Maximum likelihood localization estimation based on received signal strength

Waadt, Andreas; Kocks, Christian; Wang, Shangbo; Bruck, Guido H.; Jung, Peter

doi:10.1109/isabel.2010.5702817

Cited by 16 publications

(14 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While LSE requires little prior training or estimation (other than the path loss exponent), as our results in this work demonstrate, it performs best under low fading variance. The high-performance, most sophisticated approach to pure RSS-based localization is maximum likelihood estimation [4]; however it requires an online, accurate, estimation of the fading variance and other channel parameters, and incurs a high algorithmic complexity.…”

Section: Related Workmentioning

confidence: 99%

“…It is desired to have good localization accuracy without any additional infrastructure cost. Numerous techniques such as fingerprinting [1] [2], least squares estimation [3], maximum likelihood estimation (MLE) [4], and sequence-based localization (SBL) [5] have been developed and deployed to achieve this goal. In all these techniques, Received Signal Strength (RSS) from the neighboring access points (beacons) is used to deduce the position of the receiver.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Harnessing Non-Uniform Transmit Power Levels for Improved Sequence Based Localization

Deora

Krishnamachari

2014

2014 IEEE International Conference on Distributed Computing in Sensor Systems

View full text Add to dashboard Cite

Sequence-based localization (SBL) is a technique whereby a node is localized based on the ranked sequence of signal strengths obtained from a set of beacon nodes. SBL effectively partitions the area into regions corresponding to unique ranked sequences. Prior work has developed SBL under the assumption that all beacons have the same transmit power. In this work, we consider beacons with unequal transmit power for sequencebased localization and present heuristic algorithms for joint transmit power optimization and beacon placement. We show through comprehensive simulations that a novel enhancement of SBL utilizing optimized non-uniform transmit powers, coupled with careful beacon placement, which we refer to as NU-SBL, can dramatically improve the area partitioning compared to traditional SBL. However, in evaluating these schemes under stochastic fading, we find that the original SBL with optimized location performs nearly as well or slightly better than NU-SBL in many cases. We introduce another scheme, that we refer to as NU-SBL-ZOOM, which further allows the power levels to be optimized non-uniformly so as to focus in on a particular smaller region within the larger localization space. NU-SBL-ZOOM is found to perform much better in terms of both area partitioning as well as location error in the presence of fading.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Harnessing Non-Uniform Transmit Power Levels for Improved Sequence Based Localization

Deora

Krishnamachari

2014

2014 IEEE International Conference on Distributed Computing in Sensor Systems

View full text Add to dashboard Cite

show abstract

“…Several approaches have been proposed in the literature to tackle the problem of node localization by leveraging information extracted from existing terrestrial technologies [1][2][3][4][5][6]. Among others, range-based solutions have been widely employed in popular terrestrial localization systems, especially in WSNs, where they are preferred to range-free techniques [7,8] thanks to their reduced complexity. Range information can be obtained by exploiting different characteristics of the received signals, namely received signal strength (RSS) [9][10][11][12][13][14][15][16], time (difference) of arrival (TOA/TDOA) [17][18][19][20], and angle of arrival (AOA) [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks

Coluccia

Fascista

2019

JSAN

View full text Add to dashboard Cite

The paper addresses the problem of localization based on hybrid received signal strength (RSS) and time of arrival (TOA) measurements, in the presence of synchronization errors among all the nodes in a wireless network, and assuming all parameters are unknown. In most existing schemes, in fact, knowledge of the model parameters is postulated to reduce the high dimensionality of the cost functions involved in the position estimation process. However, such parameters depend on the operational wireless context, and change over time due to the presence of dynamic obstacles and other modification of the environment. Therefore, they should be adaptively estimated “on the field”, with a procedure that must be as simple as possible in order to suit multiple real-time re-calibrations, even in low-cost applications, without requiring human intervention. Unfortunately, the joint maximum likelihood (ML) position estimator for this problem does not admit a closed-form solution, and numerical optimization is practically unfeasible due to the large number of nuisance parameters. To circumvent such issues, a novel two-step algorithm with reduced complexity is proposed: A first calibration phase exploits nodes in known positions to estimate the unknown RSS and TOA model parameters; then, in a second localization step, an hybrid TOA/RSS range estimator is combined with an iterative least-squares procedure to finally estimate the unknown target position. The results show that the proposed hybrid TOA/RSS localization approach outperformed state-of-the-art competitors and, remarkably, achieved almost the same accuracy of the joint ML benchmark but with a significantly lower computational cost.

show abstract

“…Range-based methods make use of the received signal in order to estimate the distance between the source and the receiving sensor node. The distance information can be extracted from different measurements of the received signal, like time of arrival, time difference of arrival and received signal strength, to name a few [16][17][18]. Nowadays, these different measurements are commonly integrated together, or combined with angle of arrival observations in order to enhance the localization accuracy [19,20].…”

Section: Introductionmentioning

confidence: 99%

Elephant Herding Optimization for Energy-Based Localization

Correia¹,

Beko²,

Cruz³

et al. 2018

Preprint

View full text Add to dashboard Cite

This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. More specifically an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

show abstract

Maximum likelihood localization estimation based on received signal strength

Cited by 16 publications

References 9 publications

Harnessing Non-Uniform Transmit Power Levels for Improved Sequence Based Localization

Harnessing Non-Uniform Transmit Power Levels for Improved Sequence Based Localization

Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks

Elephant Herding Optimization for Energy-Based Localization

Contact Info

Product

Resources

About