Sensor fusion for target detection and tracking

Bonneau, Robert J.; Ertan, S.; Perretta, James; Shaw, Kailash; Rahn, Brian J.

doi:10.1109/aipr.2002.1182276

Cited by 4 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, given that an adversary diver intrusion is a rare event, the prior probabilities are difficult to estimate. Non-Bayesian approaches, e.g., the Neyman-Pearson test and a generalized likelihood ratio test (GLRT), do not require the prior probability distribution for the diver's location and treat false positives and false negatives separately based on the probability distributions of S under H 0 and H 1 ; see McDonough and Whalen (1995), Bonneau et al (2002), Clouqueur et al (2002). For example, the Neyman-Pearson test minimizes the false negative probability (probability of a successful intrusion) subject to a constraint on the false positive probability (false alarm).…”

Section: Diver Detection Test and Probability Of Detectionmentioning

confidence: 99%

Stochastic Optimization of Sensor Placement for Diver Detection

Molyboha

Zabarankin

2012

Operations Research

View full text Add to dashboard Cite

A comprehensive framework for diver detection by a hydrophone network in an urban harbor is presented. It includes a signal processing algorithm and a diver detection test and formulates optimal hydrophone placement as a two-stage stochastic optimization problem with respect to different scenarios of underwater noise. The signal processing algorithm identifies sound intensity peaks associated with diver breathing and outputs a diver number measuring the likelihood of diver presence, whereas the diver detection test aggregates the diver numbers obtained from the hydrophones in a linear statistic and optimizes the statistic's coefficients and a detection threshold for each noise scenario. The serial dependence of the diver numbers on a short time scale (several detection periods) is modeled by a hidden Markov chain, and finding the worst-case diver's trajectory for each hydrophone placement and noise scenario is reduced to a linear programming problem. The framework is tested in numerical experiments with real-life data for circular and elliptic hydrophone placements and is shown to be superior to a deterministic energy-based approach.

show abstract

Section: Diver Detection Test and Probability Of Detectionmentioning

confidence: 99%

Stochastic Optimization of Sensor Placement for Diver Detection

Molyboha

Zabarankin

2012

Operations Research

View full text Add to dashboard Cite

show abstract

“…Therefore, by combining the data from neighboring sensors, also referred to as sensor fusion, more target information can be obtained than is possible from an individual sensor, and high-resolution tracking can be achieved [5]. While some of the existing schemes monitor targets by using all nearby sensors, these sensors may suffer from high power consumption.…”

Section: Introductionmentioning

confidence: 98%

A Simple Multi-point Surveillance Scheme of a Moving Target for Wireless Sensor Networks

Tsukamoto

Ueda

Tamura

et al. 2007

2007 IEEE Globecom Workshops

View full text Add to dashboard Cite

We consider the problem of moving target tracking in a wireless sensor network (WSN) in which the capability of each sensor has relatively limited performance enabling us to construct large-scale systems/networks at reduced cost. In order to achieve moving target tracking with high resolution, the following two requirements must be satisfied: (I) Multipoint surveillance with the appropriate number of sensors, which depends on the characteristics of the applications and user requirements, and (II) Dynamic switching and quick decisionmaking of monitoring sensors in response to the movement of the target. In the present paper, we propose a simple and robust multi-point surveillance scheme for moving targets in WSNs. Through simulation experiments, we demonstrate that the proposed scheme can quickly and precisely assign monitoring sensors on a WSN of any density with low power consumption in response to the movement of the target, thereby maintaining high tracking probability even for high-speed targets.

show abstract

“…Most of the published work on networked radars, not necessarily phased arrays, focus on how to combine observations from different radars on the same target to increase sensitivity and/or accuracy, for example [5][6][7][8]. Work presented in [9] describes the performance of netted radar in terms of sensitivity and ambiguity function and develops a set of software tools to assess netted radar sensitivity and ambiguity properties in two and three dimensions.…”

Section: Introductionmentioning

confidence: 99%