Aerospace and Electronic Systems Magazine October 2017 - 36

Knowledge-Aided Processing for MER
For 97% of the collection time, the correct road segments were
identified. The only two errors occurred for short periods at the two
start-up points discussed above. Both errors were eliminated when
the vehicles traversed through the first intersection. In both test
points, the correct number of vehicles was identified, and no instances of track swapping occurred. For 91% of the collection time, the
estimated trajectories were within 10 m of truth. The overall RMS position error (including the two start-up errors noted above) was 8.1 m.

CONCLUSIONS

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REFERENCES

[2]

[4]

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Persistent surveillance of moving vehicles in urban areas is a
highly desired capability that does not exist. In DARPA's MER
program, algorithms were developed and shown to enable GMTI
surveillance over 1,000 km2 with just three radar platforms. In this
article, the initial MER feasibility efforts, MER Phase I data collection, and the results obtained during the MER Phase II effort
are summarized. In particular, it is demonstrated that the MER
processing approach developed during Phase II yielded exquisite
GMTI performance during the blind data test. These results show
that urban multipath is sufficiently predictable to be exploited and
motivates additional work in this area. Contrary to the view that
multipath is a problem, it is evident that the proper exploitation
of multipath enables localization and tracking superior to that obtained in the absence of multipath.

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IEEE A&E SYSTEMS MAGAZINE

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OCTOBER 2017

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