Performance of search via track-before-detect for distributed sensor networks

Wettergren, Thomas A.

doi:10.1109/taes.2008.4517006

Cited by 52 publications

(64 citation statements)

References 13 publications

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“…In CPS, the alarming record set R a is a subset of the entire dataset R. Localizing object's positions from sensor's readings has been a hot research topic in the past decade. Many stateof-the-art methods have been proposed in [2], [12], [4]. A common applied work is the sampling approach.…”

Section: Background and Preliminariesmentioning

confidence: 99%

Tru-Alarm: Trustworthiness Analysis of Sensor Networks in Cyber-Physical Systems

Tang

Kim

et al. 2010

2010 IEEE International Conference on Data Mining

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Abstract-A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational) components to form a situation-integrated analytical system that responds intelligently to dynamic changes of the real-world scenarios. One key issue in CPS research is trustworthiness analysis of the observed data: Due to technology limitations and environmental influences, the CPS data are inherently noisy that may trigger many false alarms. It is highly desirable to sift meaningful information from a large volume of noisy data. In this paper, we propose a method called Tru-Alarm which finds out trustworthy alarms and increases the feasibility of CPS. Tru-Alarm estimates the locations of objects causing alarms, constructs an object-alarm graph and carries out trustworthiness inferences based on linked information in the graph. Extensive experiments show that Tru-Alarm filters out noises and false information efficiently and guarantees not missing any meaningful alarms.

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Section: Background and Preliminariesmentioning

confidence: 99%

Tru-Alarm: Trustworthiness Analysis of Sensor Networks in Cyber-Physical Systems

Tang

Kim

et al. 2010

2010 IEEE International Conference on Data Mining

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“…Then, Xd = 1x EX Ixj-XT(t) 1: Rd,t e (to,t o + At)J. (2) Observe that Xd = {xi E XsI Xi-(XT(tO)+(t -t0)-VT)I1 Rd,t E (to,t o +At)},…”

Section: Search Theory In Distributed Sensor Network Applicationsmentioning

confidence: 99%

“…The consequence on grid cell density within the sensor detection region and within the evaluation region is illustrated in figure 16. The total number of grid points required, that is Ntot = (2Nmax) 2 , is affected accordingly for each case. 50d N_ 04 3 00, Ax =210…”

Section: Comparison Against a Baseline Pssmentioning

confidence: 99%

Search Path Evaluation Incorporating Object Placement Structure

Baylog¹,

Wettergren²

2007

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“…Our model for the detection performance of a multistatic sensor field (or, more accurately, the search effectiveness of the field, with successful search defined as a sequence of target detections) is an extension of the model developed in [2] for passive sensor fields. It is also similar in spirit to, and shares common elements with, the Multistatic Performance Prediction Methodology (MPPM) of Bowen and Mitnick [3].…”

Section: Introductionmentioning

confidence: 99%

“…), and to plan the specific deployment of a multistatic field (i.e., source and receiver locations), as described in [4]. In the next section, we review the analytical framework developed in [2] for assessing the performance of a distributed passive sensor network searching for a moving target. Then, in Section III, the methodology is extended to the multistatic problem through a sequence of approximations.…”

Section: Introductionmentioning

confidence: 99%

Search performance prediction for multistatic sensor fields

Walsh

Wettergren

2008

Oceans 2008

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Abstract-An existing search performance model for distributed passive sensor networks is extended to multistatic sensor networks comprised of active sources and passive receivers. The model provides an upper bound on the detection range of a receiver in the field, and uses this bound to compute the expected probability of successful search for a given target track. This expected probability depends on the numbers of sources and receivers in the field, their location distribution functions, and the location and orientation of the track. For uniformly (randomly) distributed sources and receivers, the distribution function for the upper bound on receiver detection range can be computed analytically in closed form. We show that the resulting estimates of probability of successful search for uniformly distributed multistatic fields is a reasonable approximation to more accurate values obtained via Monte Carlo simulation for various numbers of sources and receivers, and for various numbers of required detections. We conclude with a discussion of how this model may be used in designing and planning multistatic systems for undersea surveillance.

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Performance of search via track-before-detect for distributed sensor networks

Cited by 52 publications

References 13 publications

Tru-Alarm: Trustworthiness Analysis of Sensor Networks in Cyber-Physical Systems

Tru-Alarm: Trustworthiness Analysis of Sensor Networks in Cyber-Physical Systems

Search Path Evaluation Incorporating Object Placement Structure

Search performance prediction for multistatic sensor fields

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