Aerospace and Electronic Systems Magazine August 2017 - 73

Broumandan, Siddakatte, and Lachapelle

Figure 10.

IF variance and SPCA metrics outputs.

antenna was placed on the roof of a vehicle moving at speeds of
up to 50 km/h. A front-end using an 8-bit ADC, disabled AGC,
and 10 MHz bandwidth was used to collect digital samples. The
data collection environment was surrounded by up to 30 story
concrete and glass buildings as shown in Figure 9. Each data set
consists of 40 s of raw IF samples. The IF samples were passed
to predespreading interference detection metrics, namely time/
frequency power analysis and SPCA. Each metric analyzed 1 s of
IF samples to output decision statistics. Hence, for each data set,
there were 40 detection metric outputs.
Figure 10 shows IF sample variance and SPCA metric outputs as a function of time for various data sets. There are a few
data sets with variance metric outputs above the predefined
threshold indicating there are high power signals in the bandwidth. The signal variance has its highest values for data set 10.
Existence of excessive power in the bandwidth can be due to
the presence of spoofing or jamming signals. The SPCA metrics
outputs shown in Figure 10 do not exhibit the same pattern as
that of the variance output analysis. In fact, the SPCA metric
outputs are high in open sky conditions (data sets 1 and 15) and
low during the data collection in dense multipath environments.
Comparing the results of variance and SPCA outputs with those
of Figure 3 and Figure 4, one concludes that the increase in the
IF sample variance in dense urban environments is due to CW
jamming signals of unknown sources which affected the samples
during the test.
The power spectral density of the received signals for data
set 1 (clean data) and data set 10 (affected by jamming) were
AUGUST 2017

Figure 11.

Power spectral density of data set 1 and 10.

also analyzed and the results are shown in Figure 11, which also
shows power spectral density plots of data set 10 for 40 epochs.
Comparing the results of data set 1 and data set 10 reveals that the
signals of data set 10 are affected by interfering signals spread all
over the signal bandwidth. The existence of these jamming signals elevated the IF samples level and hence at various epochs the
variance detection metric values exceeded the detection threshold. This is not due to multipath but jamming signals as observed
in Figure 11. The existence of CW jamming signals did not affect
the SPCA metric outputs since SPCA is sensitive to a structural
signal type such as spoofing signals. As shown, the SPCA metric values in all of the 15 data sets were below the threshold.
Considering the collected data sets, predespreading metrics are
not affected by multipath distortion. These experimental results
justify the use of predespreading metrics to discriminate between
spoofing and multipath signals. These results are in agreement
with the data analyses results provided in the previous section.
In the next step, postspreading detection metrics are analyzed for
different data sets.
Figure 12 shows the C/N0 values and SQM metrics for PRN9 in
data set 3 and 13. In data set 3 the SQM metric values exceed the
detection threshold at t = 28 s. At this epoch the C/N0 values drop
by about 10 dB. There are several fluctuations in the C/N0 of data

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Table of Contents for the Digital Edition of Aerospace and Electronic Systems Magazine August 2017