Anchor free node tracking using ranges, odometry, and multidimensional scaling

Beck, Brian; Baxley, Robert J.

doi:10.1109/icassp.2014.6853991

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…In a mobile network over time, the estimation of node position is difficult without prior knowledge of node or pre-surveyed reference node (anchor node). The problem was addressed in [259] by using odometry data and UWB range measurements in a multidimensional scaling (MDS) framework. The MDS paradigm was exploited for both measurement data in this anchorfree indoor tracking system.…”

Section: Reference-free Approachmentioning

confidence: 99%

Review of Indoor Positioning: Radio Wave Technology

Geok

Aung

et al. 2020

Applied Sciences

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The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.

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Section: Reference-free Approachmentioning

confidence: 99%

Review of Indoor Positioning: Radio Wave Technology

Geok

Aung

et al. 2020

Applied Sciences

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“…Common anchor-based localization methods such as those based on Time Difference of Arrival (TDoA) and Time of Arrival (TOA) measurements require careful infrastructure setup. When there are no nodes with known locations, anchor-free solutions like multi-dimensional scaling (MDS) solutions are used to map pairwise distance measurements into a geometry of nodes which generated them [28], [29], [30]. However, tracking nodes with MDS to predict collision is difficult as MDS solutions are only up to rotation, reflection and translation.…”

Section: Related Workmentioning

confidence: 99%

Collision Prediction from UWB Range Measurements

Abrar¹,

Luong²,

Spencer³

et al. 2020

Preprint

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The ability to predict, and thus react to, oncoming collisions among a set of mobile agents is a fundamental requirement for safe autonomous movement, both human and robotic. This paper addresses systems that use range measurements between mobile agents for the purpose of collision prediction, which involves prediction of the agents' future paths to know if they will collide at any time. One straightforward system would use known-location static anchors to estimate agent coordinates over time, and use the track to predict collision. Fundamentally, no fixed coordinate system is required for collision prediction, so using only the pairwise range between two agents can be used to predict collision. We present lower bound analysis which shows the limitations of this pairwise method. As an alternative anchor-free method, we propose the friend-based autonomous collision prediction and tracking (FACT) method that uses all measured ranges between nearby (unknown location mobile) agents, in a distributed algorithm, to estimate their relative locations and velocities and predict future collisions between agents. Using analysis and simulation, we show the potential for FACT to achieve equal or better collision detection performance compared to other methods, while avoiding the need for anchors. We then build a network of N ultra wideband (UWB) devices and an efficient multi-node protocol which allows all O(N 2 ) pairwise ranges to be measured in N slots. We run experiments with up to six independent robot agents moving and colliding in a 2D plane and up to four anchor nodes to compare the performance of the collision prediction methods. We show that the FACT method can perform better than either other method but without the need for a fixed infrastructure of anchor nodes.

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“…In Xu et al [15], the authors use DMDS for visualizing the temporal evolution of dynamic networks. Beck and Baxley [16] proposed to use DMDS as a methodology to track nodes over time by exploiting odometry information.…”

Section: Related Workmentioning

confidence: 99%

“…The choice of the value of p varies according to the context of the applications. For offline trajectory reconstruction, a high number of p steps (p = 10) should improve the accuracy, but it inevitably increases the computational complexity since it is linear with p. For online tracking, p should be set as a low values such as p = 2, or p = 3, as suggested in [16].…”

Section: Dynamic Multidimensional Scalingmentioning

confidence: 99%