A Unified Airspace Risk Management Framework for UAS Operations †
- it should be conservative (since it is always preferable to overestimate risk for a Safety of Life application);
- it should explicitly budget contributions from different CNS elements to the overall risk;
- it should be capable of seamlessly and efficiently handling free routes and arbitrary trajectories.
1.1. Scope and Structure of the Article
1.2. Prior Work in Collision Risk Modelling
2. Models and Methods
2.1. Fundamental Definitions
2.2. Unified Airspace Risk Management Framework
2.3. Collision Risk Mitigation and Control Measures
2.4. CNS Performance-Based Collision Risk Modelling
2.5. Model Formulation
- position of the host aircraft;
- position of the intruder aircraft.
2.6. Risk Evaluation
2.7. CNS Risk Volume
- Failure of navigation systems: loss of accuracy beyond a specified limit without timely detection. This constitutes hazardously misleading information preventing timely recovery action from pilots/controllers;
- Failure of surveillance systems: loss in accuracy of aircraft localisation and non-timely relay of surveillance information to downstream sub-systems for recovery actions;
- Failure of Communication: loss/degradation of a link to the point where necessary recovery actions cannot be implemented in a timely manner.
- Relative dynamics: an inflation is introduced, which is commensurate with the known or assumed closure rate of the aircraft;
- Wake turbulence: a buffer region is added, which guarantees sufficient separation from the hazardous region in the wake of the preceding aircraft.
2.7.1. Risk Volume Decomposition
- Navigation: The navigation component of the volume is conveniently represented by a Gaussian ellipsoid. For most avionics systems, navigation sensor outputs are typically fused in a state estimator such as a Kalman filter, which outputs an optimal estimate of aircraft position as well as position uncertainty in the form of a state covariance matrix. Alternately, a more standardised and conservative representation of position uncertainty, the protection level, can be utilised. A protection level is essentially an upper bound on the position error for a given navigation system, which also takes into account the employed fault detection and isolation algorithm. The methodology of inflating the protection level to bound errors to a target probability is also well defined.
- Surveillance: The error in localising the intruder aircraft arises from two sources. The first of these is the error arising from the sensor used to measure the states of the intruder. The second arises from the time difference between observing the intruder and utilising the observation to assess the likelihood of a threat. Depending on the type of employed system, there can be a significant component of latency in the surveillance system. If the intruder aircraft are observed through a cooperative surveillance system such as ADS-B, then the error in localising the aircraft error will essentially be dependent on the error of the onboard GNSS system [43,44]. If a non-cooperative system such as radar is used to observe the intruder, then the localisation error will depend on the deployed radar characteristics and the intruder parameters. Before the estimated position of the intruder is used to assess the likelihood of a collision, it is compensated for latency in the system. Since latency cannot be directly observed in real-time, latency compensation is based on a priori modelled or measured system characteristics. Therefore, a residual error component will remain.
- Communication: If the assessment of collision risk is performed on the ground, either through an autonomous system or through visual observation by the remote PIC, then the execution of an avoidance manoeuvre is performed through the communication link between the GCS and the unmanned platform. The communication component of the risk volume is determined to provide a sufficient buffer that protects against a loss of separation due to a failure of the communication system.
2.7.2. Navigation and Surveillance Modelling
Primary Surveillance Radar Errors
- A Signal to Noise (S/N) dependent random measurement error
- Random measurement error with fixed standard deviation due to noise sources in the radar receiver’s final stages. These errors are usually small and correspond to the S/N dependent errors that are produced when S/N is high
- A bias error that occurs due to radar calibration and measurement
- Errors due to conditions of radar propagation and the uncertainties in correcting these errors
- Interference errors that occur due to various reasons such as radar clutter
- is the random error with a fixed standard deviation produced when S/N is high.
- is the range bias error as a result of calibration and measurement.
- Receiver Dependent Errors such as Clock Error, Noise and Resolution;
- Ephemeris Prediction Errors;
- Satellite Dependent Errors that include Clock Offset and Group Delays;
- Propagation Errors such as Ionospheric Delay, Tropospheric Delay and Multipath;
- User Dynamics Error.
2.7.3. Volume Coordinate Transformation
3. Application Case Studies
3.1. Terminal Control Area
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Aircraft||Fixed wing UAVs|
Wingspan: 15 m
Monostatic scanning PSR|
= 10 m
|Trajectories||Level flight (altitude: 110 m AGL)|
Constant speed (25 m/s) and heading
|Aircraft||Fixed wing UAVs |
Wingspan: 15 m
|Navigation||GNSS receiver: GPS |
RNP 0.1; Accuracy: 185.2 m
RNP 0.04; Accuracy: 75 m
|Trajectories||Level flight (altitude: 110 m/s AGL)|
Constant speed (25 m/s, 40 m/s) and heading
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Bijjahalli, S.; Gardi, A.; Pongsakornsathien, N.; Sabatini, R.; Kistan, T. A Unified Airspace Risk Management Framework for UAS Operations. Drones 2022, 6, 184. https://doi.org/10.3390/drones6070184
Bijjahalli S, Gardi A, Pongsakornsathien N, Sabatini R, Kistan T. A Unified Airspace Risk Management Framework for UAS Operations. Drones. 2022; 6(7):184. https://doi.org/10.3390/drones6070184Chicago/Turabian Style
Bijjahalli, Suraj, Alessandro Gardi, Nichakorn Pongsakornsathien, Roberto Sabatini, and Trevor Kistan. 2022. "A Unified Airspace Risk Management Framework for UAS Operations" Drones 6, no. 7: 184. https://doi.org/10.3390/drones6070184