Conference contribution
(Original article)


Fundamental Limits in RSSI-based Direction-of-Arrival Estimation


Publication Details
Author(s): Nowak T, Hartmann M, Patino-Studencki L, Thielecke J
Publication year: 2016
Conference Proceedings Title: 2016 13th Workshop on Positioning, Navigation and Communications (WPNC)

Event details
Event: 2016 13th Workshop on Positioning, Navigation and Communications (WPNC)
Event location: Bremen
Start date of the event: 19/10/2016
End date of the event: 20/10/2016
Language: English

Abstract

The use of wireless sensor networks is rapidly increasing. Also the demand of ubiquitous location sensors is swiftly expanding. Hence, energy and location-awareness come into focus of research today. A prospective approach for low-power locating sensor networks is received signal strength indicator (RSSI)-based direction finding. The presented approach is based on RSSI difference measurements retrieved by a array of directed antennas. In this paper, fundamental limits of RSSI-based direction finding are evaluated, beyond the Cramér-Rao Lower Bound (CRLB). That is not applicable for the design of a localization system topology due to the nature of the gain difference function that leads to an unbounded variance of the unbiased estimator. Thus, a maximum likelihood (ML) approach to the RSSI-based direction finding is presented. The ML estimator yields a limited variance for all signal directions. However, that benefit comes at the expense of being biased. Beyond treating direction estimates, mean square position errors are compared for both, the unbiased and the ML estimator.



How to cite
APA: Nowak, T., Hartmann, M., Patino-Studencki, L., & Thielecke, J. (2016). Fundamental Limits in RSSI-based Direction-of-Arrival Estimation. In 2016 13th Workshop on Positioning, Navigation and Communications (WPNC). Bremen, DE.

MLA: Nowak, Thorsten, et al. "Fundamental Limits in RSSI-based Direction-of-Arrival Estimation." Proceedings of the 2016 13th Workshop on Positioning, Navigation and Communications (WPNC), Bremen 2016.

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