Applying Sensor Uncertainty Mitigation Schemes to Detect-and-Avoid Systems

Impact of sensor noise on the performance of Detect-And-Avoid (DAA) systems can be reduced by implementing various mitigation schemes. This paper evaluates the Sensor Uncertainty Mitigation (SUM) method, implemented in the Detect and Avoid Alerting Logic for Unmanned Systems (DAIDALUS) algorithm, a reference implementation in the DAA minimum operational performance standards. DAIDALUS SUM performance is evaluated using a few safety and operational suitability metrics and compared with more traditional approaches using static safety buffers. A large number of encounters representative of low-speed unmanned aircraft against non-cooperative manned aircraft are simulated and evaluated. An air-to-air radar model produces representative sensor noise for the DAA system. Results show that increasing the tunable parameters for horizontal and vertical uncertainty in DAIDALUS SUM improves the safety metric at the cost of increasing the number of system alerts leading to increased workload. A range of SUM parameters is recommended as suitable values for the type of operations considered for this work. General trends and optimal SUM configurations were found to be nearly the same for two large and very different encounter data sets.