Radiation pattern correlation for mobile robot localization in low power wireless networks

Graefenstein, Juergen; Albert, Amos; Biber, Peter

doi:10.1109/robot.2009.5152379

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Low-power radio localization, which is usually based on mapping signal strength (RSSI) to distance [14], is unreliable due to obstacles, multipath fading, and noise [1]. In addition to RSSI, other methods for radio localization measure time difference of arrival or angle of arrival, but neither are in common use because they require special equipment, as noted in [6]. Graefenstein et al [6] propose a system that maps RSSI to distance and direction measurements, which they conclude provides better localization compared to direction-only techniques.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to RSSI, other methods for radio localization measure time difference of arrival or angle of arrival, but neither are in common use because they require special equipment, as noted in [6]. Graefenstein et al [6] propose a system that maps RSSI to distance and direction measurements, which they conclude provides better localization compared to direction-only techniques. For the moment, we ignore the time-varying nature of the RSSI topography, but we note that Guha and Sarkar [7] address this issue in their model of signal strength in wireless networks.…”

Section: Related Workmentioning

confidence: 99%

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Simultaneous localization and environmental mapping with a sensor network

Marinakis

Macmillan

Allen

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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In this paper, we present an algorithm for simultaneously refining a probability distribution function (PDF) for the pose of a sensor network (i.e. the locations of the sensors), and inferring the spatial variations of measured environmental parameters. Our approach iteratively refines a network pose PDF by assuming that environmental parameters vary smoothly. Both our physical experiments, which sensed wireless signal strength as the environmental variable, and our numerical simulations demonstrate that the approach has promise.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Simultaneous localization and environmental mapping with a sensor network

Marinakis

Macmillan

Allen

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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“…Therefore a rough initial signal propagation model is used to reduce the amount of work in calibration phase. Another approach is presented in [24], where the anisotropy of the antenna gain is exploited to determine heading and integrity of the position, estimated by measured signal strength, of a mobile robot.…”

Section: Fig 1 -Example For a Radio Mapmentioning

confidence: 99%

Mobile Robot Localization Using Wlan, Odometry and Gyroscope Data

Lategahn¹,

Kuenemund²,

Roehrig³

2010

IJC

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In this paper a method for estimation of position and motion of a mobile robot in an indoor environment is introduced. The proposed method uses WLAN signal strength to estimate the global position of a mobile robot in an office building. Thus signal strengths of the received access points are stored in the radio map in calibration phase. In localization phase the stored values are compared with actually measured one’s. Therefore a fingerprinting algorithm, that was introduced before, is used. The improvement of the presented work is the multi sensor fusion using Kalman filter, which enhances the accuracy of fingerprinting algorithms and tracking of the robot. For this reason odometric and gyroscopic sensors of the robot are fused with the estimated position of the fingerprinting algorithm. The paper presents the experimental results of measurements made in an office building.

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“…A previous study of a tracking-based PES with a portable tracer made of rotatable unidirectional antennas showed that active target tracking was possible with Bayesian inference, based on a priori received signal strength (RSS) data [4]. However, a PES using a unidirectional antenna exhibited the gain mismatch problem caused by unsharp patterns, back-lobes, and side-lobes, generating considerable errors in direction and distance estimation [5]. In this paper, we propose and demonstrate a novel relative position indicator (RPI) system composed of a radial antenna array (RAA) and using radial vector signal processing.…”

Section: Introductionmentioning

confidence: 99%

Accurate relative position indicator for tracking-based position estimation system

Son

Burgess

Bang

et al. 2014

IEICE Electron. Express

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A relative position indicator (RPI) system with radial antenna array (RAA) is proposed and experimentally demonstrated for use in a tracking-based position estimation system (PES). Utilizing radial vectors generated by both received signal strength (RSS) and azimuth direction of each antenna element in the RAA, accurate direction and distance-to-target can be obtained simultaneously. The proposed RPI system using a radial vector sum and a simple equalization process can mitigate the gain mismatch problem, which was a serious problem in previous tracking-based PESs.

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Radiation pattern correlation for mobile robot localization in low power wireless networks

Cited by 8 publications

References 12 publications

Simultaneous localization and environmental mapping with a sensor network

Simultaneous localization and environmental mapping with a sensor network

Mobile Robot Localization Using Wlan, Odometry and Gyroscope Data

Accurate relative position indicator for tracking-based position estimation system

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