Israeli study finds ground forces thrive on multi-drone data

Special to WorldTribune.com

TEL AVIV — Israel has concluded that infantry operations were
enhanced by feeds from multiple unmanned platforms.

Israel’s Ben-Gurion University has overseen a study financed by the U.S.
Army on the effectiveness of UAV data on urban and other military missions.
Researchers said a key question was whether and at what point does UAV data
lead to information overload for soldiers.

Israel's Guardium unmanned ground vehicle.
Israel’s Guardium unmanned ground vehicle.

“Lessons learned worldwide from the war on terror led to the development of new operation methods that are heavily aided by remote-controlled machines,” Tal Oron-Gilad, a senior lecturer, said.

The research, supported by the U.S. Army Research Laboratory, concluded that the soldiers performed better when they received multiple data sources. Organizers said a feed from an unmanned ground vehicle augmented reconnaissance from the unmanned aircraft.

“False alarm reports dropped and the research subjects felt their
performance had improved using the dual feeds,” Ben-Gurion University said on Feb. 5. “It didn’t impose an undue workload on the soldiers either. On the deficit side, the UGV feed was less useful in dense urban areas as opposed to more open areas probably because of the limited field of view it
provided in back alleys.”

Oron-Gilad led a four-member team in comparing the effectiveness of a
single UAV feed as opposed to multiple sources. The team monitored 30
university engineering students, all of whom served in Israel Army combat units, for
information overload.

Organizers said the simulation tested two scenarios. One scenario
stipulated a UAV reconnaissance flight without any particularly target. The
second scenario called for a UAV mission over an urban area supported by a
UGV.

The latest study disputed previous research that touted the
effectiveness of UAVs over UGVs. Oron-Gilad said his latest study matched
results from research he conducted in 2011.

“The team conducted an attention allocation — based on eye-tracking —
analysis, identification accuracy and false alarm analysis and a subjective
workload analysis, and a subjective task-related questionnaire to arrive at
their conclusions,” the university said.

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