Unified performance measures in network localization

Journal article
(Original article)

Publication Details

Author(s): Nowak T, Hartmann M, Thielecke J
Journal: Eurasip Journal on Advances in Signal Processing
Publication year: 2018
Volume: 48
Journal issue: Network Localization
Pages range: 1-17
ISSN: 1687-6180


With the evolving Internet of Things, location-based
services have recently become very popular. For modern wireless sensor
networks (WSNs), ubiquitous positioning is elementary. Hence, the demand
of everlasting and low-cost sensor nodes is rapidly increasing. In
terms of energy-efficiency, received signal strength (RSS)-based
direction finding is a prospective approach providing location
information in low-power sensor networks. Unfortunately, RSS-based
direction finding is, as radio-based localization is in general, prone
to multipath propagation of the wireless channel. Therefore, the impact
of multipath fading as well as all other error source have to be modeled
correctly and have to be considered in the design of a locating WSN.

this paper, we derive the classical Cramér-Rao Lower Bound (CRLB) for
RSS-based direction-of-arrival (DOA). The drawbacks of the classical
CRLB and its influence on the optimal network topology are discussed.
The CRLB indicates that the minimum variance unbiased estimator (MVUE)
does not exist for the problem of RSS-based DOA due to the nature of its
measurement function. Hence, beyond the CRLB, we derive performance
metrics for the maximum likelihood estimator (MLE) and compare position
estimation errors for the MVUE and the MLE for different network
topologies. Since both approaches, the CRLB and the maximum likelihood
(ML) limits, are not capable of handling ambiguities, we introduce
another measure for the variance of a measurement and its corresponding
position estimate based on information theory. This way, the amount of
information for a set of RSS measurements can be quantified exactly,
even in the case of ambiguous probability densities. Thus, the proposed
technique gives a holistic view on the information obtained from sensor
measurements which can be utilized for network topology optimization.

FAU Authors / FAU Editors

Hartmann, Markus
Lehrstuhl für Informationstechnik mit dem Schwerpunkt Kommunikationselektronik (Stiftungslehrstuhl)
Nowak, Thorsten
Lehrstuhl für Informationstechnik mit dem Schwerpunkt Kommunikationselektronik (Stiftungslehrstuhl)
Thielecke, Jörn Prof. Dr.
Professur für Informationstechnik (Schwerpunkt Ortsbestimmung und Navigation)

How to cite

Nowak, T., Hartmann, M., & Thielecke, J. (2018). Unified performance measures in network localization. Eurasip Journal on Advances in Signal Processing, 48(Network Localization), 1-17. https://dx.doi.org/10.1186/s13634-018-0570-8

Nowak, Thorsten, Markus Hartmann, and Jörn Thielecke. "Unified performance measures in network localization." Eurasip Journal on Advances in Signal Processing 48.Network Localization (2018): 1-17.


Last updated on 2019-06-01 at 05:10