Vol. 11, No 3: 61–70.

Computer Science and Informatics

 2026

Scientific article

UDK 004.021
pdf-version

Anna A. Basalova
bachelor’s degree, Petrozavodsk State University
(Petrozavodsk, Russia),
anna_basalova@mail.ru

Comparative Analysis of Inertial Sensor Filtering Algorithms for Unmanned Surface Vehicle Attitude Estimation

Scientific adviser:
Aleksandr A. Rogov
Reviewer:
Dmitry G. Korzun
Paper submitted on: 09/14/2026;
Accepted on: 09/28/2026;
Published online on: 09/28/2026.
 Abstract. This paper presents a field comparative evaluation of the Extended Kalman Filter (EKF), Mahony, and Madgwick algorithms for attitude estimation on an unmanned catamaran. The novelty of this study is the validation of these filters on an ESP32 microcontroller in real-world conditions on Lake Onega. The experimental methodology comprised static tests and five dynamic sessions. Quantitative results demonstrated that the Mahony filter yielded the lowest mean positioning error (40.07 m), with static yaw drift under 0.18°. These findings are applicable to USV autopilots operating in unstable GPS conditions.
Keywords: inertial sensors, Kalman filter, Mahony filter, Madgwick filter, unmanned surface vehicle attitude estimation, unmanned surface vehicle, GPS-denied navigation, sensor fusion

For citation: Basalova, A. A. Comparative Analysis of Inertial Sensor Filtering Algorithms for Unmanned Surface Vehicle Attitude Estimation. StudArctic forum. 2026, 11 (3): 61–70.

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