Multipath exploited Bayesian and Cramér-Rao bounds for single-sensor target localization

Setlur, Pawan; Devroye, Natasha

doi:10.1186/1687-6180-2013-53

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…As they both remove the reflection signal paths, the limited information acquired by each antenna array entails a set of such arrays whose individual measurements can then be synthesized to reach a location estimation for the signal source. Although the theoretical bounds of exploiting multipath have been recently studied [21], our iLocScan is the first system prototype to realize such ideas in the 2.4GHz band.…”

Section: Exploiting Physical Layer Informationmentioning

confidence: 99%

iLocScan

Zhang

Luo

et al. 2014

Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems

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Whereas a few physical layer techniques have been proposed to locate a signal source indoors, they all deem multipath a "curse" and hence take great efforts to cope with it. Consequently, each sensor only obtains the information about the direct path; this necessitates a networked sensing system (hence higher system complexity and deployment cost) with at least three sensors to actually locate a source.In this paper, we deem multipath a "bless" and thus innovatively exploit the power of it. Essentially, with minor knowledge of the geometry of an indoor space, each signal path may potentially contribute a new piece of information to the location of its source. As a result, it is possible to locate the source with only one hand-held device. At the same time, the extra information provided by multipath can help to at least partially reconstruct the geometry of the indoor space, which enables a floor plan generation process missing in most of the indoor localization systems.To demonstrate these ideas, we implement a USRP-based radio sensor prototype named iLocScan; it can simultaneously scan an indoor space (hence generate a plan for it) and position the signal source in it. Through iLocScan, we mainly aim to showcase the feasibility of harnessing multipath in assisting indoor localization, rather than to rival existing proposals in terms of localization accuracy. Nevertheless, our experiments show that iLocScan offers satisfactory results on both source localization and space scanning.

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Section: Exploiting Physical Layer Informationmentioning

confidence: 99%

iLocScan

Zhang

Luo

et al. 2014

Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems

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“…It follows directly from (45) that the weights of the Gaussian components are updated by (4) initialize the new transmitter(s) based on z ; (5) if track of any signal components lost then (6) delete the corresponding transmitter(s); (7) for = 1, . .…”

Section: Derivation Of the Particle Weight Calculationmentioning

confidence: 99%

“…The authors of [5,6] derived some theoretical bounds for multipath assisted positioning. Multipath assisted positioning schemes have, for example, been applied in radar applications [7], using ultrawideband (UWB) [8,9] or 5G [10] systems and in cooperative systems [11].…”

Section: Introductionmentioning

confidence: 99%

Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning

Ulmschneider

Gentner

Jost

et al. 2018

Journal of Electrical and Computer Engineering

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In multipath assisted positioning, multipath components arriving at a receiver are regarded as being transmitted by a virtual transmitter in a line-of-sight condition. As the locations and clock offsets of the virtual and physical transmitters are in general unknown, simultaneous localization and mapping (SLAM) schemes can be applied to simultaneously localize a user and estimate the states of physical and virtual transmitters as landmarks. Hence, multipath assisted positioning enables localizing a user with only one physical transmitter depending on the scenario. In this paper, we present and derive a novel filtering approach for our multipath assisted positioning algorithm called Channel-SLAM. Making use of Rao-Blackwellization, the location of a user is tracked by a particle filter, and each landmark is represented by a sum of Gaussian probability density functions, whose parameters are estimated by unscented Kalman filters. Since data association, that is, finding correspondences among landmarks, is essential for robust longterm SLAM, we also derive a data association scheme. We evaluate our filtering approach for multipath assisted positioning by simulations in an urban scenario and by outdoor measurements.

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“…In [22] and [23] we considered single sensor localization in urban scenarios. In such urban scenarios, radar returns consist of a direct path return along with multipath returns from signal reections off surfaces such as building walls or oors.…”

Section: Topic 2: Multipath Exploitation In Radar Systemsmentioning

confidence: 99%

Fundamental Bounds on Information Fusion with Focus on Waveform-based Intent Detection and Avoidance

Devroye¹

2013

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Standard Form 298 (Rev. 8/98) REPORT DOCUMENTATION PAGE Prescribed by ANSI Std. Z39.18Form The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to the Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

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Multipath exploited Bayesian and Cramér-Rao bounds for single-sensor target localization

Cited by 14 publications

References 22 publications

iLocScan

iLocScan

Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning

Fundamental Bounds on Information Fusion with Focus on Waveform-based Intent Detection and Avoidance

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