MAMPI – Multipath-assisted Device-free Localization with Magnitude and Phase Information

The interconnection of devices, driven by the Internet of Things (IoT), enables a broad variety of smart applications and location-based services. The latter is often realized via transponder based approaches, which actively determine device positions within Wireless Sensor Networks (WSN). In addition, interpreting wireless signal measurements also enables the utilization of radar-like passive localization of objects, further enhancing the capabilities of WSN ranging from environmental mapping to multipath detection. For these approaches, the target objects are not required to hold any device nor to actively participate in the localization process. Instead, the signal delays caused by reflections at objects within the propagation environment are used to localize the object. In this work, we used Ultra-Wide Band (UWB) sensors to measure Channel Impulse Responses (CIRs) within a WSN. Determining an object position based on the CIR can be achieved by formulating an elliptical model. Based on this relation, we propose a CIR environmental mapping (CIR-EM) method, which represents a heatmap generation of the propagation environment based on the CIRs taken from radio communication signals. Along with providing imaging capabilities, this method also allows a more robust localization when compared to state-of-the-art methods. This paper provides a proof-of-concept of passive localization solely based on evaluating radio communication signals by conducting measurement campaigns in an anechoic chamber as a best-case environment. Furthermore, shortcomings due to physical layer limitations when using non-dedicated hardware and signals are investigated. Overall, this work lays a foundation for related research and further evaluation in more application-oriented scenarios.

“…In the time domain, the range resolution R defines the ability to distinguish between targets and depends on the used bandwidth

of the signal following Equation ( 3 ). Given an UWB channel (

) the range resolution is

or in time domain

[ 27 , 34 , 35 ].

…”

Section: Fundamentalsmentioning

confidence: 99%

Lab-Based Evaluation of Device-Free Passive Localization Using Multipath Channel Information

Ninnemann

Schwarzbach

Jung

et al. 2021

“…Algorithm 1: Pseudo-code for the error modeling and Monte-Carlo simulation c 2020 IEEE, Reprinted, with permission, from [5].…”

Section: Description Of the Algorithmmentioning

confidence: 99%

“…In the simulation, we assume a 5 × 5 m corridor with two walls (y = 0 and y = 5) that create reflections. We place the transmitter Tx at (0, 2.5) and the receiver Rx at (5,2). This asymmetrical geometry prevents that the simulated MPCs becomes identical, leading to multiple minima in the nearest neighbor algorithm.…”

Section: Simulation Setupmentioning

confidence: 99%

See 1 more Smart Citation

MAMPI-UWB—Multipath-Assisted Device-Free Localization with Magnitude and Phase Information with UWB Transceivers

Cimdins

Schmidt

Hellbrück

2020

Self Cite

In this paper, we propose a multipath-assisted device-free localization (DFL) system that includes magnitude and phase information (MAMPI). The DFL system employs ultra-wideband (UWB) channel impulse response (CIR) measurements, enabling the extraction of several multipath components (MPCs) and thereby benefits from multipath propagation. We propose a radio propagation model that calculates the effect on the received signal based on the position of a person within a target area. Additionally, we propose a validated error model for the measurements and explain the creation of different feature vectors and extraction of the MPCs from Decawave DW1000 CIR measurements. We evaluate the system via simulations of the position error probability and a measurement setup in an indoor scenario. We compare the performance of MAMPI to a conventional DFL system based on four sensor nodes that measures radio signal strength values. The combination of the magnitude and phase differences for the feature vectors results in a position error probability that is comparable to a conventional system but requires only two sensor nodes.

“…Indoor localization using ultra-wideband (UWB) radio is a popular research topic [ 1 , 2 , 3 , 4 , 5 , 6 ] and an off-the-shelf real-time localization system [ 7 ]. Its suitability for mobile applications and its great localization accuracy provide solutions for a wide spectrum of applications.…”

Section: Introductionmentioning

confidence: 99%

Distance Measurements in UWB-Radio Localization Systems Corrected with a Feedforward Neural Network Model

Krapež

Vidmar

Munih

2021

An ultra-wideband (UWB) localization system is an alternative in a GPS-denied environment. However, a distance measurement with UWB modules using a two-way communication protocol induces an orientation-dependent error. Previous research studied this error by looking at parameters such as the received power and the channel response signal. In this paper, the neural network (NN) method for correcting the orientation-induced distance error without the need to calculate the signal strength, obtain the channel response or know any parameters of the antenna and the UWB modules is presented. The NN method utilizes only the measured distance and the tag orientation, and implements an NN model obtained by machine learning, using measurements at different distances and orientations of the two UWB modules. The verification of the experimental setup with 12 anchors and a tag shows that with the proposed NN method, 5 cm better root mean square error values (RMSEs) are obtained for the measured distance between the anchors and the tag compared to the calibration method that did not include orientation information. With the least-square estimator, 14 cm RMSE in 3D is obtained with the NN model corrected distances, with a 9 cm improvement compared to when raw distances are used. The method produces better results without the need to obtain the UWB module’s diagnostics parameters that are required to calculate the received signal strength or channel response, and in this way maintain the minimum packet size for the ranging protocol.