  Detect-And-Avoid (DAA) algorithms for unmanned air vehicles have industry
standards called Minimum Operational Performance Standards (MOPS), establishing
criteria to check whether they can ensure safe separation for all plausible
operational conditions. However, these MOPS ensure performance for the
avoidance maneuvers, which are open-loop, but not for the maneuvers that bring
the air vehicles back to their intended courses, closing the control loop of
the missions. In this paper, we analyze the closed-loop performance of existing
DAA algorithms, by experimenting large numbers of traffic configurations with 4
aircraft in a delimited airspace.
  We measure and analyze their rates of loss of separation and timeout events,
the latter happening when a chain of maneuvers exceeds the maximum supply of
energy in a vehicle. We also analyze the efficiency of the closed-loop logic,
expressed as the excess fuel rate, and study the relationship between safety
and efficiency in these scenarios. Our results show that the inefficiency
caused by DAA algorithms is very significant in dense airspaces and that it
offers ample space for improvement.
  Performing the simulations of the closed-loop mission management logic can be
highly time consuming. Despite there being Neural Network approximations of DAA
logic that alleviate the computational load, none of those that we found
available can properly handle cases with more than one intruder in the ownship
surveillance range. Therefore, in an attempt to overcome the high computational
cost of analyzing the closed-loop performance of DAA algorithms, we study the
correlation between inefficiency and safety of closed-loop scenarios, and some
indicators from the corresponding open-loop scenarios, such as angle of
deviation and number of deviations per flight time.
