into S independent problems: one summand per target. Let us denote one of the summands by Q s (X; X l ).…”

Section: Maximizing the Q-function (M-step)mentioning

confidence: 99%

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On Sequential Track Extraction within the PMHT Framework

Wieneke

Koch

2007

EURASIP J. Adv. Signal Process.

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Tracking multiple targets in a cluttered environment is a challenging task. Probabilistic multiple hypothesis tracking (PMHT) is an efficient approach for dealing with it. Essentially PMHT is based on expectation-maximization for handling with association conflicts. Linearity in the number of targets and measurements is the main motivation for a further development and extension of this methodology. In particular, the problem of track extraction and deletion is apparently not yet satisfactorily solved within this framework. A sequential likelihood-ratio (LR) test for track extraction has been developed and integrated into the framework of traditional Bayesian multiple hypothesis tracking by Günter van Keuk in 1998. As PMHT is a multiscan approach as well, it also has the potential for track extraction. In this paper, an analogous integration of a sequential LR test into the PMHT framework is proposed. We present an LR formula for track extraction and deletion using the PMHT update formulae. The LR is thus a by-product of the PMHT iteration process, as PMHT provides all required ingredients for a sequential LR calculation. Therefore, the resulting update formula for the sequential LR test affords the development of track-before-detect algorithms for PMHT. The approach is illustrated by a simple example.

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“…Sensor model (9) over the targets, the maximization problem decomposes into S independent problems: one summand per target. Let us denote one of the summands by Q s (X; X l ).…”

Section: Maximizing the Q-function (M-step)mentioning

confidence: 99%

“…The most important task within a track management system is the choice of an appropriate test function for track candidates [7,8]. Some authors [9] use statistical hypothesis testing outside PMHT to determine whether a track is true or false. Target visibility is an approach published in [7,10,11].…”

Section: Introductionmentioning

confidence: 99%

On Sequential Track Extraction within the PMHT Framework

Wieneke

Koch

2007

EURASIP J. Adv. Signal Process.

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“…The Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm developed by Streit and Luginbuhl [1] is a batch technique that uses the Expectation-Maximization (EM) algorithm to obtain MAP estimates of the sequence of target states. The application considered here often produces a large amount of clutter and PMHT has been successfully demonstrated by one of the authors on other active sonar data containing clutter [16]. Therefore, the technical approach that guided this effort was to leverage NUWC's expertise in the PMHT algorithm and apply an existing implementation (with appropriate modifications) to the data provided by ARL/UT.…”

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confidence: 99%

Multi-Target Tracking for High Frequency Active Systems

Hempel¹,

Greineder²

2006

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The purpose of the effort reported here is to investigate modern multi-target tracking algorithms for high frequency active applications. High frequency active sensor systems are currently being evaluated by the Navy to meet the Sea Power 21 Sea Shield objectives for force protection and port security. These systems have elementary baseline tracking capabilities and could benefit from incorporating an advanced acoustic multi-target tracking algorithm designed for distributed active sensors. The incorporation of an improved tracking capability is aimed at reducing the high rate of false tracks being reported during system testing. This effort used active measurements from prototype actve sonar sensors to demonstrate true multi-target tracking on structured test data provided by ARL/UT, assess overall tracking performance and identify areas requiring algorithm improvements. APPROACH The limited overall scope of this investigation required focusing on a single tracking method that was likely to demonstrate improvement over an existing baseline tracker. Several different tracking methods were considered for this study: Bayesian (e.g., particle filter), recursive (e.g., Kalman filter), and batch methods (e.g., Multi-Hypothesis Tracking and Probabilistic Multi-Hypothesis Tracking). The prototype active sonar system and associated baseline processing chain considered in this study is capable of producing high resolution target detections at a high rate relative to the expected target dynamics. Consequently, Bayesian methods were not investigated because they are best suited to applications involving highly non-linear or non-Gaussian state or process equations. Moreover, the baseline tracker is recursive and hence batch type tracking methods were made the focus of this study. Multi-Hypothesis Tracking (MHT) is a batch type tracking method that, under ideal conditions, enumerates all possible data association hypotheses and produces maximum likelihood estimates of the target track. For situations containing a significant amount of clutter or false detections the computational burden of MHT may require limiting the number of data association hypotheses. This modification makes the algorithm and implementation much more complicated and sacrifices any Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply w...

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A novel multiple maneuvering targets tracking algorithm with data association and track management

Zhang

2013

Int. J. Control Autom. Syst.

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A PMHT algorithm for active sonar

Cited by 7 publications

References 8 publications

On Sequential Track Extraction within the PMHT Framework

On Sequential Track Extraction within the PMHT Framework

Multi-Target Tracking for High Frequency Active Systems

A novel multiple maneuvering targets tracking algorithm with data association and track management

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