SPEAR: Source Position Estimation for Anchor Position Uncertainty Reduction

Angjelichinoski, Marko; Denkovski, Daniel; Atanasovski, Vladimir; Gavrilovska, Liljana

doi:10.1109/lcomm.2014.020414.132780

Cited by 24 publications

(22 citation statements)

References 7 publications

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“…[4]- [7]. The second approach is based on optimization techniques such as semidefinite programming (SDP) or secondorder cone programming (SOCP) [8]- [10]. The SDP or SOCP-based solutions have better performance over TLS-based solutions but incur high computational cost.…”

Section: Introductionmentioning

confidence: 99%

RSSI-based self-localization with perturbed anchor positions

Kumar

Arablouei

Jurdak

et al. 2017

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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We consider the problem of self-localization by a resource-constrained mobile node given perturbed anchor position information and distance estimates from the anchor nodes. We consider normally-distributed noise in anchor position information. The distance estimates are based on the log-normal shadowing path-loss model for the RSSI measurements. The available solutions to this problem are based on complex and iterative optimization techniques such as semidefinite programming or second-order cone programming, which are not suitable for resource-constrained environments. In this paper, we propose a closed-form weighted least-squares solution. We calculate the weights by taking into account the statistical properties of the perturbations in both RSSI and anchor position information. We also estimate the bias of the proposed solution and subtract it from the proposed solution. We evaluate the performance of the proposed algorithm considering a set of arbitrary network topologies in comparison to an existing algorithm that is based on a similar approach but only accounts for perturbations in the RSSI measurements. We also compare the results with the corresponding Cramer-Rao lower bound. Our experimental evaluation shows that the proposed algorithm can substantially improve the localization performance in terms of both root mean square error and bias.

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Section: Introductionmentioning

confidence: 99%

RSSI-based self-localization with perturbed anchor positions

Kumar

Arablouei

Jurdak

et al. 2017

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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“…In [3] we investigate the effect of anchor position uncertainty on source localization performance and prove that severe accuracy degradations can occur. 30 In [4] we propose a joint localization framework, named Source Position Estimation for Anchor position uncertainty Reduction-SPEAR, that aims to jointly estimate the unknown positions of the sources and reduce the uncertainty of the anchor positions. The joint localization framework uses non-Bayesian estimation formalism since it models the unknown positions of the sources and the 35 uncertain anchors as deterministic parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The joint localization framework uses non-Bayesian estimation formalism since it models the unknown positions of the sources and the 35 uncertain anchors as deterministic parameters. [4] further introduces a Joint Maximum Likelihood (JML) localization algorithm as a typical representative of the joint localization framework, investigates its performance in typical scenarios and shows significant source localization improvements. Furthermore, it is shown that the JML can significantly reduce the initial anchor position uncer-40 tainty.…”

Section: Introductionmentioning

confidence: 99%

Geometric interpretation of theoretical bounds for RSS-based source localization with uncertain anchor positions

Denkovski

Angjelichinoski

Atanasovski

et al. 2017

Digital Signal Processing

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The Received Signal Strength based source localization can encounter severe problems originating from uncertain information about the anchor positions in practice. The anchor positions, although commonly assumed to be precisely known prior to the source localization, are usually obtained using previous estimation algorithm such as GPS. This previous estimation procedure produces anchor positions with limited accuracy that result in degradations of the source localization algorithm and topology uncertainty. We have recently addressed the problem with a joint estimation framework that jointly estimates the unknown source and uncertain anchors positions and derived the theoretical limits of the framework. This paper extends the authors previous work on the theoretical performance bounds of the joint localization framework with appropriate geometric interpretation of the overall problem exploiting the properties of semidefiniteness and symmetry of the Fisher Information Matrix and the Cramèr-Rao Lower Bound and using Information and Error Ellipses, respectively. The numerical results aim to illustrate and discuss the usefulness of the geometric interpretation. They provide in-depth insight into the geometrical properties of the joint localization problem underlining the various possibilities for practical design of efficient localization algorithms. Ellipse 65 For two-dimensional parameter estimation, such as network node localization, the ellipsoids become ellipses. In the case of the FIM, they are referred as Information Ellipses (IEs) and for the CRLB -Error Ellipses (EEs). This paper extends our previous work in [5] and focuses on the geometric interpretation of the joint localization framework and its appropriate theoretical bounds. The 70 geometrical interpretation of the FIM provides insights into the distribution of the information about the unknown positions that is stored in the observation vector by decoupling it into orthogonal spatial dimensions. Equivalently, the geometrical interpretation of the CRLB illustrates the estimation error spread, i.e. the variance of estimation error around the true value of the estimated parame-75ter. These insights are important since they allows deeper understanding of the localization problem. The geometrical interpretation also provides insights into the possibilities for maximizing the location information in terms of problem dimensioning, network topology design, specific node mobility patterns, channel modeling etc., considering specific limitations and conditions. This can assist 80 the definition of algorithmic rules for optimal combining of different information components, resulting in minimization of the estimation error.Considering the benefits that stem from the geometric interpretation of the performance bounds and related metrics, the contributions of this paper are summarized as follows. The paper introduces and discusses the geometric inter-85 pretation of the general problem of parameter estimation, as well as the problem of joint estimation, emphasizing ...

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“…Our algorithm proposed in [10], referred to as "WLSRP" hereafter, improves the WLSR algorithm by accounting for perturbation in the anchor position information as well. In contrast to the iterative and computationally complex solutions such as those based on second-order cone programming (SOCP) or semidefinite programming (SDP) [11]- [13], WLSR and WLSRP are closed-form and easy to implement making them suitable for localization in resource-constrained applications.…”

Section: Introductionmentioning

confidence: 99%

Multi-mode tracking of a group of mobile agents

Kumar

Arablouei

Jurdak

et al. 2017

2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC)

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We consider the problem of tracking a group of mobile nodes, which have limited computational and energy resources, using noisy RSSI measurements and position estimates available within the group. Existing solutions such as clusterbased GPS duty-cycling, individual tracking, and multilaterationbased localization and tracking can only partially deal with the challenges of dynamic grouping scenarios where neighbourhoods and resource availability may frequently change. To efficiently cope with these challenges, we propose a new group-based multi-mode tracking algorithm. The proposed algorithm takes the group size and resource availability into consideration and determines the best solution at any particular time instance. We consider a clustering approach where a cluster head assigns the task of GPS activation and coordinates the usage of resources among the cluster members. We evaluate the energy-accuracy trade-off of the proposed algorithm for various fixed sampling intervals. The evaluation is based on the 2D position tracks of 40 nodes simulated using Reynolds' flocking model. For a given energy budget, the proposed algorithm reduces the mean tracking error by up to 20% in comparison with the existing energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as accurate as the individual-based tracking while using around 50% less energy.

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SPEAR: Source Position Estimation for Anchor Position Uncertainty Reduction

Cited by 24 publications

References 7 publications

RSSI-based self-localization with perturbed anchor positions

RSSI-based self-localization with perturbed anchor positions

Geometric interpretation of theoretical bounds for RSS-based source localization with uncertain anchor positions

Multi-mode tracking of a group of mobile agents

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