Sequential ML for Multistatic Sonar Tracking

Blanding, Wayne; Willett, Peter; Coraluppi, Stefano

doi:10.1109/oceanse.2007.4302356

Cited by 16 publications

(13 citation statements)

References 14 publications

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“…At each phase, a typical prior knowledge is used: at the acoustic object tracking stage, the continuity of the echoes; at the grouping phase, the properties of target's dimensions, velocity, and standard deviation of its body parts in space and time; and at the classification phase, the kinematic features, and the number of body parts are used. range, intensity and correlations are used to detect acoustic objects and track their location over time using a variant of sequential MLE object tracking [28]. These acoustic objects, referred as objects in compare to target or clatter, are subobjects of a target, or of a clatter in the medium.…”

Section: Discussionmentioning

confidence: 99%

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Noncontact Wideband Sonar for Human Activity Detection and Classification

2014

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This paper suggests using a wideband sonar system to detect and classify human activity in indoor environment. The high bandwidth enables precise tracking of body parts, and its enhanced correlation properties can be used to distinguish between human and nonhuman objects. Maximal Likelihood (ML) criterions to derive kinematic features and processing methods to estimate the subject activity level and activity type were derived and tailored to the wideband sonar. For tracking and association of the echoes reflected from the different body part, an efficient approximation of the sequential ML estimator was derived in the natural time-space domain, which eases the exploitation of the a priori knowledge about the human subject target. For classification of the activity, a weighted two level nested k-nearest neighbor classifier was applied on only four kinematic features. A set of experiments with five subjects, performing three different activity types of standing, walking, and swinging upper limbs, was carried out in a typical indoor environment. The proposed technology has managed to classify well the different activity types and demonstrated the potential of this technology for continuous assessment of various kinematic features of humans in indoor environment with reduced costs, under any light, smoke, or humidity conditions. This can be useful for instance for monitoring patients at home, and for detecting intruders.

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Section: Discussionmentioning

confidence: 99%

“…The tracking problem (5) can be solved using the sequential MLE [28] for all (acoustic) objects in the medium. This can be implemented with a Viterbi algorithm [32].…”

Section: ) Sequential Mle Approximationmentioning

confidence: 99%

Noncontact Wideband Sonar for Human Activity Detection and Classification

2014

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“…Subsequently, in [4]- [6], it was applied in a multistatic active application, which is how we currently employ it. The assumptions used to develop the ML-PDA LR are [7], [8] as follows.…”

Section: A Likelihood Ratiosmentioning

confidence: 99%

“…For any arbitrary number of measurements , this equation multiplied out yields (4) where the terms represent , the target-centered Gaussian terms. It should also be noted that the amplitude distribution ratios have been left out of the above equation; including them would not affect the following discussion.…”

Section: B Relationship Between Ml-pda and Ml-pmhtmentioning

confidence: 99%

ML–PDA and ML–PMHT: Comparing Multistatic Sonar Trackers for VLO Targets Using a New Multitarget Implementation

Schoenecker

Willett

Bar‐Shalom

2014

IEEE J. Oceanic Eng.

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The maximum-likelihood probabilistic data association (ML-PDA) tracker and the maximum-likelihood probabilistic multihypothesis (ML-PMHT) tracker are tested in their capacity as algorithms for very low observable (VLO) targets (meaning 6-dB postsignal processing or even less) and are then applied to five synthetic benchmark multistatic active sonar scenarios featuring multiple targets, multiple sources, and multiple receivers. Both methods end up performing well in situations where there is a single target or widely spaced targets. However, ML-PMHT has an inherent advantage over ML-PDA in that its likelihood ratio (LR) has a simple multitarget formulation, which allows it to be implemented as a true multitarget tracker. This formulation, presented here for the first time, gives ML-PMHT superior performance for instances where multiple targets are closely spaced with similar motion dynamics.Index Terms-Bistatic, expectation maximization (EM), low observable, maximum likelihood, maximum-likelihood probabilistic data association (ML-PDA), maximum-likelihood probabilistic multihypothesis (ML-PMHT), multistatic, multitarget, multitarget ML-PMHT, sonar, tracking.

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“…It was first developed in a passive narrowband application in [10]. Subsequently, in [14], [15], and [3] it was expanded to a bistatic active application, which is how we currently employ it. The assumptions that go into ML-PDA and the development of the likelihood function are well documented in [11] and [4].…”

Section: Ml-pda Trackermentioning

confidence: 99%

Maximum likelihood probabilistic data association tracker applied to bistatic sonar data sets

Schoenecker

Willett²,

Bar‐Shalom³

2010

SPIE Proceedings

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In the early 1990's, the Maximum Likelihood Probabilistic Data Association (ML-PDA) tracker was developed in a passive sonar framework, and subsequent research has shown it to be effective for tracking very low SNR targets. This was done with both active and passive sonar, for targets that have some given type of deterministic motion. Recent work has focused on applying ML-PDA to bistatic sonar data. Here, we apply ML-PDA in a sliding window implementation to three bistatic data sets used by the MSTWG (Multistatic Tracking Working Group): the SEABAR 2007 data set, the TNO Blind 2008 data set, and a new blind data set provided by Metron in 2009.

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Sequential ML for Multistatic Sonar Tracking

Abstract: We apply the sequential (as opposed to batch) ML-PDA to several data-sets from the MSTWG (multi-static tracking working group) library: from NURC, ARL/UT and TNO.

Cited by 16 publications

References 14 publications

Noncontact Wideband Sonar for Human Activity Detection and Classification

Noncontact Wideband Sonar for Human Activity Detection and Classification

ML–PDA and ML–PMHT: Comparing Multistatic Sonar Trackers for VLO Targets Using a New Multitarget Implementation

Maximum likelihood probabilistic data association tracker applied to bistatic sonar data sets

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