Hazard tracking with integrity for surveillance applications

Abstract

This paper presents a new aircraft traffic tracking algorithm for surveillance applications that integrates sensory information from multiple avionics sensors in an efficient manner and assesses the sensor consistency for integrity purposes. Measurements from various avionics sensors and ownship information are used to form a relative baseline vector. Once these relative baseline vectors are formed, they are integrated and estimated using an Interacting Multiple Model (IMM) filter, which has multiple Kalman filters with different dynamics models running in parallel and interacting with each other through an underlying Markov chain. Once the estimated baseline vector is obtained from the IMM filter, a series of integrity checks are performed. These include 1) the evaluation of the normalized residuals, and 2) the evaluation of the Autonomous Integrity Monitored Extrapolation (AIME) test statistic. Four sets of flight data are used to demonstrate the functionality of the algorithm, which include one set of simulated flight data based on several possible aircraft trajectories, and three sets of simulated data and real flight data from the RTCA DO-317 document.