Cooperative Group Localization Based on Factor Graph for Next-Generation Networks

In ill-conditioned communication environment, multiple target localization is of important practical significance. The cooperative group localization (CGL) model was firstly put forward, which has verified the effectiveness of localization performance gain and simultaneous multiple target localization in ill conditions. However, there exist two inherent difficulties: the strict demand for CGL topology and the high complexity. By the rational use of information to relax restrictions on topology and by dividing the complex problem into some simple local ones, the factor graph (FG) together with the sum-product algorithm is a perfect candidate for the problems above. In order to solve the two problems, we propose the weighted FG-based CGL (WFG-CGL) algorithm which incorporates the optimal weights based on the information reliability. In order to further reduce the complexity, we propose the low-complexity FG-based CGL (LCFG-CGL) algorithm. The Cramer-Rao lower bound (CRLB) of the localization error in CGL is first derived. Theoretical analysis and numerical results indicate that the proposed algorithms not only perform better in relaxing CGL topology requirement, but also enjoy high localization accuracy under low complexity in comparison with the existing CGL algorithm.

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Target localization accuracy and multiple target localization: Tradeoff in MIMO radars

2008 42nd Asilomar Conference on Signals, Systems and Computers ◽

10.1109/acssc.2008.5074479 ◽

2008 ◽

Cited By ~ 3

Author(s):

Hana Godrich ◽

Alexander M. Haimovich ◽

Rick S. Blum

Keyword(s):

Target Localization ◽

Multiple Target ◽

Localization Accuracy

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Role of pinnae and head movements in localizing pure tones 1The authors would like to thank Mr. Remi Humbert for implementing the experiment in Turbo Pascal and for his further assistance.

Swiss Journal of Psychology ◽

10.1024//1421-0185.58.3.170 ◽

1999 ◽

Vol 58 (3) ◽

pp. 170-179 ◽

Cited By ~ 4

Author(s):

Barbara S. Muller ◽

Pierre Bovet

Keyword(s):

Sound Source ◽

Head Movement ◽

Movement Analysis ◽

Head Movements ◽

Sound Sources ◽

Localization Accuracy ◽

Sound Effects ◽

Posterior Quadrant ◽

Localization Performance ◽

Pure Tones

Twelve blindfolded subjects localized two different pure tones, randomly played by eight sound sources in the horizontal plane. Either subjects could get information supplied by their pinnae (external ear) and their head movements or not. We found that pinnae, as well as head movements, had a marked influence on auditory localization performance with this type of sound. Effects of pinnae and head movements seemed to be additive; the absence of one or the other factor provoked the same loss of localization accuracy and even much the same error pattern. Head movement analysis showed that subjects turn their face towards the emitting sound source, except for sources exactly in the front or exactly in the rear, which are identified by turning the head to both sides. The head movement amplitude increased smoothly as the sound source moved from the anterior to the posterior quadrant.

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Modeling the City Distribution System Reliability with Bayesian Networks to Identify Influence Factors

Scientific Programming ◽

10.1155/2016/7109235 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Hao Zhang ◽

Liyu Zhu ◽

Shensi Xu

Keyword(s):

Bayesian Networks ◽

Bayesian Network ◽

System Reliability ◽

Distribution System ◽

Complex Problem ◽

Practical Significance ◽

Correlation Degree ◽

Urban Distribution ◽

Distribution System Reliability ◽

The City

Under the increasingly uncertain economic environment, the research on the reliability of urban distribution system has great practical significance for the integration of logistics and supply chain resources. This paper summarizes the factors that affect the city logistics distribution system. Starting from the research of factors that influence the reliability of city distribution system, further construction of city distribution system reliability influence model is built based on Bayesian networks. The complex problem is simplified by using the sub-Bayesian network, and an example is analyzed. In the calculation process, we combined the traditional Bayesian algorithm and the Expectation Maximization (EM) algorithm, which made the Bayesian model able to lay a more accurate foundation. The results show that the Bayesian network can accurately reflect the dynamic relationship among the factors affecting the reliability of urban distribution system. Moreover, by changing the prior probability of the node of the cause, the correlation degree between the variables that affect the successful distribution can be calculated. The results have significant practical significance on improving the quality of distribution, the level of distribution, and the efficiency of enterprises.

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Method of Optimization for Target Localization Model Parameters Based on LSSVR

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.934 ◽

2011 ◽

Vol 268-270 ◽

pp. 934-939

Author(s):

Xue Wen He ◽

Gui Xiong Liu ◽

Hai Bing Zhu ◽

Xiao Ping Zhang

Keyword(s):

Particle Swarm Optimization ◽

Fitness Function ◽

Particle Swarm ◽

Parameters Optimization ◽

Target Localization ◽

Likelihood Method ◽

Support Vector ◽

Swarm Optimization ◽

Localization Accuracy ◽

Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

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Sound-Localization Performance in the Cat: The Effect of Restraining the Head

Journal of Neurophysiology ◽

10.1152/jn.00747.2004 ◽

2005 ◽

Vol 93 (3) ◽

pp. 1223-1234 ◽

Cited By ~ 49

Author(s):

Daniel J. Tollin ◽

Luis C. Populin ◽

Jordan M. Moore ◽

Janet L. Ruhland ◽

Tom C. T. Yin

Keyword(s):

Short Duration ◽

Saccadic Eye Movements ◽

Head Movements ◽

Gaze Shifts ◽

Localization Accuracy ◽

Head Restraint ◽

The Gaze ◽

Long Duration ◽

Localization Performance ◽

Visual Targets

In oculomotor research, there are two common methods by which the apparent location of visual and/or auditory targets are measured, saccadic eye movements with the head restrained and gaze shifts (combined saccades and head movements) with the head unrestrained. Because cats have a small oculomotor range (approximately ±25°), head movements are necessary when orienting to targets at the extremes of or outside this range. Here we tested the hypothesis that the accuracy of localizing auditory and visual targets using more ethologically natural head-unrestrained gaze shifts would be superior to head-restrained eye saccades. The effect of stimulus duration on localization accuracy was also investigated. Three cats were trained using operant conditioning with their heads initially restrained to indicate the location of auditory and visual targets via eye position. Long-duration visual targets were localized accurately with little error, but the locations of short-duration visual and both long- and short-duration auditory targets were markedly underestimated. With the head unrestrained, localization accuracy improved substantially for all stimuli and all durations. While the improvement for long-duration stimuli with the head unrestrained might be expected given that dynamic sensory cues were available during the gaze shifts and the lack of a memory component, surprisingly, the improvement was greatest for the auditory and visual stimuli with the shortest durations, where the stimuli were extinguished prior to the onset of the eye or head movement. The underestimation of auditory targets with the head restrained is explained in terms of the unnatural sensorimotor conditions that likely result during head restraint.

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Factor Graph Weight Particles aided Distributed Underwater Cooperative Positioning Algorithm

10.21203/rs.3.rs-345295/v1 ◽

2021 ◽

Author(s):

lingling zhang ◽

baoguo yu ◽

Chengkai Tang ◽

yi zhang ◽

Houbing Song

Keyword(s):

Power Efficiency ◽

High Efficiency ◽

Low Complexity ◽

Factor Graph ◽

Network Nodes ◽

Cooperative Positioning ◽

Underwater Localization ◽

Positioning Algorithm ◽

Marine Exploration ◽

Real Test

Abstract The growing scale of marine exploration requires high-resolution underwater localization, which necessitates cooperation among underwater network nodes, considering the channel complexity and power efficiency. In this paper, we proposed factor graph weight particles aided distributed underwater nodes cooperative positioning algorithm (WP-DUCP). It capitalized on the factor graph and sum-product algorithm to decompose the global optimization to the product of several local optimization functions. Combined with the Gaussian parameters to construct the weighted particles and to realize the belief transfer, it shows low complexity and high efficiency, suitable to the energy-restricted and communication distance-limited underwater networks. In terms of convergence, localization resolution, and computation complexity, we conducted the simulation and real-test with comparison to the existing co-localization methods. The results verified a higher resolution of the proposed method with no extra computation burden.

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