Aerospace and Electronic Systems Magazine July 2018 - 52

LTE CommSense for Object Detection in Indoor Environment
RF daughter card [15] is used, which operates in the range of 2.3-2.9
GHz. The VERT2450 antenna [16], which operates in the LTE passband frequency range, is used with the daughter card.

The received signal is compared
to the expected signal to

1
 

estimate the change in the
environment.
Description of Captured Data
According to the LTE frame structure, one frame has a 10-ms duration and consists of 10 individual subframes, each with a duration
of 1 ms. These data will be in complex (in-phase and quadrature
[I-Q]) format. Each subframe is mapped with a cell-specific reference signal and primary and secondary synchronization signals.
Therefore, after evaluating channel estimates from an LTE frame,
we can shift by one frame to get a new set of readings from the real-time LTE DL recording and evaluate another channel estimate.
Because one recording of LTE DL data is captured for 15 s, we can
evaluate up to 1,500 channel estimates from 1,500 readings from a
single recording using the preceding procedure. If LTE DL data are
captured for more than 15 s, a higher number of channel estimates
can be computed accordingly.

ANALYSIS OF CAPTURED DATA
After the LTE DL data are captured using the modeled LTE receiver in the USRP SDR platform [13] and channel estimates are
evaluated for specific experimental scenarios, they are processed
to visualize and distinguish the differences and infer environment
changes because of possible target presence or absence. Two types
of analysis were performed, viz. PCA [17] for dimensionality reduction followed by cluster analysis and FrFT to analyze TFRs of
the channel estimates.
To perform cluster analysis, the higher-dimension data were
first reduced to the lower two or three dimensions and plotted in
scatterplot. PCA was used to perform dimensionality reduction.
The superiority of PCA for many uses is established in [17]. PCA
involves a procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables
called principal components.
To perform PCA, first the covariance matrix of the dataset was
calculated as follows:
T
1
F =   ∗ ( xk − v )( xk − v ) ,
N
 

(1)

where v = xk is the mean of the data and N is equal to the number
of elements in the dataset. PCA is based on the projection of correlated high-dimensional data onto a hyperplane. This mapping
52

uses only the first few q nonzero eigenvalues and the corresponding eigenvectors of the covariance matrix Fi = U i Λ iU iT , where
Λ i is a diagonal matrix whose entries are the eigenvalues λ of
i,j
Fi in decreasing order and the matrix Ui includes the eigenvectors corresponding to the eigenvalues in its columns. The vector
1

yi,k W
 xk  W T  xk  is a q-dimensional reduced representation
of the observed vector xk, where the Wi weight matrix contains the
q principal orthonormal axes in its column Wi = U i,q Λ i 2 .
As an alternative data visualization method to readily detect
environment changes from their processed plots, FrFT processing
was also explored. Information about this topic can be found in
[18]. FrFT (also called rotational Fourier transform or angular Fourier transform) can be considered a rotation by angle α (not a multiple of π/2) in the time-frequency plane or a decomposition of the
signal in terms of chirps. The FrFT was computed using the angle
of rotation in the time-frequency plane as the fractional power of
the ordinary Fourier transform. Letting x(u) be an arbitrary signal,
its ath-order FrFT is defined as [18]
X a ( u ) =  ka ( u , u′ ) x ( u′ ) du′,

(2)

where a is the fractional transformation order (corresponding to a
rotation angle α = aπ/2, with a ∈ℜ). a is a unitless scalar quantity;
its value ranges from 0 to 4, so the value of α can range from 0 to 2
* π radian. ka(u,u′) is the FrFT kernel and is defined as

((

)

 A ∗ exp  jπ u 2 + u′2 ∗ cot α − 2uu′ ∗ csc α
( )
( )

 0

α ≠ n ∗π , n ∈ I
k a ( u , u′ ) = 

δ ( u − u ′ ) ;α = n ∗ 2 ∗ π , n ∈ I

δ ( u + u ′ ) ;α + π = n ∗ 2 ∗ π , n ∈ I


)
;

α

where A0 =

e2

j ∗ sin (α )

.

The applicability of FrFT for visual feature extraction using
TFRs is established in [19] in the case of synthetic aperture radar
(SAR) automatic target recognition (ATR). For LTE CommSense,
the channel estimates are obtained in the time-frequency grid.
These channel estimates were analyzed using their corresponding
TFRs to find consistent visual differences for data visualization
and inference.
Lastly, we show the performance change of CommSense with
respect to distance of the object from the CommSense system.

Cluster Analysis
The objective of this phase was to establish the feasibility of detecting human targets in an indoor environment using cluster analysis. Though the scope of this work is to detect a human target in
an indoor environment, the capability of detecting other objects
can be explored. The dimensions of the captured channel estimates
were reduced using PCA and plotted in two and three dimensions
for visual analysis. We investigated whether we can differentiate

IEEE A&E SYSTEMS MAGAZINE

JULY 2018



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