Aerospace and Electronic Systems Magazine November 2016 Tutorial X - 107


Fasano et al.
Assuming a certain collision detection threshold dCPA,thresh the adaptive maximum threshold for the LOS rate is derived as
		

(51)

This represents an ideal value since it does not take into account
the unavoidable sensing uncertainty σlosrate. The latter can be included in the calculation as follows:
	(52)
Considering the worst case assumptions used in the derivation, it is
evident that the criterion presented in (52) is conservative for any
range larger than rmin, and for intruders which are slower than the
fastest ones. In particular, conservativeness depends on how the probability of declaration changes as a function of range. Conservativeness will in general imply a worse trade-off between missed and false
conflict detection, which expresses quantitatively the different levels
of situation awareness achievable by tracking architectures which enable or do not enable reliable range and range rate estimates [47].
A general concept expressed in (52) is the need to consider
margins within the conflict detection process, in order to take into
account the unavoidable problem uncertainties. Indeed, (47) also
gives some insight for the case of a noncooperative sensing system
providing 3D information, showing that a LOS rate error generates an error on dCPA which depends quadratically on range and
inversely on range rate. (See Fig. 21.) This does not apply to the
case of ADS-B-based architectures, where it is more convenient
to analyze dCPA uncertainty by expressing (41) in terms of linear
components of relative position and velocity.
In general, tracking uncertainties should be taken into account
in order to have a statistically consistent approach for conflict detection.
Finally, it is worth recalling that in the case of integration of
UAS into controlled airspace, minimum distances for conflict detection have been defined (see Fig. 7), and margins can be assigned
based on estimated uncertainties. Instead, regarding SAA for small
UAS in future scenarios such as UAS traffic management (UTM)
[48], the process of defining minimum separation standards is still
underway.

V.  AVOIDING: COLLISION AVOIDANCE METHODS
After sensing the environment in which the UA is flying, and detecting and tracking targets in that environment that may pose a
collision risk, the UAS must determine an effective avoidance maneuver. The selected maneuver must be appropriate for the timing
to collision, and must take into account the vehicle capabilities and
the constraints of the environment.
Timing to collision is critical to determining what type of maneuver is possible, and how much time is available to determine
and implement the maneuver. Referring back to Fig. 6, the time
frames defined by the FAA UAS workshop include the following.
1.	 Separation assurance - there is enough time for the pilot to
determine a collision avoidance maneuver, and coordinate it
NOVEMBER 2016, Part II of II	

Fig. 21.

Quadratic dependence of error on estimated dCPA on range, for azimuth
rate error of 0.3 °/s [14].

with ATC via voice communication, before directing the UA to
make the maneuver.
2.	 Self-separation - there is enough time for the pilot to deliberately determine a collision avoidance maneuver, and direct the
UA to make the maneuver. There may not be time to coordinate with ATC beforehand, so the pilot (or the automation) will
take this self-separation action first, then coordinate with ATC
afterwards.
3.	 Collision avoidance - there is only a short period of time to
determine a collision avoidance maneuver, and direct the UA
to make the maneuver. If the pilot is in the loop, the action to
direct the UA must be taken very quickly. Alternatively, automated capabilities onboard the UA can also be programmed to
make the last moment maneuvers, without the pilot in the loop.
In each case, the human pilot can evaluate the target information and decide on the avoidance maneuver. This decision can be
supported by a display of UAS and target track information, along
with other information in the environment that can limit the avoidance alternatives. This information can include projected collision
avoidance alternatives, as generated by automation. In order for
the collision avoidance decision to be made without the pilot in
the loop, the collision avoidance alternatives and the decision on
which one to take must be generated by automation. Facets of the
collision avoidance algorithms for each of these alternatives are
discussed in the following sections.

A.  REMOTELY OPERATED COLLISION AVOIDANCE
In order for the pilot to evaluate a target and decide how to direct
the UA to avoid a collision, the pilot must be presented with information about the target and the UA, and the environment in which
they are operating. For large UAs, the GCS can have a display of
these factors, plus tools to probe alternative solutions. For small
UAs, the pilot's controller can have similar tools, though the pro-

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