The objective of this paper is to analyze the performance of an indoor positioning system based on the ultrasound level, usually called RSSI (received signal strength indicator). This paper builds on past experiences where RSSI values from beams sent in different directions were compared [1]. There relative values of RSSI were measured but herein we go one step further and perform localization based on absolute values of RSSI. The use of RSSI could potentially mean a simplification of the traditional positioning systems design in relation to other methods such as TOF (time-of-flight), being an alternative and interesting method for positioning. The distance between nodes (transmitter and receiver) is estimated from RSSI values using a signal propagation model in which the power losses due to the spherical divergence and atmospheric absorption are considered. However, in real conditions, other factors related to the characteristics of the nodes have their impact on RSSI measurements. One of them has to do with the beamwidth of the transducers. The signal strength will be conditioned by the angle of incidence of the transducers when these have a narrow beamwidth. On the other hand, the common use of wireless nodes in the deployment of these systems leads to the RSSI measurement to be affected by the battery level in the nodes. These effects introduce significant errors in the distance estimation and therefore on the localization precision. Herein we propose a mechanism for modeling the power loss due to the orientation of the ultrasonic transducers, as well as an algorithm to compensate for the effect of the variations in the battery level on RSSI measurements. Some experimental results have been obtained with 5 nodes (4 transmitters and 1 receiver) to show the quality of our compensation using a real positioning system. Location errors smaller than 10 cm for each coordinate are obtained in contrast to errors of several meters which are normally attained in this kind of systems based on RF (radio frequency) RSSI-values.
[1]
R. King.
Electromagnetic waves and antennas above and below the surface of the earth
,
1979
.
[2]
S. Holm,et al.
Airborne ultrasound data communications: the core of an indoor positioning system
,
2005,
IEEE Ultrasonics Symposium, 2005..
[3]
Andreas Savvides,et al.
An Empirical Characterization of Radio Signal Strength Variability in 3-D IEEE 802.15.4 Networks Using Monopole Antennas
,
2006,
EWSN.
[4]
A. J. Zuckerwar,et al.
Atmospheric absorption of sound: Further developments
,
1995
.
[5]
W.M. Waters,et al.
Bandpass Signal Sampling and Coherent Detection
,
1982,
IEEE Transactions on Aerospace and Electronic Systems.
[6]
Panarat Cherntanomwong,et al.
Indoor localization system using wireless sensor networks for stationary and moving target
,
2011,
2011 8th International Conference on Information, Communications & Signal Processing.
[7]
J.L. Brown,et al.
On Quadrature Sampling of Bandpass Signals
,
1979,
IEEE Transactions on Aerospace and Electronic Systems.
[8]
Sverre Holm,et al.
Robust ultrasonic indoor positioning using transmitter arrays
,
2010,
2010 International Conference on Indoor Positioning and Indoor Navigation.
[9]
Ángel de la Torre,et al.
Accurate time synchronization of ultrasonic TOF measurements in IEEE 802.15.4 based wireless sensor networks
,
2013,
Ad Hoc Networks.