Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware

Mohammed Abdulhakim Al-Absi,Ahmed Abdulhakim Al-Absi,Rui Fu,Young-Sil Lee,Hoon-Jae Lee,Ki-Hwan Kim

doi:10.3390/electronics10243067

Mohammed Abdulhakim Al-Absi, Ahmed Abdulhakim Al-Absi + Show 4 more

Open Access

https://doi.org/10.3390/electronics10243067

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Abstract

Recently, Unmanned Aerial Vehicles (UAVs) have made significant impacts on our daily lives with the advancement of technologies and their applications. Tracking UAVs have become more important because they not only provide location-based services, but are also faced with serious security threats and vulnerabilities. UAVs are smaller in nature, move with high speed, and operate in a low-altitude environment, which makes it conceivable to track UAVs using fixed or mobile radars. Kalman Filter (KF)-based methodologies are widely used for extracting valuable trajectory information from samples composed of noisy information. As UAVs’ trajectories resemble uncertain behavior, the traditional KF-based methodologies have poor tracking accuracy. Recently, the Diffusion-Map-based KF (DMK) was introduced for modeling uncertainties in the environment without prior knowledge. However, the model has poor accuracy when operating in environments with higher noise. In order to achieve better tracking performance, this paper presents the Uncertainty and Error-Aware KF (UEAKF) for tracking UAVs. The UEAKF-based tracking method provides a good tradeoff among preceding estimate confidence and forthcoming measurement under dynamic environments; the resulting filter is robust and nonlinear in nature. The experimental results showed that the UEAKF-based UAV tracking model achieves much better Root Mean Square Error (RMSE) performance compared to the existing particle filter-based and DMK-based UAV tracking models.

Highlights

Unmanned Aerial Vehicles (UAVs)/drone communication can be divided into four main types: unmanned aircraft to unmanned aircraft (U2U), unmanned aircraft to ground station (U2Gs), unmanned aircraft to the network (U2N), and unmanned aircraft to satellite (U2S) [1,2]
This paper presents the tracking of an unmanned aerial vehicle using uncertainty and error-aware Kalman Filter methodologies
The Uncertainty and Error-Aware KF (UEAKF) in Equations (15), (18), (23) and (24) for tracking unmanned aerial vehicles is modeled with the updating parameter δw = δz and δw = δz, and is executed together with other existing Kalman Filter (KF)-based UAV tracking methodologies

Summary

Introduction

Environment, this manuscript presents an error-aware Kalman filter (UEAKF) employing RLS for tracking drones. The UEAKF UAV tracking model achieves much better RMSE performance in comparison with the PF-based and DMK-based UAV tracking model under a stochastic environment The remainder of this manuscript is organized as follows: In Section 2, the unmanned aerial vehicle tracking model using the uncertainty and error-aware Kalman filter algorithm in unknown and noisy environments is presented. 2. Tracking of Unmanned Aerial Vehicles Using Uncertainty and Error-Aware Kalman Filter Algorithm. This paper presents the tracking of an unmanned aerial vehicle using uncertainty and error-aware Kalman Filter methodologies. Kalman Filter Algorithm Let us consider a stochastic unmanned aerial vehicle tracking system as follows: yl+1 = Bl yl + δl xl,. The covariance matrix with constraint is computed for error update

Uncertainty and Error-Aware Kalman Filter Algorithm

Prediction Phase

Updating Phase

Covariance Matrix

Simulation Analysis and Result

Conclusions