PyLayers: An open source dynamic simulator for indoor propagation and localization

Amiot, Nicolas; Laaraiedh, Mohamed; Uguen, Bernard

doi:10.1109/iccw.2013.6649206

Cited by 30 publications

(22 citation statements)

References 7 publications

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“…We consider a scenario representative of indoor localization based on METIS Madrid grid model [66]. We employ the ray tracing simulation tool in order to model the propagation of signals in the uplink and downlink for channel training and tracking [67]. We set f Unless otherwise stated, the required number of elements for the SD-based training is set to V = 3.…”

Section: A Simulation Setupmentioning

confidence: 99%

Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

Shahmansoori

Uguen

Destino

et al. 2019

IEEE Signal Process. Lett.

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Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location of the user terminals are largely undiscovered. In this paper, we propose a novel support detection-based channel training method for frequency selective millimeter-wave (mm-wave) multiple-input-multiple-output system with lens antenna arrays. We show that accurate position and orientation estimation and tracking is possible using signals from a single transmitter with lens antenna arrays. Particularly, the beamspace channel estimation is formulated as two sparse signal recovery problems in the downlink and uplink for the estimation of angle-of-arrival, angle-of-departure, and timeof-arrival. The proposed method offers a higher sparse detection probability compared to the compressed sensing based solutions. Finally, a joint heuristic beamformer design and user position and orientation tracking approach are proposed based on initial estimation of channel parameters obtained in the training phase.

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Section: A Simulation Setupmentioning

confidence: 99%

Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

Shahmansoori

Uguen

Destino

et al. 2019

IEEE Signal Process. Lett.

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“…K is a constant that depends on the units for d rssi and f . For f in MHz and d in km, K = 32.44 [1]. We implement three methods in the measurement model: (i) No Classification (NC), (ii) Hard (acceptance/rejection) Classification (HC), and (iii) Soft (probabilistic) Classification (SC).…”

Section: B Localization Frameworkmentioning

confidence: 99%

“…Radio signals are easily distorted by the presence of dynamic objects, the room temperature, dust, and even humidity. Furthermore, shadow fading and multipath propagation severely hinder the reliability of signal strength for ranging [1]. The current state-of-art of radio-based positioning techniques [2], [3] are broadly based on the following four distinct categories: (i) Received Signal Strength Indicator (RSSI), (ii) Angle Of Arrival (AOA), (iii) Time Of Arrival (TOA), and (iv) Physical Layer Information (PHY).…”

Section: Introductionmentioning

confidence: 99%

DeepLocNet: Deep Observation Classification and Ranging Bias Regression for Radio Positioning Systems

Dhanjal

Ghaffari

Eustice

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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WiFi technology has been used pervasively in finegrained indoor localization, gesture recognition, and adaptive communication. Achieving better performance in these tasks generally boils down to differentiating Line-Of-Sight (LOS) from Non-Line-Of-Sight (NLOS) signal propagation reliably which generally requires expensive/specialized hardware due to the complex nature of indoor environments. Hence, the development of low-cost accurate positioning systems that exploit available infrastructure is not entirely solved. In this paper, we develop a framework for indoor localization and tracking of ubiquitous mobile devices such as smartphones using on-board sensors. We present a novel deep LOS/NLOS classifier which uses the Received Signal Strength Indicator (RSSI), and can classify the input signal with an accuracy of 85%. The proposed algorithm can globally localize and track a smartphone (or robot) with a priori unknown location, and with a semi-accurate prior map (error within 0.8 m) of the WiFi Access Points (AP). Through simultaneously solving for the trajectory and the map of access points, we recover a trajectory of the device and corrected locations for the access points. Experimental evaluations of the framework show that localization accuracy is increased by using the trained deep network; furthermore, the system becomes robust to any error in the map of APs.

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“…The used data are generated by a deterministic UWB raytracing simulator using a reference indoor office environment [17]. The simulator gives access directly to (x * h)(t) for 900 different TX and RX positions taken on a regular grid, which are then used to generate y(t) by adding noise.…”

Section: A Simulation Contextmentioning

confidence: 99%

Public discussion strategies for secret key generation from sampled IR-UWB channel responses

Tunaru

Denis

Uguen³

2014

2014 10th International Conference on Communications (COMM)

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The generation of symmetric keys using physical layer measurements (e.g. received signal strength, channel responses etc.) is based on the reciprocity property of wireless channels. However, noise or other transmission artifacts can degrade the link reciprocity and lead to bit mismatching between legitimate users. One of the challenges of this new key management paradigm is to obtain as many identical bits as possible, while guaranteeing good secrecy of the public exchanges that allow the bit correction. Previous studies have provided a proof of concept for exploiting oversampled ultra wideband signals to generate secret keys with practical lengths. In order to improve the security of such schemes with respect to a nearby passive attacker with correlated channel observations, we propose public discussion alternatives including one strategy based on the signal time of flight (ToF). We show that these alternatives can reduce the advantage that such an attacker can have in terms of bit agreement between the legitimate keys and his guess.

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PyLayers: An open source dynamic simulator for indoor propagation and localization

Cited by 30 publications

References 7 publications

Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

DeepLocNet: Deep Observation Classification and Ranging Bias Regression for Radio Positioning Systems

Public discussion strategies for secret key generation from sampled IR-UWB channel responses

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