Cooperative Bayesian Self-Tracking for Wireless Networks

Wymeersch, Henk; Ferner, Ulric J.; Win, Moe Z.

doi:10.1109/lcomm.2008.080419

Cited by 25 publications

(17 citation statements)

References 4 publications

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“…We refer to this as the geometric relationship of multipath propagation. 12 Such a relationship may appear to be useful for localization at first glance. This, however, is not the case when the MPCs are resolvable.…”

Section: B Geometric Relationship Of Multipath Propagationmentioning

confidence: 99%

“…Hence, 11 Since wider transmission bandwidths translate to higher multipath resolution, dominant MPCs can be resolved via the use of wide bandwidth signals [16]- [20]. 12 If the reflection is not ideal, for example the wall is not flat in Fig. 2, then the corresponding MPCs at the array can be thought of as direct paths coming from different virtual anchors.…”

Section: B Geometric Relationship Of Multipath Propagationmentioning

confidence: 99%

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On the accuracy of localization systems using wideband antenna arrays

Shen

Win

2010

IEEE Trans. Commun.

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131

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Abstract-Accurate positional information is essential for many applications in wireless networks. Time-of-arrival (TOA) and angle-of-arrival (AOA) are the two most commonly used signal metrics for localizing nodes with unknown positions. In this paper, we consider a wireless network in which each node is equipped with a wideband antenna array capable of performing both TOA and AOA measurements. Since both the position and orientation of the agent are of interest, we propose a localization framework that jointly estimates these two parameters. The notion of equivalent Fisher information is applied to derive the squared error bounds for the position and orientation. Since our analysis starts from the received waveforms rather than directly from the signal metrics, these bounds characterize the fundamental limits of the position and orientation accuracy. Surprisingly, our result reveals that AOA measurements obtained by wideband antenna arrays do not further improve position accuracy beyond that provided by TOA measurements.Index Terms-Localization, wideband antenna array, equivalent Fisher information (EFI), Cramèr-Rao bound (CRB), squared position error bound (SPEB), squared orientation error bound (SOEB).

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Section: B Geometric Relationship Of Multipath Propagationmentioning

confidence: 99%

Section: B Geometric Relationship Of Multipath Propagationmentioning

confidence: 99%

On the accuracy of localization systems using wideband antenna arrays

Shen

Win

2010

IEEE Trans. Commun.

Self Cite

131

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“…Then (60) Proof: Invoking (50), and noticing that in the source localization case and reduce to and , as can be found by setting in (56) and (46), respectively, we obtain (61) where are as in (10) (62) where denote the th smallest eigenvalue of a matrix and , since has a single eigenvalue of multiplicity .…”

Section: Theorem T3 (Initial Smoothing Parameter: Source Loc)mentioning

confidence: 99%

“…Finally, notice that in order for inequality (61) to hold it is sufficient it that is nonnegative, which under the fact that is positive semidefinite, i.e., , is equivalent to…”

Section: Theorem T3 (Initial Smoothing Parameter: Source Loc)mentioning

confidence: 99%

On the Maximum Likelihood Approach for Source and Network Localization

Destino

Abreu

2011

IEEE Trans. Signal Process.

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We consider the source and network localization problems, seeking to strengthen the relationship between the Weighted-Least-Square (WLS) and the Maximum-Likelihood (ML) solutions of these problems. To this end, we design an optimization algorithm for source and network localization under the principle that: a) the WLS and the ML objectives should be the same; and b) the solution of the ML-WLS objective does not depend on any information besides the set of given distance measurements ( observations). The proposed Range-Global Distance Continuation (R-GDC) algorithm solves the localization problems via iterative minimizations of smoothed variations of the WLS objective, each obtained by convolution with a Gaussian kernel of progressively smaller variances. Since the last (not smoothed) WLS objective derives directly from the ML formulation of the localization problem, and the R-GDC requires no initial estimate to minimize it, final result is maximum-likelihood approach to source and network localization problems. The performance of the R-GDC method is compared to that of state-of-the-art techniques such as semidefinite programming (SDP), nonlinear Newton least squares (NLS), and the Stress-of-a-MAjorizing-Complex-Objective-Function (SMACOF) algorithms, as well as to the Cramér-Rao LowerBound (CRLB). The comparison reveals that the solutions obtained with the R-GDC algorithm is insensitive to initial estimates and provides a localization error that closely approaches that of the corresponding fundamental bounds. The R-GDC is also found to achieve a complexity order comparable to that of the SMACOF, which is known for its efficiency.

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“…From the localization perspective, a substantial portion of work is focused on collaborative or cooperative localization, whereby MDs gather position-related observations amongst themselves in addition to their observations from BSs and simultaneously estimate their locations. While analysis is known to be hard and the majority of papers eventually resort to simulation-based insights (e.g., see [26]- [29]), the results of [21] can be used to show that enhancing a classical localization procedure with collaboration is beneficial to positioning accuracy. Specifically, a device making just a single collaborative connection to a secondary device meeting some minimum BS connectivity condition enjoys a strict reduction in the Cramér-Rao lower bound (CRLB) of its positioning error.…”

Section: A Prior Art and Motivationmentioning

confidence: 99%

A Tractable Analysis of the Improvement in Unique Localizability Through Collaboration

Schloemann

Dhillon

Buehrer

2016

IEEE Trans. Wireless Commun.

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In this paper, we mathematically characterize the improvement in device localizability achieved by allowing collaboration among devices. Depending on the detection sensitivity of the receivers in the devices, it is not unusual for a device to be localized to lack a sufficient number of detectable positioning signals from localized devices to determine its location without ambiguity (i.e., to be uniquely localizable). This occurrence is well-known to be a limiting factor in localization performance, especially in communications systems. In cellular positioning, for example, cellular network designers call this the hearability problem. We study the conditions required for unique localizability and use tools from stochastic geometry to derive accurate analytic expressions for the probabilities of meeting these conditions in the noncollaborative and collaborative cases. We consider the scenario without shadowing, the scenario with shadowing and universal frequency reuse, and, finally, the shadowing scenario with random frequency reuse. The results from the latter scenario, which apply particularly to cellular networks, reveal that collaboration between two devices separated by only a short distance yields drastic improvements in both devices' abilities to uniquely determine their positions. The results from this analysis are very promising and motivate delving further into techniques which enhance cellular positioning with small-scale collaborative ranging observations among nearby devices.

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Cooperative Bayesian Self-Tracking for Wireless Networks

Cited by 25 publications

References 4 publications

On the accuracy of localization systems using wideband antenna arrays

On the accuracy of localization systems using wideband antenna arrays

On the Maximum Likelihood Approach for Source and Network Localization

A Tractable Analysis of the Improvement in Unique Localizability Through Collaboration

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