Aerospace and Electronic Systems Magazine July 2018 - 54

LTE CommSense for Object Detection in Indoor Environment

Figure 8.

Sorted eigenvalues and their cumulative energy contribution for indoor human-target detection.

cipal components are sufficient for this
case to faithfully represent the variability of the input data, the sorted eigenvalues and their cumulative energy contribution are plotted in Figure 8. It can
be observed that the values of the eigenvalues gradually decrease and that after
the 35th eigenvalue, the magnitudes of
other eigenvalues are negligible. Consequently, the cumulative energy contribution of the eigenvalues saturates after
the 35th eigenvalue. This information
is required to reduce the dimension of
the data while preserving the variability.
Before passing the data to the pattern
classifier, dimension reduction following the preceding procedure may be performed for efficient computation. The
largest two principal components are
plotted in a 2D scatterplot and shown in
Figure 9a. The three-dimensional (3D)
scatterplot of the largest three principal components is shown in Figure
9b. In the scatterplots in Figure 9, the
red cluster points correspond to indoor
backgrounds and black cluster points
represents indoor backgrounds with the
human target present.
It can be observed that clusters with
and without a human target are distinct.
Hence, it can be concluded that the human target can be detected in indoor environments using the CommSense system.

Visualization and Inference

Figure 9.

Indoor human-target detection using cluster analysis: (a) 2D scatterplot of the largest two principal
components, (b) 3D scatterplot of the largest three principal components.

54

IEEE A&E SYSTEMS MAGAZINE

FrFT was performed on the channel estimates to investigate whether distinguishing features can be observed directly
from the plots of the processed estimates.
The channel estimates with and without a
human target in an indoor environment in
the FrFT domain are shown in Figure 10.
The top row of the figure shows multiple
channel estimates for the with-human
case, and the bottom row shows channel
estimates for the without-human case.
We observed the individual TFRs and
selected the corresponding TFR, which
shows the most consistent distinctive features. Figure 11 shows the TFRs for different rotation angles for both the cases,
i.e., with and without human. Although
in all images, the difference between the
first- and the second-row images for a
particular angle is visible, it is prominent
JULY 2018



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