Aircraft Conflict Detection and Avoidance through Interacting Multiple Model (IMM) Estimation

TL;DR

This paper applies the Interacting Multiple Model (IMM) estimation technique to improve aircraft conflict detection and avoidance by accurately tracking maneuvering aircraft amidst sensor noise.

Contribution

It introduces the implementation of IMM estimation for aircraft tracking and conflict avoidance, enhancing target identification accuracy in noisy conditions.

Findings

IMM improves tracking accuracy of maneuvering aircraft.

The algorithm effectively predicts target location for conflict avoidance.

Enhanced feedback maneuvering reduces collision risk.

Abstract

The practical problem of tracking a maneuvering aircraft during flight has always been a crucial task in order to safeguard airborne assets from unknown threats. Therefore, the need for an efficient target detection and identification technique is substantial and growing. The multiple model (MM) estimation have proven to be one of the most reliable and accurate among various filtering algorithms. In this paper we will implement the Interacting Multiple Model (IMM) estimation technique for the aforementioned purpose of target identification. This target's motion, though defined by predefined dynamics, is obscured due to the noises from tracking sensors. The algorithm intends to predict the location of target and provide feedback maneuver to the reference aircraft in order to avoid a conflict.