Delay Tracking of Spread-Spectrum Signals for Indoor Optical Ranging

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

doi:10.3390/s141223176

Cited by 12 publications

(8 citation statements)

References 18 publications

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“…Note that considering the error as unbiased AWGN means there are no remaining systematic or other biasing error contributions (cancelled after error correction and calibration, or considered negligible compared with random contributions). This includes multipath (MP) errors, which means working in an MP free environment (large open spaces with low wall reflectivity or else, MP cancellation capabilities [41,47] or working with orientable detectors [42]).…”

Section: Methods Descriptionmentioning

confidence: 99%

“…Additive White Gaussian Noise (AWGN) hypothesis is supposed for the IR and camera observations in these models. For this, IR measurements must be mostly multipath (MP) free, which can be reasonably assumed in sufficiently large scenarios with low reflectivity of walls and ceiling, or by implementing MP mitigation techniques [41] or oriented sensors (as in [42] in an IR communications framework). The IR positioning system, with cm level precision, was successfully developed and shown in the past, and a model that relates the variances of position estimate to the target position was derived.…”

Section: Introductionmentioning

confidence: 99%

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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.

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Section: Methods Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Nevertheless, the quantities r ir will be named as observations hereafter as only a constant factor is needed to convert φ ir into r ir . [41,47] or working with orientable detectors [42]).…”

Section: Methods Descriptionmentioning

confidence: 99%

“…For this, IR measurements must be mostly multipath (MP) free, which can be reasonably assumed in sufficiently large scenarios with low reflectivity of walls and ceiling, or by implementing MP mitigation techniques [41] or oriented sensors (as in [42] in an IR communications framework). The IR positioning system, with cm level precision, was successfully developed and shown in the past, and a model that relates the variances of position estimate to the target position was derived.…”

Section: Introductionmentioning

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.

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“…In optical communications in closed environments, higher bit rates are being achieved thanks to advances in the technologies employed to manufacture new devices (transmitters and receivers), which present higher bandwidths and thus permit the use of different types of modulation. Various positioning methods based on emitting/receiving optical signals use trilateration from distance measurements obtained from time of flight (ToF), phase of arrival (PoA) [ 1 , 2 ] and received signal strength (RSS) [ 3 ]. Position can also be estimated via triangulation by measuring the angle of arrival (AoA) at which the signal reaches the receiver.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Effect of Optical Signal Multipath

De-La-Llana-Calvo

Galilea

Gardel

et al. 2017

Sensors

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Here, we propose a model to determine the effect of multipath in indoor environments when the shape and characteristics of the environment are known. The main paper goal is to model the multipath signal formation to solve, as much as possible, the negative effects in light communications, as well as the indoor positioning errors due to this phenomenon when using optical signals. The methodology followed was: analyze the multipath phenomenon, establish a theoretical approach and propose different models to characterize the behavior of the channel, emitter and receiver. The channel impulse response and received signal strength are obtained from different proposed algorithms. We also propose steps for implementing a numerical procedure to calculate the effects of these multipaths using information that characterizes the environment, transmitter and receiver and their corresponding positions. In addition, the results of an empirical test in a controlled environment are compared with those obtained using the model, in order to validate the latter. The results may largely vary with respect to the cell size used to discretize the environment. We have concluded that a cell size whose side is 20-times smaller than the minimum distance between emitter and receiver (i.e., 10 cm × 10 cm for a 2-m distance) provides almost identical results between the empirical tests and the proposed model, with an affordable computational load.

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Delay Tracking of Spread-Spectrum Signals for Indoor Optical Ranging

Cited by 12 publications

References 18 publications

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared 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

Modeling the Effect of Optical Signal Multipath

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