Infrared local positioning system using phase differences

Martín-Gorostiza, Ernesto; Meca, Francisco Javier Meca; Galilea, José Luis Lázaro; Salido-Monzú, David; Martos-Naya, Eduardo

doi:10.1109/upinlbs.2014.7033733

Cited by 15 publications

(13 citation statements)

References 22 publications

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“…The distance error is a function of the distance between emitter and sensor

, as well as the angle of incidence

. We will not repeat the other constant parameters that model the system, which can be found in the works that describe it [ 44 , 45 ]. Finally, Figure 3 b shows the evolution of the standard deviation of the distance measurement error

versus the distance in the xy-plane

.…”

Section: Resultsmentioning

confidence: 99%

Optimization of the Coverage and Accuracy of an Indoor Positioning System with a Variable Number of Sensors

Domingo-Perez

Galilea

Bravo

et al. 2016

Sensors

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This paper focuses on optimal sensor deployment for indoor localization with a multi-objective evolutionary algorithm. Our goal is to obtain an algorithm to deploy sensors taking the number of sensors, accuracy and coverage into account. Contrary to most works in the literature, we consider the presence of obstacles in the region of interest (ROI) that can cause occlusions between the target and some sensors. In addition, we aim to obtain all of the Pareto optimal solutions regarding the number of sensors, coverage and accuracy. To deal with a variable number of sensors, we add speciation and structural mutations to the well-known non-dominated sorting genetic algorithm (NSGA-II). Speciation allows one to keep the evolution of sensor sets under control and to apply genetic operators to them so that they compete with other sets of the same size. We show some case studies of the sensor placement of an infrared range-difference indoor positioning system with a fairly complex model of the error of the measurements. The results obtained by our algorithm are compared to sensor placement patterns obtained with random deployment to highlight the relevance of using such a deployment algorithm.

show abstract

“…The distance error is a function of the distance between emitter and sensor

, as well as the angle of incidence

versus the distance in the xy-plane

.…”

Section: Resultsmentioning

confidence: 99%

Optimization of the Coverage and Accuracy of an Indoor Positioning System with a Variable Number of Sensors

Domingo-Perez

Galilea

Bravo

et al. 2016

Sensors

View full text Add to dashboard Cite

show abstract

“…After the IR and camera processing blocks, two position estimates, trueX^bold-italicIR and trueX^bold-italicc, are obtained from the IR and camera sensors respectively. trueX^bold-italicIR is attained by hyperbolic trilateration from differences of distances [45] and trueX^bold-italicc is obtained by projecting the camera image plane onto the scene plane by means of a homography transformation. We assume both estimates are affected by bi-dimensional zero-mean Gaussian uncertainties, represented by their respective covariance matrices ∑bold-italicIR, ∑bold-italicc.…”

Section: Methods Descriptionmentioning

confidence: 99%

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

Martín-Gorostiza

García-Garrido

Pizarro

et al. 2019

Sensors

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A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements is between 0.4 cm (fusion) and 2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4 × 3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

show abstract

“…After the IR and camera processing blocks, two position estimates, X IR and X c , are obtained from the IR and camera sensors respectively. X IR is attained by hyperbolic trilateration from differences of distances [45] and X c is obtained by projecting the camera image plane onto the scene plane by means of an homography transformation. We assume A deep explanation of the IR positioning system (developed in the past) can be read in [17,45].…”

Section: Methods Descriptionmentioning

confidence: 99%

“…All features of the test bench are summarized in Table 2, including devices, BLC configuration and test conditions (notation and configuration indexes or labels correspond to those in Figure 7). The IR measurements and positioning system performance had already been developed and shown in past [45]. The camera data has been collected for fusion purposes, which constitutes the core of the results presented in this paper.…”

Section: Setupmentioning

confidence: 99%

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

Martín-Gorostiza¹,

García-Garrido²,

Pizarro³

et al. 2019

Preprint

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A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements  is between 0.4 cm (fusion) and  2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4x3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

show abstract

Infrared local positioning system using phase differences

Cited by 15 publications

References 22 publications

Optimization of the Coverage and Accuracy of an Indoor Positioning System with a Variable Number of Sensors

Optimization of the Coverage and Accuracy of an Indoor Positioning System with a Variable Number of Sensors

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

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