Physical Modeling for Device-Free Localization exploiting Multipath Propagation of Mobile Radio Signals

Schmidhammer, Martin; Walter, Michael; Gentner, Christian; Sand, Stephan

doi:10.23919/eucap48036.2020.9135773

Cited by 8 publications

(27 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After that, the RMSE iter error is calculated, in the same manner as it was described in (8), between the x pc i , y pc i representing transformed position estimates (2)-(7), (9) and x i , y i . As it was described in the Section 3.1.1, the decrease of the RMSE iter error defines the correct direction of transformation (9). In a case when the RMSE iter error is increasing the β i angle must take the opposite sign.…”

Section: Correction Of the Heading Error Of Successively Determined Pmentioning

confidence: 89%

“…In the Figure 1 a block diagram illustrating the operations performed in a hybrid localization system using the proposed method is shown. It can be seen that the results of the RDMs are not used to determine independent position estimates (as it can be observed in typical solutions [9,20]), but to correct position estimates determined by using the inertial navigation algorithm. The inertial navigation algorithm is based on the Kalman filtration process, in which the displacement of the monitored person is estimated on the basis of measured movement parameters (linear accelerations and angular rates) using the strap down navigation equations [6,8,10].…”

Section: Description Of the Proposed Methods Of Positional Data Integrmentioning

confidence: 99%

“…The main role of the computational unit is to gather the measurement data from the inertial and UWB modules. The values of linear accelerations, angular rates registered by the triaxial MEMS sensors are used as an input data of the inertial navigation algorithm based on Kalman filtration [9,25]. Measurement data collection is constant, which means that the relative movement of the monitored person is calculated in a real time (using the inertial navigation algorithm) with 500 ms latency.…”

Section: The Structure Of the Mobile And Reference Nodesmentioning

confidence: 99%

“…In the current state-of-the-art other methods used in indoor localization and navigation systems can be pointed: odometry, machine vision, lidars, echolocation, environmental sounding (with radio channel sounding), and optical markers. Their usage allows to meet the restrictive requirements regarding the precision and stability, but their applicability is usually limited for object (robots) tracking taking into account the cost of the infrastructure and the need of precision environmental recognition [9].…”

Section: Introductionmentioning

confidence: 99%

“…This means that the proposed method can be used when a typical way of positional data integration method, i.e., using loosely coupled Kalman filter with independent estimation of position by both subsystems, cannot be implemented. The scenario of usage less than three reference node in further part of the article will be named as the case with reduced number of reference nodes [2,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Person Tracking in Ultra-Wide Band Hybrid Localization System Using Reduced Number of Reference Nodes

Rajchowski

Stefański

Sadowski

et al. 2020

Sensors

View full text Add to dashboard Cite

In this article a novel method of positional data integration in an indoor hybrid localization system combining inertial navigation with radio distance measurements is presented. A point of interest is the situation when the positional data and the radio distance measurements are obtained from less than thee reference nodes and it is impossible to unambiguously localize the moving person due to undetermined set of positional equations. The presented method allows to continuously provide localization service even in areas with disturbed propagation of the radio signals. Authors performed simulation and measurement studies of the proposed method to verify the precision of position estimation of a moving person in an indoor environment. It is worth noting that to determine the simulation parameters and realize the experimental studies the hybrid localization system demonstrator was developed, combining inertial navigation and radio distance measurements. In the proposed solution, results of distance measurements taken to less than three reference nodes are used to compensate the drift of the position estimated using the inertial sensor. In the obtained simulation and experimental results it was possible to reduce the localization error by nearly 50% regarding the case when only inertial navigation was used, additionally keeping the long term root mean square error at the level of ca. 0.50 m. That gives a degradation of localization precision below 0.1 m with respect to the fusion Kalman filtration when four reference nodes are present.

show abstract

Section: Correction Of the Heading Error Of Successively Determined Pmentioning

confidence: 89%

Section: Description Of the Proposed Methods Of Positional Data Integrmentioning

confidence: 99%

Section: The Structure Of the Mobile And Reference Nodesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Person Tracking in Ultra-Wide Band Hybrid Localization System Using Reduced Number of Reference Nodes

Rajchowski

Stefański

Sadowski

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

Bayesian multipath‐enhanced device‐free localisation: Simulation‐ and measurement‐based evaluation

Schmidhammer

Siebler

Gentner

et al. 2022

IET Microwaves Antenna & Prop

Self Cite

View full text Add to dashboard Cite

Device-free localisation (DFL) systems infer presence and location of moving users by measuring to which extent they change the received signal power in wireless links. Consequently, users not only induce perturbations to the power of the line of sight but also to the power of reflected and scattered signals which are observed in the received signal as multipath components (MPCs). Since the propagation paths of MPCs differ inherently from the line-of-sight path, these propagation paths can be considered as additional network links. This extended network determines the multipath-enhanced device-free localisation (MDFL) system. Based on empirical models that relate perturbations in the received power of MPCs to the user location, the localisation problem can be solved by non-linear Bayesian filtering. In this work, we investigate the point mass filter and the particle filter as possible solutions. We demonstrate the applicability of these solutions using ultra-wideband measurements and develop and verify a numerical simulation framework that flexibly enables a sound evaluation of MDFL. Based on both measurements and simulations, we show a significant improvement of the localisation performance of MDFL compared to DFL. The overall localisation performance is thereby comparable for both filters. Eventually, we show that complexity and divergence probability, rather than localisation performance, are the decisive factors for the choice of the filter solution.

show abstract

Multipath-Enhanced Device-Free Localization in Wideband Wireless Networks

Schmidhammer

Gentner

Sand

et al. 2021

Antennas Wirel. Propag. Lett.

Self Cite

View full text Add to dashboard Cite

State-of-the-art device-free localization systems infer presence and location of users based on received signal strength measurements of line-of-sight links in wireless networks. In this letter, we propose to enhance device-free localization systems by exploiting multipath propagation between the individual network nodes. Particularly indoors, wireless propagation channels are characterized by multipath propagation, i.e., received signals comprise multipath components due to reflection and scattering. Given prior information about the surrounding environment, e.g., a floor plan, the individual propagation paths of multipath components can be derived geometrically. Inherently, these propagation paths differ spatially from the line-of-sight propagation path and can be considered as additional links in the wireless network. This extended network determines the novel multipath-enhanced device-free localization system. Using theoretical performance bounds on the localization error, we show that including multipath components into device-free localization systems improves the overall localization performance and extends the coverage area significantly.

show abstract

Physical Modeling for Device-Free Localization exploiting Multipath Propagation of Mobile Radio Signals

Cited by 8 publications

References 15 publications

Person Tracking in Ultra-Wide Band Hybrid Localization System Using Reduced Number of Reference Nodes

Person Tracking in Ultra-Wide Band Hybrid Localization System Using Reduced Number of Reference Nodes

Bayesian multipath‐enhanced device‐free localisation: Simulation‐ and measurement‐based evaluation

Multipath-Enhanced Device-Free Localization in Wideband Wireless Networks

Contact Info

Product

Resources

About