TDOA positioning in NLOS scenarios by particle filtering

Boccadoro, Mauro; Angelis, Guido De; Valigi, Paolo

doi:10.1007/s11276-012-0420-9

Cited by 37 publications

(34 citation statements)

References 20 publications

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“…Particle filtering was also implemented for positioning problems in, e.g., References [ 58 , 60 , 63 , 64 , 65 ]. In the SIR version of the particle filter, also known as a bootstrap filter or condensation algorithm, the particles are randomly generated from the motion (or dynamics) model.…”

Section: Particle Filtering For Tdoa-based Positioningmentioning

confidence: 99%

Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes

Khalaf-Allah

2020

Sensors

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In this article, the four-anchor time difference of arrival (TDoA)-based three-dimensional (3D) positioning by particle filtering is addressed. The implemented particle filter uses 1000 particles to represent the probability density function (pdf) of interest, i.e., the posterior pdf of the target node’s state (position). A resampling procedure is used to generate particles in the prediction step, and TDoA measurements are used to determine the importance, i.e., weight, of each particle to enable updating the posterior pdf and estimating the position of the target node. The simulation results show the feasibility of this approach and the possibility to employ it in indoor positioning applications under the assumed working conditions using, e.g., the ultra-wideband (UWB) wireless technology. Therefore, it is possible to enable unmanned air vehicle (UAV) positioning applications, e.g., inventory management in large warehouses, without the need for an excessive number of anchor nodes.

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Section: Particle Filtering For Tdoa-based Positioningmentioning

confidence: 99%

Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes

Khalaf-Allah

2020

Sensors

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“…for localization purpose [16][17][18][19]. They provide fine-grained location information, e.g., Active…”

Section: Related Workmentioning

confidence: 99%

A hop-count based positioning algorithm for wireless ad-hoc networks

2014

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We propose a range-free localization algorithm for a wireless ad-hoc network utilizing the hopcount metric's ability to indicate proximity to anchors (i.e., nodes with known positions). In traditional sense, hop-count generally means the number of intermediate routers a datagram has to go through between its source and the destination node. We analytically show that hop-count could be used to indicate proximity relative to an anchor node. Our proposed algorithm is computationally feasible for resource constrained wireless ad-hoc nodes, and gives reasonable accuracy. We perform both real experiments and simulations to evaluate the algorithm's performance. Experimental results show that our algorithm outperforms similar proximity based algorithms utilizing Received Signal Strength (RSS) and Expected Transmission Count (ETX). We also analyze the impact of various parameters like the number of anchor nodes, placements of anchor nodes and varying transmission powers of the nodes on the hop-count based localization algorithm's performance through simulation.

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“…where z k is the vector of measured range values, and v k is the measurement noise, which is assumed to be white with covariance matrix R. The covariance matrices for the 4-D EKF [32] have been chosen by exhaustive search over the space of matrices Q = I 4 and R = I 4 , with the objective of minimizing the estimation error. The search resulted in a 4-D EKF with Q = I 4 and R = 10 I 4 .…”

Section: Loosely Coupled Integration With the Extended Kalman Fimentioning

confidence: 99%

GNSS/Cellular Hybrid Positioning System for Mobile Users in Urban Scenarios

Angelis¹,

Baruffa

Cacopardi

2013

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a hybrid scheme for user positioning in an urban scenario using both a Global Navigation Satellite System (GNSS) and a mobile cellular network. To maintain receiver complexity (and costs) at a minimum, the location scheme combines the time-difference-of-arrival (TDOA) technique measurements obtained from the cellular network with GNNS pseudorange measurements. The extended Kalman filter (EKF) algorithm is used as a data integration system over the time axis. Simulated results, which are obtained starting from real measurements, demonstrate that the use of cellular network data may provide increased location accuracy when the number of visible satellites is not adequate. In every case, the obtained accuracy is within the limits required by emergency location services, e.g., Enhanced 911 (E911).

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TDOA positioning in NLOS scenarios by particle filtering

Cited by 37 publications

References 20 publications

Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes

Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes

A hop-count based positioning algorithm for wireless ad-hoc networks

GNSS/Cellular Hybrid Positioning System for Mobile Users in Urban Scenarios

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