Combined AOA/TOA UWB localization

Galler, Stefan; Gerok, Waldemar; Schroeder, J.; Kyamakya, Kyandoghere; Kaiser, Thomas

doi:10.1109/iscit.2007.4392171

Cited by 29 publications

(19 citation statements)

References 7 publications

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“…1 and it is practically implemented in [7]. At time step k, the range measurements at each receiver antenna can be obtained by multiplying the estimated TOA with the speed of wave propagation [6]. It this work, these measurements are modelled as…”

Section: System Descriptionmentioning

confidence: 99%

“…Unlike the above referenced methods which only employ a single antenna per anchor, several two-dimensional (2D) localization methods have been proposed that make use of anchors equipped with closely spaced antennas. For instance, in [6], a localization scheme is proposed based on the measurements of the TOAs and the TDOA at two neighboring antennas. The TDOA is used in turn to compute an angle-of-arrival (AOA) which is combined to the TOA to locate the target.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A joint localization and synchronization technique using time of arrival at multiple antenna receivers

Yousefi

Chang

Champagne

2013

2013 Asilomar Conference on Signals, Systems and Computers

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In this work, a system scheme is proposed for tracking a radio emitting target moving in two-dimensional space. The localization is based on the use of biased time-of-arrival (TOA) measurements obtained at two asynchronous receivers, each equipped with two closely spaced antennas. By exploiting the multi-antenna configuration and using all the TOA measurements up to current time step, the relative clock bias at each receiver and the target position are jointly estimated by solving a nonlinear least-squares (NLS) problem. To this end, a novel time recursive algorithm is proposed which fully takes advantage of the problem structure to achieve computational efficiency while using orthogonal transformations to ensure numerical reliability. The CramerRao lower bound (CRLB) is also derived as a lower bound on the error in estimating the position and biases. Simulation results show that the mean-squared error (MSE) of the proposed method approaches the CRLB closely.

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Section: System Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A joint localization and synchronization technique using time of arrival at multiple antenna receivers

Yousefi

Chang

Champagne

2013

2013 Asilomar Conference on Signals, Systems and Computers

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show abstract

“…[28] [29] [30] combine both TOA and AOA to localize mobile nodes in celluar networks. [31] [32] [33] analysis localization performance of AOA and TOA with UWB radio. However, to the best of our knowledge, no paper has implemented a localization system using both RSSIs and TOA like ours.…”

Section: Related Workmentioning

confidence: 99%

RoamingHART: A Collaborative Localization System on WirelessHART

Zhu

Huang

Han

et al. 2012

2012 IEEE 18th Real Time and Embedded Technology and Applications Symposium

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Abstract-Localization in wireless sensor networks is an important functionality that is required for tracking personnel and assets in industrial environments, especially for emergency response. Current commercial localization systems such as GPS suffer from the limitations of either high cost or low availability in many situations (e.g., in-door environments that exclude direct line-of-sight signal reception). The development of industrial wireless sensor networks such as WirelessHART provides an alternative. In this paper, we present the design and implementation of RoamingHART: a collaborative localization system on WirelessHART as an industrially viable solution. This solution is built upon several technological advances. First, RoamingHART adds the roaming functionality to WirelessHART and thus provides a means for keeping mobile WirelessHART devices connected to the network. Second, RoamingHART employs a collaborative framework to integrate different types of distance measurements into the location estimation algorithm by weighing them according to their precision levels. RoamingHART adopts several novel techniques to improve distance estimation accuracy and decreases the RSSI pre-survey cost. These techniques include introducing distance error range constraints to the measurements, judiciously selecting the initial point in location estimation and online updating the signal propagation models in the anchor nodes. Our implementation of RoamingHART can be applied to any WirelessHART-conforming network because no modification is needed on the WirelessHART field devices. We have implemented a complete RoamingHART system to validate both the design and the effectiveness of our localization algorithm. Our experiments show that the mobile device never drops out of the WirelessHART network while moving around; with the help of even one dependable anchor, using RSSI can yield at least 75% of distance errors below 5 meters, which is quite acceptable for many typical industrial automation applications.

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“…Ultra-wide band communications are specifically suited for TDOA distance estimation because of the large available bandwidth [7].…”

Section: Introductionmentioning

confidence: 99%

Correlation-based radio localization in an indoor environment

Callaghan

Czink

Mani

et al. 2011

J Wireless Com Network

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We investigate the feasibility of using correlation-based methods for estimating the spatial location of distributed receiving nodes in an indoor environment. Our algorithms do not assume any knowledge regarding the transmitter locations or the transmitted signal, but do assume that there are ambient signal sources whose location and properties are, however, not known. The motivation for this kind of node localization is to avoid interaction between nodes. It is most useful in non-line-of-sight propagation environments, where there is a lot of scattering. Correlation-based node localization is able to exploit an abundance of bandwidth of ambient signals, as well as the features of the scattering environment. The key idea is to compute pairwise cross correlations of the signals received at the nodes and use them to estimate the travel time between these nodes. By doing this for all pairs of receivers, we can construct an approximate map of their location using multidimensional scaling methods. We test this localization algorithm in a cubicle-style office environment based on both ray-tracing simulations, and measurement data from a radio measurement campaign using the Stanford broadband channel sounder. Contrary to what is seen in other applications of cross-correlation methods, the strongly scattering nature of the indoor environment complicates distance estimation. However, using statistical methods, the rich multipath environment can be turned partially into an advantage by enhancing ambient signal diversity and therefore making distance estimation more robust. The main result is that with our correlation-based statistical estimation procedure applied to the real data, assisted by multidimensional scaling, we were able to compute spatial antenna locations with an average error of about 2 m and pairwise distance estimates with an average error of 1.84 m. The theoretical resolution limit for the distance estimates is 1.25 m.

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Combined AOA/TOA UWB localization

Cited by 29 publications

References 7 publications

A joint localization and synchronization technique using time of arrival at multiple antenna receivers

A joint localization and synchronization technique using time of arrival at multiple antenna receivers

RoamingHART: A Collaborative Localization System on WirelessHART

Correlation-based radio localization in an indoor environment

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