Data fusion and error reduction algorithms for sensor networks

Gorski, Jason; Wilson, Larry; Elhajj, Imad H.; Tan, Jindong

doi:10.1109/iros.2005.1545068

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Detection, classification and identification algorithms used in low level processing are based on future-based inference techniques, physical models and cognitive-base models [8]. Examples of these methods easily can be found in the literature based on parametric techniques such as classical inference of probability [9]- [12] or Bayesians inference [13]- [16]. Method based on physical models with estimation of Kalman Filtering can be found in [17]- [22] or least squares technique [23]- [26] and methods based on cognitive-based models can be reviewed in [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Intelligent sensor with data fusion to improve the care and management of water

Rivera-Mejia¹,

Seanez-Hernandez²,

Hernandez-Lopez³

et al. 2011

2011 IEEE International Instrumentation and Measurement Technology Conference

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The problem of the scarce of drinking water is increasing every day around the world and is required of new monitoring systems in order to improve the care and management of water. The intelligent sensors with function of data fusion are a good alternative to build intelligent monitoring systems. This paper presents a data fusion method based on least squares technique to design one intelligent sensor using a microcontroller of low computational capability. Three variables are used by the data fusion model to estimate the water consumption; the result is compared with a real value of water consumption. The intelligent sensor proposed is able to detect if the water consumption is normal or not and its performance was simulated using real data of water consumption of an Instituto Tecnológico de Chihuahua facility. Intelligent water consumption monitoring systems can be built for warning about improper water use and improve its care and management.

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Section: Introductionmentioning

confidence: 99%

Intelligent sensor with data fusion to improve the care and management of water

Rivera-Mejia¹,

Seanez-Hernandez²,

Hernandez-Lopez³

et al. 2011

2011 IEEE International Instrumentation and Measurement Technology Conference

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“…Multiple sensor data fusion has been an intensive research topic [8][9][10][11][12][13][14]. Data fusion in large wireless sensor networks is getting more attention recently [15][16][17]. Data fusion in primitive sensor networks is different from that in the conventional sensor networks [18] since each individual sensor is not capable of locating one or more targets.…”

Section: Introductionmentioning

confidence: 99%

Real-time target tracking simulations in large disparate sensor networks

Lin

Rushing

Graves

et al. 2007

Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007

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Real-time target tracking in large disparate sensor networks has been simulated with a parallelized search based data fusion algorithm using a simulated annealing approach. The networks are composed of large numbers of low fidelity binary and bearing-only sensors, and small numbers of high fidelity position sensors over a large region. The primitive sensors provide limited information, not sufficient to locate targets; the position sensors can report both range and direction of the targets. Target positions are determined through fusing information from all types of sensors. A score function, which takes into account the fidelity of sensors of different types, is defined and used as the evaluation function for the optimization search. The fusion algorithm is parallelized using spatial decomposition so that the fusion process can finish before the arrival of the next set of sensor data. A series of target tracking simulations are performed on a Linux cluster with communication between nodes facilitated by the Message Passing Interface (MPI). The probability of detection (POD), false alarm rate (FAR), and average deviation (AVD) are used to evaluate the network performance. The input target information used for all the simulations is a set of target track data created from a theater level air combat simulation.

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