On the Crucial Impact of Antennas and Diversity on BLE RSSI-Based Indoor Localization

Schulten, Henry; Kuhn, Marc; Heyn, Robert; Dumphart, Gregor; Trosch, Florian; Wittneben, A.

doi:10.1109/vtcspring.2019.8746717

Cited by 14 publications

(12 citation statements)

References 6 publications

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“…The joint MLE of distance and clock offset is given by d (asyn,gen) ,ǫ (asyn,gen) ∈ arg max d,ǫ 6 It can be shown that (2), (4), (8) are the MLE also for the respective 2D cases with analogous assumptions on e k . Instead of (5), this case features f (∆ k |d) = c π (d 2 −c 2 ∆ 2 k ) −1/2 as observation PDF. The details are omitted.…”

Section: General Case With Asynchronous Clocksmentioning

confidence: 93%

“…4 If the directions e k were known, d could be determined from the linear system of K equations c∆ k = −e T k d, but this would require specific knowledge about the environment as in multipath-assisted localization [10], [14] and is not possible with our statistical description of the e k . 5 In detail, (5) follows from projection c∆ k = −e T k d and assumptions II and III. This holds because any projection of a uniformly distributed 3D unit vector has uniform distribution.…”

Section: B Delays Extracted Without Error; Asynchronous Clocksmentioning

confidence: 99%

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Inter-Node Distance Estimation from Multipath Delay Differences of Channels to Observer Nodes

Dumphart

Kuhn

Wittneben

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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We study the estimation of distance d between two wireless nodes by means of their wideband channels to a third node, called observer. The motivating principle is that the channel impulse responses are similar for small d and drift apart when d increases. Following this idea we propose specific distance estimators based on the differences of path delays of the extractable multipath components. In particular, we derive such estimators for rich multipath environments and various important cases: with and without clock synchronization as well as errors on the extracted path delays (e.g. due to limited bandwidth). The estimators readily support (and benefit from) the presence of multiple observers. We present an error analysis and, using ray tracing in an exemplary indoor environment, show that the estimators perform well in realistic conditions. We describe possible localization applications of the proposed scheme and highlight its major advantages: it requires neither precise synchronization nor line-of-sight connection. This could make wireless user tracking feasible in dynamic indoor settings.

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Section: General Case With Asynchronous Clocksmentioning

confidence: 93%

Section: B Delays Extracted Without Error; Asynchronous Clocksmentioning

confidence: 99%

Inter-Node Distance Estimation from Multipath Delay Differences of Channels to Observer Nodes

Dumphart

Kuhn

Wittneben

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

Self Cite

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“…The advantages 2, 3, 4 make the paradigm qualified for dynamic settings with time-variant channels. In such circumstances, any scheme that crucially relies on training or calibration would deteriorate heavily (e.g., fingerprinting [21], [22] or parameter estimation with calibrated channel models [4], [15]).…”

Section: Opportunities and Use Casesmentioning

confidence: 99%

“…This poses a great challenge to wireless localization and ranging. Distance estimates obtained from the received signal strength (RSS) tend to have large relative error as shadowing, antenna patterns, and small-scale fading cause large RSS fluctuations [15], [16]. Time of arrival (TOA) distance estimates, which can be obtained with wideband or ultra-wideband (UWB) systems, often have a substantial bias as a result of LOS obstruction, synchronization errors, processing delays, and multipath interference [17]- [20].…”

Section: Introductionmentioning

confidence: 99%

Pairwise Node Localization From Differences in Their UWB Channels to Observer Nodes

Dumphart,

Kramer,

Heyn

et al. 2021

Preprint

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We consider the problem of localization and distance estimation between a pair of wireless nodes in a multipath propagation environment, but not the usual way of processing a channel measurement between them. We propose a novel paradigm which compares the two nodes' ultra-wideband (UWB) channels to other nodes, called observers. The key principle is that the channel impulse responses (CIRs) are similar at small inter-node distance d and differ increasingly with increasing d. We present distance estimators which utilize the rich location information contained in the delay differences of extracted multipath components (MPCs). Likewise, we present estimators for the relative position vector which process both MPC delays and MPC directions. We do so for various important cases: with and without clock synchronization, delay measurement errors, and knowledge of the MPC association between the two CIRs. The estimators exhibit great technological advantages: they do not require precise time-synchronization, line-of-sight conditions, or knowledge about the observer locations or the environment. We study the estimation accuracy with a numerical evaluation based on random sampling and, additionally, with an experimental evaluation based on measurements in an indoor environment. The proposal shows the potential for great accuracy in theory and practice. Integrating the paradigm into existing localization algorithms and systems could enable low-cost localization of wireless users or networks in dynamic multipath settings.

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“…Also, in [14] an algorithm with high localization accuracy when the signal to noise ratio is high is proposed which can improve the accuracy without the need of modifying the underlying hardware. Previous proposed models based on Bayesian or Particle filter noise reduction solutions [15] are still computationally expensive and are not always fit for convoluted indoor environments [16,17].…”

Section: Introductionmentioning

confidence: 99%

Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation

Dolha

Negirla

Alexa

et al. 2019

Sensors

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Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

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On the Crucial Impact of Antennas and Diversity on BLE RSSI-Based Indoor Localization

Cited by 14 publications

References 6 publications

Inter-Node Distance Estimation from Multipath Delay Differences of Channels to Observer Nodes

Inter-Node Distance Estimation from Multipath Delay Differences of Channels to Observer Nodes

Pairwise Node Localization From Differences in Their UWB Channels to Observer Nodes

Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation

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