A swarm-based approach to real-time 3D indoor localization: Experimental performance analysis

Monica, Stefania; Ferrari, Gianluigi

doi:10.1016/j.asoc.2016.02.020

Cited by 22 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We remark that the obtained range error estimation model is substantially similar to the one derived in [ 16 ] and used in [ 19 ], even if the indoor scenarios of the experimental distance measurement campaigns are different. The similarity between the two models, however, confirms that the proposed LS approximation of the range error is reliable (regardless of the environment and the considered set of distance measurements).…”

Section: Statistical Model Of the Range Estimation Error: Experimesupporting

confidence: 60%

“…Our results show that the statistical model derived in the first part of the paper can significantly improve the accuracy of the considered localization algorithms, with a reduction of the position estimation error up to 66%. In [ 19 ], a swarm-based algorithm is used to address localization and a statistical model similar (but different from) to that proposed in this paper is used.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error

Monica

Ferrari

2018

Sensors

Self Cite

View full text Add to dashboard Cite

Interest in the Internet of Things (IoT) is rapidly increasing, as the number of connected devices is exponentially growing. One of the application scenarios envisaged for IoT technologies involves indoor localization and context awareness. In this paper, we focus on a localization approach that relies on a particular type of communication technology, namely Ultra Wide Band (UWB). UWB technology is an attractive choice for indoor localization, owing to its high accuracy. Since localization algorithms typically rely on estimated inter-node distances, the goal of this paper is to evaluate the improvement brought by a simple (linear) statistical model of the distance error. On the basis of an extensive experimental measurement campaign, we propose a general analytical framework, based on a Least Square (LS) method, to derive a novel statistical model for the range estimation error between a pair of UWB nodes. The proposed statistical model is then applied to improve the performance of a few illustrative localization algorithms in various realistic scenarios. The obtained experimental results show that the use of the proposed statistical model improves the accuracy of the considered localization algorithms with a reduction of the localization error up to 66%.

show abstract

Section: Statistical Model Of the Range Estimation Error: Experimesupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error

Monica

Ferrari

2018

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In sonar, radar and underwater radar, it is often of interest to determine the location of an object from its emissions [ 3 , 4 ]. Indoor localization in wireless networks is addressed relying on a swarm-based approach [ 5 ]. Earthquake epicentre localization can also be handled with this approach [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Survey on the Performance of Source Localization Algorithms

Fresno

Robles

Martínez-Tarifa

et al. 2017

Sensors

View full text Add to dashboard Cite

The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton–Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time, is the combined MLE-HLS algorithm.

show abstract

“…• A recent optimization-based algorithm [23], which relies on the use of Particle Swarm Optimization (PSO) to obtain effective localization.…”

Section: Relevant Approaches For Indoor Localizationmentioning

confidence: 99%

Experimental Results on Agent-Based Indoor Localization using WiFi Signaling

Monica¹,

Bergenti²

2018

ijacsa

Self Cite

View full text Add to dashboard Cite

Abstract-This paper discusses experimental results on the possibility of accurately estimating the position of smart devices in known indoor environments using agent technology. Discussed localization approaches are based on WiFi signaling, which can be considered as an ubiquitous technology in the large majority of indoor environments. The use of WiFi signaling ensures that no specific infrastructures nor special on-board sensors are required to support localization. Localization is performed using range estimates from the fixed access points of the WiFi network, which are assumed to have known positions. The performance of two range-based localization algorithms are discussed. The first, called Two-Stage Maximum-Likelihood algorithm, is well-known in the literature, while the second is a recent optimization-based algorithm that uses particle swarm techniques. Results discussed in the last part of the paper show that a proper processing of WiFi-based range estimates allows obtaining accurate position estimates, especially if the optimization-based algorithm is used.

show abstract

A swarm-based approach to real-time 3D indoor localization: Experimental performance analysis

Cited by 22 publications

References 8 publications

Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error

Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error

Survey on the Performance of Source Localization Algorithms

Experimental Results on Agent-Based Indoor Localization using WiFi Signaling

Contact Info

Product

Resources

About