With an expected increase in air traffic and an expected improvement in surveillance equipment as part of the next generation air traffic management system (NextGen), there is a potential need for an advanced aircraft tracking system. This thesis introduces a new aircraft traffic-tracking algorithm for surveillance applications that integrates sensory information from several avionics sensors such as Automatic Dependent Surveillance – Broadcast (ADS-B), Automatic Dependent Surveillance – Rebroadcast (ADS-R), Traffic Information Service – Broadcast (TIS-B), The Traffic Collision and Avoidance System (TCAS) II in an efficient manner and assesses the sensor consistency for integrity purposes.
Position and, in some configurations, velocity reports from various surveillance sensors and ownship information are used to form relative baseline vectors. These relative baseline vectors are input to an Interacting Multiple Model (IMM) filter, which consist of multiple Kalman filters with different dynamics models running in parallel and interacting with each other through an underlying Markov chain. The estimated baseline vectors are obtained from the IMM filter and a series of integrity checks are performed. For the filter whose model is most suitable to the actual separation vector dynamics, these integrity checks include evaluation of 1) normalized residuals, 2) the Autonomous Integrity Monitored Extrapolation (AIME) test statistic, 3) outlier rejection techniques. Normalized residuals detect sudden jumps and miss-modeling errors; AIME test statistic detects slowly growing errors. Outlier rejection technique is similar to 1 and detects sudden jumps in sensor measurements, which do not follow the tendency of the estimated aircraft trajectory. The integration of ADS-B position and velocity reports with TCAS range measurements is also an excellent monitor for the detection of off-nominal position and velocity reports. Estimated baseline vectors that pass these integrity checks are advanced to the next stage, where ellipsoid uncertainty regions around each relative position vector are computed.
The predicted aircraft state and track for multiple aircraft can be used for predicting conflicts for various time horizons, generating alerts to the flightcrew, advising maneuvers to the flightcrew, or for situational awareness. For conflict detection, traffic tracking algorithm will be propagated ahead in time so that relative baseline vectors are predicted for various time horizons. Depending on the proximity of the relative position vector to the origin, alerts can be generated to the flightcrew.
To achieve smoother trajectory and velocity estimates during phases of flight with varying dynamics conditions, running multiple Kalman filters with models tailored to these dynamics conditions has been shown to work using a CV, CA, and CT models with two sets of NASA VSST IAN simulated data and two sets of simulated data from RTCA DO-317A.
","abstract_html":"<p>With an expected increase in air traffic and an expected improvement in surveillance equipment as part of the next generation air traffic management system (NextGen), there is a potential need for an advanced aircraft tracking system. This thesis introduces a new aircraft traffic-tracking algorithm for surveillance applications that integrates sensory information from several avionics sensors such as Automatic Dependent Surveillance – Broadcast (ADS-B), Automatic Dependent Surveillance – Rebroadcast (ADS-R), Traffic Information Service – Broadcast (TIS-B), The Traffic Collision and Avoidance System (TCAS) II in an efficient manner and assesses the sensor consistency for integrity purposes.</p><p>Position and, in some configurations, velocity reports from various surveillance sensors and ownship information are used to form relative baseline vectors. These relative baseline vectors are input to an Interacting Multiple Model (IMM) filter, which consist of multiple Kalman filters with different dynamics models running in parallel and interacting with each other through an underlying Markov chain. The estimated baseline vectors are obtained from the IMM filter and a series of integrity checks are performed. For the filter whose model is most suitable to the actual separation vector dynamics, these integrity checks include evaluation of 1) normalized residuals, 2) the Autonomous Integrity Monitored Extrapolation (AIME) test statistic, 3) outlier rejection techniques. Normalized residuals detect sudden jumps and miss-modeling errors; AIME test statistic detects slowly growing errors. Outlier rejection technique is similar to 1 and detects sudden jumps in sensor measurements, which do not follow the tendency of the estimated aircraft trajectory. The integration of ADS-B position and velocity reports with TCAS range measurements is also an excellent monitor for the detection of off-nominal position and velocity reports. Estimated baseline vectors that pass these integrity checks are advanced to the next stage, where ellipsoid uncertainty regions around each relative position vector are computed.</p><p>The predicted aircraft state and track for multiple aircraft can be used for predicting conflicts for various time horizons, generating alerts to the flightcrew, advising maneuvers to the flightcrew, or for situational awareness. For conflict detection, traffic tracking algorithm will be propagated ahead in time so that relative baseline vectors are predicted for various time horizons. Depending on the proximity of the relative position vector to the origin, alerts can be generated to the flightcrew.</p><p>To achieve smoother trajectory and velocity estimates during phases of flight with varying dynamics conditions, running multiple Kalman filters with models tailored to these dynamics conditions has been shown to work using a CV, CA, and CT models with two sets of NASA VSST IAN simulated data and two sets of simulated data from RTCA DO-317A.</p>","abstract_has_math":false,"creators":["Bezawada, Rajesh"],"institution":"Ohio University","degree_name":"Master of Science (MS)","degree_level":"masters","degree_discipline":"Electrical Engineering (Engineering and Technology)","degree_department":null,"school":null,"contributors":["Uijt de Haag, Maarten"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-24T03:35:52Z","subjects":["Aerospace Engineering","Electrical Engineering","NextGen","Onboard","Aircraft","Traffic","Tracking","Algorithm","Support","Conflict","Detection","Resolution","Multi","sensor","Data","Integration","Integrity","Monitoring","VSST","IIFD","Hazard Monitor","IMM filter","Kalman filter","ADSB","TCAS"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1354660894","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Uijt de Haag, Maarten"]},{"key":"dc:creator","label":"Author","values":["Bezawada, Rajesh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012"]},{"key":"dc:publisher","label":"Institution","values":["Ohio University / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering (Engineering and Technology)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Ohio University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Aerospace Engineering","Electrical Engineering","NextGen","Onboard","Aircraft","Traffic","Tracking","Algorithm","Support","Conflict","Detection","Resolution","Multi","sensor","Data","Integration","Integrity","Monitoring","VSST","IIFD","Hazard Monitor","IMM filter","Kalman filter","ADSB","TCAS"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. 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This thesis introduces a new aircraft traffic-tracking algorithm for surveillance applications that integrates sensory information from several avionics sensors such as Automatic Dependent Surveillance – Broadcast (ADS-B), Automatic Dependent Surveillance – Rebroadcast (ADS-R), Traffic Information Service – Broadcast (TIS-B), The Traffic Collision and Avoidance System (TCAS) II in an efficient manner and assesses the sensor consistency for integrity purposes.
Position and, in some configurations, velocity reports from various surveillance sensors and ownship information are used to form relative baseline vectors. These relative baseline vectors are input to an Interacting Multiple Model (IMM) filter, which consist of multiple Kalman filters with different dynamics models running in parallel and interacting with each other through an underlying Markov chain. The estimated baseline vectors are obtained from the IMM filter and a series of integrity checks are performed. For the filter whose model is most suitable to the actual separation vector dynamics, these integrity checks include evaluation of 1) normalized residuals, 2) the Autonomous Integrity Monitored Extrapolation (AIME) test statistic, 3) outlier rejection techniques. Normalized residuals detect sudden jumps and miss-modeling errors; AIME test statistic detects slowly growing errors. Outlier rejection technique is similar to 1 and detects sudden jumps in sensor measurements, which do not follow the tendency of the estimated aircraft trajectory. The integration of ADS-B position and velocity reports with TCAS range measurements is also an excellent monitor for the detection of off-nominal position and velocity reports. Estimated baseline vectors that pass these integrity checks are advanced to the next stage, where ellipsoid uncertainty regions around each relative position vector are computed.
The predicted aircraft state and track for multiple aircraft can be used for predicting conflicts for various time horizons, generating alerts to the flightcrew, advising maneuvers to the flightcrew, or for situational awareness. For conflict detection, traffic tracking algorithm will be propagated ahead in time so that relative baseline vectors are predicted for various time horizons. Depending on the proximity of the relative position vector to the origin, alerts can be generated to the flightcrew.
To achieve smoother trajectory and velocity estimates during phases of flight with varying dynamics conditions, running multiple Kalman filters with models tailored to these dynamics conditions has been shown to work using a CV, CA, and CT models with two sets of NASA VSST IAN simulated data and two sets of simulated data from RTCA DO-317A.
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Position and, in some configurations, velocity reports from various surveillance sensors and ownship information are used to form relative baseline vectors. These relative baseline vectors are input to an Interacting Multiple Model (IMM) filter, which consist of multiple Kalman filters with different dynamics models running in parallel and interacting with each other through an underlying Markov chain. The estimated baseline vectors are obtained from the IMM filter and a series of integrity checks are performed. For the filter whose model is most suitable to the actual separation vector dynamics, these integrity checks include evaluation of 1) normalized residuals, 2) the Autonomous Integrity Monitored Extrapolation (AIME) test statistic, 3) outlier rejection techniques. Normalized residuals detect sudden jumps and miss-modeling errors; AIME test statistic detects slowly growing errors. Outlier rejection technique is similar to 1 and detects sudden jumps in sensor measurements, which do not follow the tendency of the estimated aircraft trajectory. The integration of ADS-B position and velocity reports with TCAS range measurements is also an excellent monitor for the detection of off-nominal position and velocity reports. Estimated baseline vectors that pass these integrity checks are advanced to the next stage, where ellipsoid uncertainty regions around each relative position vector are computed.
The predicted aircraft state and track for multiple aircraft can be used for predicting conflicts for various time horizons, generating alerts to the flightcrew, advising maneuvers to the flightcrew, or for situational awareness. For conflict detection, traffic tracking algorithm will be propagated ahead in time so that relative baseline vectors are predicted for various time horizons. Depending on the proximity of the relative position vector to the origin, alerts can be generated to the flightcrew.
To achieve smoother trajectory and velocity estimates during phases of flight with varying dynamics conditions, running multiple Kalman filters with models tailored to these dynamics conditions has been shown to work using a CV, CA, and CT models with two sets of NASA VSST IAN simulated data and two sets of simulated data from RTCA DO-317A.
"],"dc:format":["application/pdf","p.140","18.52 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1354660894"],"dc:language":["English"],"dc:publisher":["Ohio University / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"dc:subject":["Aerospace Engineering","Electrical Engineering","NextGen","Onboard","Aircraft","Traffic","Tracking","Algorithm","Support","Conflict","Detection","Resolution","Multi","sensor","Data","Integration","Integrity","Monitoring","VSST","IIFD","Hazard Monitor","IMM filter","Kalman filter","ADSB","TCAS"],"dc:title":["Onboard Aircraft Traffic Tracking Algorithm to Support Conflict Detection and Resolution using Multi-sensor Data Integration and Integrity Monitoring"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Electrical Engineering (Engineering and Technology)"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science (MS)"],"thesis:institution_name":["Ohio University"]},"updated_at":"2026-07-24T03:35:52Z"}