On the Cramer-Rao lower bound for RSS-based positioning in wireless cellular networks

Veletić, Mladen; Šunjevarić, Milan

doi:10.1016/j.aeue.2014.02.012

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…The proposed algorithm employs kNN as a regression model to find the MU-BTS distances d i . In addition, our proposal uses GA as an optimization tool to find the solutions to the system defined by (1). Based on this, the hybrid ML technique proposed here is denoted as the kNN/GAq algorithm, where q is the number of BTSs used in the regression model.…”

Section: A Proposal Descriptionmentioning

confidence: 99%

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A Hybrid Machine Learning Approach for Mobile User Positioning in Cellular Networks

Timoteo¹,

Cunha²,

Silva³

et al. 2017

Anais De XXXV Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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The outstanding growth of location-based services and applications for mobile devices has motivated research about wireless positioning techniques for outdoor and indoor environments. In the present paper, a machine learning approach is proposed for finding the mobile user location. More precisely, a hybrid machine learning technique is proposed to obtain the position of a mobile user in an outdoor environment of cellular networks. The proposal employs k-Nearest Neighbors as a regression model to find the distances between the mobile and the base stations, and Genetic Algorithms to estimate mobile position. Simulation results show that the proposed algorithm has better performance than the COST-231/Nelder-Mead trilateration technique. Friedman and Nemenyi tests are used to statistically validate the results.

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Section: A Proposal Descriptionmentioning

confidence: 99%

“…Wireless positioning systems have received increasing attention in recent years [1], [2]. Position obtaining through wireless and mobile technologies is a key factor to achieve an accurate knowledge of mobile terminal location, which is essential for providing location-based services [3].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Machine Learning Approach for Mobile User Positioning in Cellular Networks

Timoteo¹,

Cunha²,

Silva³

et al. 2017

Anais De XXXV Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…According to [26] a fixed environment-dependent PLE generally has smaller variance compared to the dynamic PLE obtained through an estimation process, which demonstrates the random nature associated with the propagation environment. PLE estimation suffers from multipath and shadowing effects, which are the main sources of error in RSS based positioning systems, especially in indoor environments.…”

Section: Introductionmentioning

confidence: 95%

Body Attenuation and Path Loss Exponent Estimation for RSS-Based Positioning in WSN

Silva

Afonso

Rocha

2016

Wireless Pers Commun

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The internet of things (IoT) is a vision consisting of a world where a large number of common objects used in a daily basis are imbued with computational and communication capabilities, efficiently automating innumerous tasks [1]. This vision is closely related to the ubiquitous computing vision from Mark Weiser, in which "technologies disappear into the background", facilitating daily tasks [2]. There are several institutions working in order to achieve this vision, not only in the form of standards creation and improvement, but also in the interaction between different standards, so a global network augmented by these objects can be realized [3]. Sensor networks have a central role in this ubiquitous computing vision, since they provide the connection to the real world through sensors and actuators. A plethora of different sensors can be applied to the most diverse scenarios and the information generated by these sensors can be used to achieve higher understanding or process efficiency. The terms IoT and ubiquitous computing, along with other terms such as ambient intelligence, pervasive computing and smart objects are related and sometimes used interchangeably in literature. A common feature shared between these visions, directly related to the data sensed from these smart objects, is the location from where the data is collected. Positioning technology is a key enabler for the ubiquitous computing vision due to the application opportunities that arise with the knowledge of the position of the user or device. In this work, the focus is on positioning capabilities in wireless sensor networks (WSN) applied to indoor environment, which is a major component of the ubiquitous computing vision. WSN nodes are sensing capable devices composed by a microcontroller and a wireless transceiver. WSN constraints nowadays relate to device cost, energy and computational capabilities, the latter of which trades-off with higher battery recharge frequencies. Positioning in WSN can be accomplished by using optical, acoustic or radio frequency (RF) technologies, to produce range, angle or pattern information. Using one of these measurements, or a combination of them, the location of a device can be computed. Ultrawideband (UWB) is a technology that enables ranging capabilities between devices and allows the detection of multipath components of transmitted signals by using large channel bandwidths. This technology holds the key to solve indoor positioning systems due to its sub-meter ranging accuracy. Yet, due to the existence of two versions of UWB standard competing among each other, a pulsed version and a frequency division version, standard acceptance has been very slow. Furthermore, the ranging capability feature is optional, not obligating manufacturers to implement it in order to comply with the standard. Due to the slow standard acceptance, there isn't yet a mass market in place, which in turn affects device cost for currently available off-the-shelf components. The inclusion of UWB in IEEE 802.15.4 standard [4] as a low po...

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“…A cellular network has the advantage of wide coverage and can be used as an auxiliary system for BDS [13]. However, the positioning accuracy of the cellular network is usually above the m level [14]. For the high-precision positioning requirements of port containers, the positioning accuracy must be below the m level.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Localization Algorithm with Adaptive Kalman Gain for Port Container Seamless Positioning

Yue

Xu²,

Xue

et al. 2022

Wireless Communications and Mobile Computing

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BeiDou Navigation Satellite System (BDS) has provided high-precision, reliable positioning, navigation, and timing services anywhere in the world since the end of 2020. However, the positioning performance of BDS does not behave well like the other global navigation satellite systems (GNSS) in the transition areas indoor and outdoor, particularly in container ports, because the navigation signals are blocked and reflected by containers. In order to solve the above problem, as the pulse radio ultra-wideband (UWB) has the characteristics of high positioning accuracy, high multipath resolution, large bandwidth, and high communication rate, it is used as the Beidou auxiliary system, and the two are combined. In addition, the positioning algorithm greatly affects BDS/UWB integrated positioning performance. The single Newton iterative least square (LS) positioning algorithm does not take into account the continuity of position in time, leading to disordered positioning results and large positioning errors. For obtaining high positioning accuracy, the Kalman filter (KF) is introduced to process the output results of the LS algorithm, and a fusion localization algorithm of the Newton iterative least square and Kalman filter (LS-KF) with adaptive Kalman gain is proposed in this paper. The fusion algorithm can solve effectively the problem that the Kalman gain approaches a stable state after multiple observation epochs, resulting in a constant ratio between the predicted and the measured states. Besides, the algorithm can be used to assess the confidence of each measurement state, determine the Kalman gain adaptive adjustment factor according to the obtained confidence, and further adjust the Kalman gain through the adaptive factor. Theoretical analysis and simulation results show that the proposed algorithm can improve the overall positioning accuracy and anti-interference ability.

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On the Cramer-Rao lower bound for RSS-based positioning in wireless cellular networks

Cited by 10 publications

References 9 publications

A Hybrid Machine Learning Approach for Mobile User Positioning in Cellular Networks

A Hybrid Machine Learning Approach for Mobile User Positioning in Cellular Networks

Body Attenuation and Path Loss Exponent Estimation for RSS-Based Positioning in WSN

A Fusion Localization Algorithm with Adaptive Kalman Gain for Port Container Seamless Positioning

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