Distance Matrix Reconstruction from Incomplete Distance Information for Sensor Network Localization

Drineas, Petros; Javed, Asif; Magdon‐Ismail, Malik; Pandurangan, Gopal; Virrankoski, Reino; Savvides, Andreas

doi:10.1109/sahcn.2006.288510

Cited by 61 publications

(79 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fully connected network assumption is one obvious drawback of the proposed approaches. In case of partially connected isotropic networks, it is possible to use the average hop length and hop counts between indirectly connected nodes [5] or other means [16] to deduce the remaining requisite distances for the multidimensional similarity matrix construction. Or we can borrow the idea of [17] by building local maps from the fully connected subsets in the WSNs and then patching them together for global positioning.…”

Section: Discussionmentioning

confidence: 99%

“…In the conventional MUSIC approach, the DOAs are estimated from the noise subspace through a line search or nonlinear optimization. On the other hand, in the full-set subspace algorithm, a closed-form expression of (20) is available for estimation of the node positions as they are linear in (15) or (16).…”

Section: A Full-set Subspace Algorithmmentioning

confidence: 99%

“…We assume a fully connected WSN, which is a frequently used assumption, say in the classical multidimensional scaling (MDS) [15]- [17] for localization where distance measurements between all pairs of sensors are required. For a partially connected WSN, there exist techniques for estimating the missing distance information such as shortest-path distance computation [15] and reconstruction based on singular value decomposition [16]. Alternatively, local maps can first be built from the fully connected subsets in the WSN and then patched together to form the global map [17].…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Novel Subspace Approach for Cooperative Localization in Wireless Sensor Networks Using Range Measurements

Chan

2009

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Abstract-Estimating the positions of sensor nodes is a fundamental and crucial problem in wireless sensor networks. In this paper, three novel subspace methods for node localization in a fully connected network are devised with the use of range measurements. Biases and mean square errors of the sensor node position estimates are also derived. Computer simulations are included to contrast the performance of the proposed algorithms with the conventional subspace positioning method, namely, classical multidimensional scaling, as well as Cramér-Rao lower bound.Index Terms-Position estimation, range-based measurements, subspace method, wireless sensor networks.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: A Full-set Subspace Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A Novel Subspace Approach for Cooperative Localization in Wireless Sensor Networks Using Range Measurements

Chan

2009

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…♦ It would also be interesting to apply the methods of this section to sensor network localization problems requiring the reconstruction of an incomplete distance matrix. See, for example, [21,35,25]. The five proteins not shown in Figure 2.…”

Section: 12])mentioning

confidence: 99%

Douglas-Rachford feasibility methods for matrix completion problems

Artacho

Borwein²,

Tam³

2014

ANZIAMJ

View full text Add to dashboard Cite

show abstract

“…The distance matrix after applying a simple transformation has very low rank as stated through the following lemma [23]. Lemma 1.…”

Section: Euclidean Distance Matrix Completionmentioning

confidence: 99%

Ad-hoc microphone array calibration from partial distance measurements

Taghizadeh

Asaei

Garner

et al. 2014

2014 4th Joint Workshop on Hands-Free Speech Communication and Microphone Arrays (HSCMA)

View full text Add to dashboard Cite

We address the problem of ad hoc microphone array calibration where some of the distances between the microphones can not be measured. The conventional techniques require information about all the distances for accurate reconstruction of the array geometry. To alleviate this condition, we propose to exploit the properties of Euclidean distance matrices within the framework of low-rank matrix completion to recover the missing entries. We provide rigorous analysis to bound the calibration error using noisy measurements. This study elucidates the links between the performance and the structure of the missing distances, along with the size of the network. The experiments carried out on real data recordings demonstrate these theoretical insights. A significant improvement is achieved by the proposed Euclidean distance matrix completion algorithm over the state-of-the-art techniques for ad hoc microphone array calibration.

show abstract

Distance Matrix Reconstruction from Incomplete Distance Information for Sensor Network Localization

Cited by 61 publications

References 7 publications

A Novel Subspace Approach for Cooperative Localization in Wireless Sensor Networks Using Range Measurements

A Novel Subspace Approach for Cooperative Localization in Wireless Sensor Networks Using Range Measurements

Douglas-Rachford feasibility methods for matrix completion problems

Ad-hoc microphone array calibration from partial distance measurements

Contact Info

Product

Resources

About