Aerospace and Electronic Systems Magazine October 2017 - 34

Knowledge-Aided Processing for MER
information that would support the required modeling and testing
of this approach was not available.

RESULTS FROM THE BLIND DATA TEST

Figure 11.

Ray-tracing analysis of Area 1. (a) Portions of drivable regions that
are predicted to support LOS are shown in green. (b) Optical view. (c)
Predicted number of multipath returns in drivable regions indicated by
color bar.

34

The culmination of the MER Phase II effort was the blind data
test, in which the participants were provided a subset of the Phase
I radar data that they had not previously examined. In this test, the
number of moving instrumented vehicles and their locations were
not provided. The performers were challenged to demonstrate that
algorithms developed in the Phase II effort were successful at detecting the correct number of vehicles and extracting the vehicle
trajectories from the measured data collected in an urban environment. It was understood that the data were challenging due to the
nature of urban multipath and the artifacts discussed earlier. Consistent with these difficulties, the performers were given 30 days to
process the blind data set. During this period, the MER processing
architecture and artifact mitigation approaches were significantly
refined. Fully automated operation, although desirable for a fielded system, was not required in this test. As discussed below, the
excellent performance obtained during the test suggests that the
proposed approach is robust and motivates additional future work.
The demonstration of a fully automated, real-time architecture, as
well as the assessment of tracking performance versus vehicle density, is recommended.
To illustrate the sparse regions where LOS was available in the
urban areas selected for the blind data test, the ground positions that
are predicted (via ray tracing) to support LOS for Test Point B (from
Area 1) are color-coded green in Figure 11a. In this image, nearby
buildings are indicated by the black rectangles, as confirmed in the
optical image of the same area appearing in Figure 11b. The limited
LOS is particularly troublesome in the roads running horizontally at
north-south values of approximately 150, 210, and 270 m.
To illustrate the benefit of multipath, the total number of predicted multipath returns (counting only single bounce and double
bounce) as a function of ground position is shown in Figure 11c. To
estimate this number, the reflections from each building surface are
considered. Due to the large number of nearby buildings, more than
one multipath return is predicted at most potential vehicle positions.
It is seen that multipath fills in the substantial gaps in LOS coverage. However, a standard (i.e., non-MER) tracker will not correctly
interpret the numerous multipath returns due to their multiplicity
and significant offsets in range and Doppler from the LOS return.
The results obtained with MER processing for Test Point A are
shown in Figure 12. The predicted and true vehicle trajectories are
illustrated in Figure 12a. The green rectangles in this figure are
the outlines of nearby buildings. Only two moving vehicles were
employed in this test point: the solid blue line indicates the true
trajectory for Vehicle 1, and the solid red line indicates the true trajectory for Vehicle 2. The dashed lines indicate the corresponding
estimated trajectories. It is evident that the correct number of moving vehicles (two) was identified, and movies of the truth versus
predictions verify that track swapping did not occur. The accuracy
of the estimated trajectories is outstanding. The root mean square
(RMS) position errors for the estimated trajectories are shown in
Figure 12b, with the error for Vehicle 1 shown in blue and the error

IEEE A&E SYSTEMS MAGAZINE

OCTOBER 2017



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