Comparison of Dynamic Models for Aerial Target Tracking Maneuvers Based on Stability and Measurement Loss

Today, unmanned vehicles get involved in challenging missions like search and rescue, surveillance, recognition, border patrolling, and other information-gathering roles. These vehicles prevent humans from being in dangerous situations, and their cost of production is lower than manned vehicles. Many researchers in past decades have studied the problem of tracking maneuvering targets based on noisy sensor measurements. The key to successfully tracking a target is to extract useful information from observations about the target state. Indeed, a proper model of the target dynamic and sensor observation will facilitate the extraction of this information, significantly. The filters used for estimation are the base model because there is knowledge of the target motion model. The purpose of this paper is to investigate and compare the capability of different dynamic models in tracking a high-maneuverability target using a 3D space by using a visual sensor. The goal is to test 10 different dynamic models with several different random processes and filters to find the most suitable model for tracking an aerial target. Sensor failure and model processing error have been selected as the two main criteria in measuring the performance of these models. We have introduced the best dynamic model based on the behavior of these models against these defects.

Automated summary

This paper investigates the importance of using unmanned vehicles for challenging missions such as search and rescue, surveillance, and recognition, and explores the capability of different dynamic models in tracking high-maneuverability targets. Through testing and comparing 10 different dynamic models and filters, it identifies the most suitable model for tracking aerial targets.