Maximum likelihood position estimation in ad-hoc networks using a dead reckoning approach

Hernandez, O.; Bouchereau, Frantz; Muñoz, David F.

doi:10.1109/twc.2008.060230

Cited by 10 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As noted before, the methods in [15,16] are based on the dead reckoning principle. The method reported in [15] provides an accurate location estimation of the nodes in ad hoc networks, and the reported estimation error is around [1.5 × 10 −2 to 3.5 × 10 −2 ]; however, they consider the time and angle of arrival estimation at each node in the route.…”

Section: High Dense Network Highmentioning

confidence: 99%

“…A dead reckoning approach usually depends on gyroscopic, accelerometer observations, the awareness of an initial reference location often called "a fix", and on the knowledge of the consecutive traveling direction finding [10] of the consecutive steps, as well as the range or path length in each travel direction, in order to determine the progress of a traveler at a given time. Thus, conventional dead reckoning approaches demand highly equipped nodes with accurate angle/distance measurement capabilities [15,16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Context-Aware Statistical Dead Reckoning for Localization in IoT Scenarios

Muñoz-Rodríguez

Villalpando-Hernández

Vargas-Rosales

2023

Sensors

View full text Add to dashboard Cite

The current trends in 5G and 6G systems anticipate vast communication capabilities and the deployment of massive heterogeneous connectivity with more than a million internet of things (IoT) and other devices per square kilometer and up to ten million gadgets in 6G scenarios. In addition, the new generation of smart industries and the energy of things (EoT) context demand novel, reliable, energy-efficient network protocols involving massive sensor cooperation. Such scenarios impose new demands and opportunities to cope with the ever-growing cooperative dense ad hoc environments. Position location information (PLI) plays a crucial role as an enabler of several location-aware network protocols and applications. In this paper, we have proposed a novel context-aware statistical dead reckoning localization technique suitable for high dense cooperative sensor networks, where direct angle and distance estimations between peers are not required along the route, as in other dead reckoning-based localization approaches, but they are obtainable from the node’s context information. Validation of the proposed technique was assessed in several scenarios through simulations, achieving localization errors as low as 0.072 m for the worst case analyzed.

show abstract

Section: High Dense Network Highmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Context-Aware Statistical Dead Reckoning for Localization in IoT Scenarios

Muñoz-Rodríguez

Villalpando-Hernández

Vargas-Rosales

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Remark 1 : The combination of dead reckoning and range measurements is an existing approach which has been used in many fields such as GPS-INS [ 16 , 17 ], robotics [ 18 , 19 ], vehicular ad-hoc networks [ 20 – 22 ] and pedestrian localization in indoor environments [ 23 , 24 ]. Dead reckoning determines a present position from a known past position.…”

Section: Position Estimation Based On Displacement Correctionmentioning

confidence: 99%

A Novel Position Estimation Method Based on Displacement Correction in AIS

Jiang

Zhang

Yang

2014

Sensors

View full text Add to dashboard Cite

A new position estimation method by using the signals from two automatic identification system (AIS) stations is proposed in this paper. The time of arrival (TOA) method is enhanced with the displacement correction, so that the vessel's position can be determined even for the situation where it can receive the signals from only two AIS base stations. Its implementation scheme based on the mathematical model is presented. Furthermore, performance analysis is carried out to illustrate the relation between the positioning errors and the displacement vector provided by auxiliary sensors. Finally, the positioning method is verified and its performance is evaluated by simulation. The results show that the positioning accuracy is acceptable.

show abstract

“…Die folgenden Abschnitte beschreiben einen integrierten Ansatz zur infrastrukturlosen Positionierung in selbstorganisierenden Sen- (Hernandez, Bouchereau, Munoz, 2008), (Gustafsson et al, 2002). Aufgrund der Komplexit€ a at und der erforderlichen Rechenleistung sind diese Verfahren jedoch nur eingeschr€ a ankt f€ u ur resourcenschonende Systeme geeignet.…”

Section: Anwendungsbereicheunclassified

Robuste funkbasierte Sensornetze – Aktuelle Forschungsaktivitäten und innovative Lösungsansätze für infrastrukturlose Positionierungssysteme

Adalan

Simhandl²,

Arthaber

2010

Elektrotech. Inftech.

View full text Add to dashboard Cite

Dieser Artikel befasst sich mit der Problematik verl€ a asslicher und robuster Positionierungssysteme bei fehlenden infrastrukturellen Voraussetzungen. Insbesondere wird auf G€ u uter€ u uberwachung n€ a aher eingegangen, der IEEE 802.15.4a-Standard auf physikalischer Ebene zusammengefasst, weiters werden die relevanten Positionierungsalgorithmen erkl€ a art und gezeigt, dass mit IEEE 802.15.4a-kompatiblen Signalen mehrere Problemstellungen durch Verwendung ein und derselben Hardware gel€ o ost werden k€ o onnen. Schlü sselwö rter: funkbasierte Sensornetze; G€ u uter€ u uberwachung; infrastrukturlose Positionierung Robust wireless sensor networks.This article addresses reliable and robust positioning systems in the absence of suitable infrastructure. In particular, we focus on selfpositioning of goods in logistic control briefly, describe the physical layer specifications of the IEEE 802.15.4a Standard, explain the relevant positioning techniques, and show how the IEEE 802.15.4a Standard can be used to solve a variety of problems while using one and the same hardware.

show abstract

Maximum likelihood position estimation in ad-hoc networks using a dead reckoning approach

Cited by 10 publications

References 24 publications

Context-Aware Statistical Dead Reckoning for Localization in IoT Scenarios

Context-Aware Statistical Dead Reckoning for Localization in IoT Scenarios

A Novel Position Estimation Method Based on Displacement Correction in AIS

Robuste funkbasierte Sensornetze – Aktuelle Forschungsaktivitäten und innovative Lösungsansätze für infrastrukturlose Positionierungssysteme

Contact Info

Product

Resources

About