Data Fusion for Anomaly and Degradation Detection

Sources of sensor errornbsp;leave a UTMnbsp;(UAS Traffic Management)nbsp;system vulnerable.nbsp;Thenbsp;impact of anomalous sensor behavior can be devastating to a UTM system. Degraded sensor accuracy can lead to broken tracksnbsp;ornbsp;split tracks within a UTM system.nbsp;Degraded sensor sensitivity may result in a Mid-Air Collision with an aircraft not detected bynbsp;UTMnbsp;sensors.nbsp;It may also result innbsp;more Loss of Well-Clearnbsp;violations and more frequent Near Mid-Air Collisions.nbsp;nbsp;This proposal recommends research and development in the area of monitoring a diverse set of UTM sensor outputs for detection and identification of anomalous sensor behavior as a technique to enable In-Time System-wide Safety Assurance (ISSA). In addition, research will be performed to develop an in-time methodology for translating out-of-spec sensor data to overall UTM system safety.nbsp;nbsp;CAL Analytics has teamed with Hidden Level to accomplish the following Phase 1 researchnbsp;tasks:nbsp;Perform market study of UTM Sensorsnbsp;Perform study to determine minimum required sensor data elements to enable ISSAnbsp;Develop a framework for comparingnbsp;sensor outputsnbsp;Perform study to assess separability of anomalous sensornbsp;behaviornbsp;Develop requirementsnbsp;andnbsp;performance metrics for guiding Phase II algorithm developmentnbsp;Perform study exploringnbsp;methods to assess system safety given degraded sensor performancenbsp;Document results of studies in final report with recommendations for Phase II.nbsp;The results of this research will form the basis of a UTMnbsp;ISSA technique that will:nbsp;Enable detectionnbsp;and reportingnbsp;ofnbsp;anomalous sensor behavior throughnbsp;data miningnbsp;nbsp;Identify impacts to UTM system safety usingnbsp;anomalous sensor behavior to assess the impact to overall UTM system safety.nbsp;