Integrity for Multi-Sensor Cooperative Positioning

Xiong, Jun; Cheong, Joon Wayn; Xiong, Zhi; Dempster, Andrew G.; Tian, Shengfeng; Wang, Rong

doi:10.1109/tits.2019.2956936

Cited by 47 publications

(20 citation statements)

References 38 publications

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“…Thanks to the recent disclosure of GNSS raw measurements in the mass market GNSS receivers (such as for the embedded chipsets in the smartphones), developers, and researchers can now access raw GNSS observables (e.g., code/phase raw pseudorange measurements and Doppler measurements) prior to their actual use in PVT computation [40][41][42]. Such data can be transmitted, synchronised, and combined among different asynchronous users to reciprocally improve the positioning performance, the mutual situational awareness, and to open a variety of new solutions for the monitoring of GNSS integrity within a network of users [43,44]. As assessed within the HANSEL project and described in Section 4, the combination of GNSS raw pseudorange measurements can be exploited to determine the inter-agent distance (a.k.a.)…”

Section: Dgnss-based Cooperative Positioningmentioning

confidence: 99%

A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities: The HANSEL Project

et al. 2020

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In urban contexts, the increasing density of electronic devices equipped with Global Navigation Satellite System (GNSS) receivers and complementary positioning technologies is attracting research and development efforts devoted to an improvement of the quality of life towards the smart city paradigm. Vehicular and pedestrian positioning and navigation capabilities are among the major drivers for innovation in this process. Ultra-low-cost electronics such as smartphones and Internet of Things (IoT) sensors aim at providing accurate and reliable positioning solutions through a set of promising solutions. Among these, snapshot positioning allows to remotely perform the post-processing of GNSS signals in IoT sensor networks while Wi-Fi™ ranging and cooperative positioning provide auxiliary anchors of opportunity to enhance indoor/outdoor positioning capabilities. This paper presents an innovative platform to perform a centralised testing and assessment of such positioning and navigation technologies along with a set of results obtained in the context of the European project HANSEL, by relying on current network technologies and infrastructures (i.e., Wi-Fi™ and cellular connectivity).

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Section: Dgnss-based Cooperative Positioningmentioning

confidence: 99%

A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities: The HANSEL Project

et al. 2020

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“…Although BRAIM does not require measurement redundancy, the fusion of GNSS pseudoranges and terrestrial ranges should positively affect the a posteriori distribution (shown in Gabela et al., 2019) and, consequently, the integrity. The fusion of GNSS and terrestrial ranges (as defined and used in this paper) in integrity monitoring of land‐based applications is not common, and we were able to find only two studies that have done this (J. Liu et al., 2020; Xiong et al., 2019). It is also worth mentioning that similar efforts of ensuring sufficient measurement redundancy previously have been made for civil aviation.…”

Section: Introductionmentioning

confidence: 99%

“…Cooperative Integrity Monitoring is proposed by Xiong et al. (2019) in addition to the FDE algorithm that is capable of better detection of GNSS faults than classic RAIM, and of detecting multiple faulty cooperative measurements.…”

Section: Introductionmentioning

confidence: 99%

Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

Gabela

Kealy²,

Hedley

et al. 2021

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“…Among these, for example, vehicles can periodically broadcast messages to other vehicles which can benefit from them in a plethora of manners. In particular, position information and auxiliary range data can be used, under certain conditions, to assess or refine the quality of the GNSS positioning solutions in a cooperative fashion [11], [12]. Ranging associated to the position estimate of a set of users actually provides opportunistic, additional landmarks to the positioning problem faced by conventional GNSS receivers.…”

Section: Introductionmentioning

confidence: 99%

A Cognitive Particle Filter for Collaborative DGNSS Positioning

2020

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The advances in low-latency communications networks and the ever-growing amount of devices offering localization and navigation capabilities opened a number of opportunities to develop innovative network-based collaborative solutions to satisfy the increasing demand for positioning accuracy and precision. Recent research works indeed, have fostered the concept of networked Global Navigation Satellite System (GNSS) receivers supporting the sharing of raw measurements with other receivers within the same network. Such measurements (i.e. pseudorange and Doppler) can be processed through Differential GNSS (DGNSS) techniques to retrieve inter-agent distances which can be in turn integrated to improve positioning performance. This paper investigates an improved Bayesian estimation algorithm for a sensorless, tight-integration of DGNSS-based collaborative measurements through a modified Particle Filter (PF), namely Cognitive PF. Differently from Extended Kalman Filter and Uscented Kalman Filter indeed, a PF natively support the non-Gaussian noise distribution which characterizes DGNSS-based interagent distances. The proposed Cognitive PF is hence designed, implemented and optimized according to the architecture of a proprietary Inertial Navigation System (INS)-free Global Navigation Satellite System (GNSS) software receiver. Experimental tests performed through realistic radio-frequency GNSS signals showed a remarkable improvement in positioning accuracy w.r.t. reference PF and EKF architectures.

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Integrity for Multi-Sensor Cooperative Positioning

Cited by 47 publications

References 38 publications

A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities: The HANSEL Project

A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities: The HANSEL Project

Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

A Cognitive Particle Filter for Collaborative DGNSS Positioning

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