IEEE Systems, Man and Cybernetics Magazine - October 2020 - 33

spread of possible locations, with a clustering around the
ground truth. The result near (40,37) directly depicts how
this method could benefit from particle filtering.
Conclusion and Future Work
This method allows for a position update utilizing present RSSI values. These values can be polled at a faster
rate than GNSS position updates allow. Additionally, as
it is based upon signals that are orders of magnitude
greater in signal strength as compared to GNSS, it is
able to operate in regions where GNSS is unavailable
to provide a position update. This method has a maximum spatial resolution of 0.5 m and a spatial accuracy
that is dependent upon the ability for a measurement
point to converge with a value; as a result, the accuracy is variable.
It is noteworthy that while the method shows promise,
several improvements are needed to fully realize a robust
integrated navigation solution between GNSS, inertial
inputs, and RSSI positioning. Additionally, the effect of RF
propagation in heated compartments needs to be further
investigated. It is possible that ducting due to thermal
boundaries could increase the RSSI channel flicker, adding additional error to the model.
The method could immediately benefit from additional
aiding sensors to determine the location of boundaries. By
logging and tracking physical boundaries, the model can
be refined on the fly using in situ measurements on alternate sides of boundaries. Additionally, unique features can
be utilized as correction points for the IMU.
The system could be expanded to use multiple simultaneous streams of data to fuse the models between users.
This would enable the users to track each other and allow
for the rapid generation of a valid model for each of the
BSSID RSSIs in question.

navigation and radio-frequency-based aiding sensors, electronic warfare, multiagent robotics, wireless communications, control systems, sensor networks, and soft computing. He is the associate editor of IEEE Systems Journal and
a Senior Member of IEEE.
References
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land vehicles," M.S. thesis, Royal Inst. Technol., Stockholm, Sweden, 2006.
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2020. doi: 10.3390/s20102818.

About the Authors
Ryan Schwingle (ryan.schwingle@student.nmt.edu)
earned his B.S. and M.S. degrees from the New Mexico
Institute of Mining and Technology (NMT), Socorro, in
2014 and 2019, respectively, and is also a volunteer firefighter. Prior to his time at NMT, he worked in the San
Francisco Bay Area as a broadcast engineer in both radio
and television. After earning his B.S. degree, he worked as
a project engineer for TRAX International. His research
interests are in the areas of wireless communication,
radio-frequency-aided sensors, fire science, timing, and
high-power electromagnetics.
Aly El-Osery (aly.elosery@nmt.edu) earned his B.S.,
M.S., and Ph.D. degrees in electrical engineering from the
University of New Mexico in 1997, 1998, and 2002, respectively. He is a professor and the chair of the Electrical Engineering Department, and most recently, dean of all graduate
studies at the New Mexico Institute of Mining and Technology, Socorro. His research interests are in the areas of inertial

[11] G. Pipelidis, N. Tsiamitros, C. Gentner, D. B. Ahmed, and C. Prehofer, "A
novel lightweight particle filter for indoor localization," in Proc. Int. Conf. Indoor
Positioning and Indoor Navigation (IPIN), Sept. 2019, pp. 1-8. doi: 10.1109/
IPIN.2019.8911744.
[12] C. Zhang, N. Qin, Y. Xue, and L. Yang, "Received signal strength-based indoor
localization using hierarchical classification," Sensors, vol. 20, no. 4, p. 1067, 2020. doi:
10.3390/s20041067.
[13] R. Schwingle, "Mapping and modeling Wi-Fi as an aid for positional reference,"
M.S. thesis, New Mexico Inst. Mining and Technol., Socorro, NM, 2019.
[14] A. El-Osery, K. Wedeward, and S. Bruder. (2020). EE 565: Position, navigation and
timing [Online]. Available: http://ee.nmt.edu/~elosery/spring_2020/ee565/
[15] W. S. Flenniken, "Modeling inertial measurement units and analyzing the
effect of their errors in navigation applications," Ph.D. thesis, Auburn Univ.,
Auburn, AL, 2005.
[16] H. Hou, "Modeling inertial sensors errors using Allan variance," Ph.D. thesis, Univ.
Calgary, Calgary, Canada, 2004.
[17] J. A. Farrell and M. Barth, The Global Positioning System & Inertial Navigation.
New York: McGraw-Hill, 1999.

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