Efficient solution and performance analysis of 3-D position estimation by trilateration

Manolakis, D.E.

doi:10.1109/7.543845

Cited by 274 publications

(197 citation statements)

References 12 publications

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“…Lateration. When ranges between landmarks and the mobile node can be determined, lateration is used to estimate position [59]. Figure 5a illustrates the method.…”

Section: Localization Phasementioning

confidence: 99%

A Survey on Localization for Mobile Wireless Sensor Networks

Amundson

Koutsoukos

2009

Lecture Notes in Computer Science

255

142

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Abstract. Over the past decade we have witnessed the evolution of wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation.

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“…Lateration. When ranges between landmarks and the mobile node can be determined, lateration is used to estimate position [59]. Figure 5a illustrates the method.…”

Section: Localization Phasementioning

confidence: 99%

A Survey on Localization for Mobile Wireless Sensor Networks

Amundson

Koutsoukos

2009

Lecture Notes in Computer Science

255

142

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“…This is especially interesting since we are usually not interested in the pose of the marker target itself but rather in the pose of an object rigidly connected to that marker. Note also that this kind of geometric analysis of constellation is used in other areas as well, for example to estimate the accuracy of GPS-based measurements [7].…”

Section: Interpretation Of Covariancesmentioning

confidence: 99%

Predicting and estimating the accuracy of n-occular optical tracking systems

Bauer

Schlegel

Pustka

et al. 2006

2006 IEEE/ACM International Symposium on Mixed and Augmented Reality

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Marker-based optical tracking systems are widely used in augmented reality, medical navigation and industrial applications. We propose a model for the prediction of the target registration error (TRE) in these kinds of tracking systems by estimating the fiducial location error (FLE) from two-dimensional errors on the image plane and propagating that error to a given point of interest. We have designed a set of experiments in order to estimate the actual parameters of the model for any given tracking system. We present the results of a study which we used to demonstrate the effect of different sources of error. The method is applied to real applications to show the usefulness for any kind of augmented reality system. We also present a set of tools that can be used to visualize the accuracy at design time.

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“…We can localize the source from its distance to three or more microphones at fixed positions. This method is called multilateration [12], and the source is localized by intersecting circles with radii equal to the measured distances, as illustrated in Fig. 2.…”

Section: Source Localizationmentioning

confidence: 99%

Source localization and tracking in non-convex rooms

Ocal

Vetterli

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider the estimation of the acoustic source position in a known room from recordings by a microphone array. We propose an algorithm that does not require the room to be convex, nor a line-of-sight path between the microphone array and the source to be present. Times of arrival of early echoes are exploited through the image source model, thereby transforming the indoor localization problem to a problem of localizing multiple sources in the free-field. The localized virtual sources are mirrored into the room using the image source method in the reverse direction. Further, we propose an optimization-based algorithm for improving the estimate of the source position. The algorithm minimizes a cost function derived from the geometry of the localization problem. We apply the designed optimization algorithm to track a moving source, and show through numerical simulations that it improves the tracking accuracy when compared with the naïve approach.

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Efficient solution and performance analysis of 3-D position estimation by trilateration

Cited by 274 publications

References 12 publications

A Survey on Localization for Mobile Wireless Sensor Networks

A Survey on Localization for Mobile Wireless Sensor Networks

Predicting and estimating the accuracy of n-occular optical tracking systems

Source localization and tracking in non-convex rooms

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