Performance Evaluation of IMU and DVL Integration in Marine Navigation

Abstract

Global navigation satellite system (GNSS) spoofing poses a significant threat to maritime logistics. Many maritime electronic devices rely on GNSS time, positioning, and speed for safe vessel operation. In this study, inertial measurement unit (IMU) and Doppler velocity log (DVL) devices, which are important in the event of GNSS spoofing or outage, are considered in conventional navigation. A velocity integration method using IMU and DVL in terms of dead-reckoning is investigated in this study. GNSS has been widely used for ship navigation, but IMU, DVL, or combined IMU and DVL navigation have received little attention. Military-grade sensors are very expensive and generally cannot be utilized in smaller vessels. Therefore, this study focuses on the use of consumer-grade sensors. First, the performance of a micro electromechanical system (MEMS)-based yaw rate angle with DVL was evaluated using 60 min of raw data for a 50 m-long ship located in Tokyo Bay. Second, the performance of an IMU-MEMS using three gyroscopes and three accelerometers with DVL was evaluated using the same dataset. A gyrocompass, which is equipped on the ship, is used as a heading reference. The results proved that both methods could achieve less than 1 km horizontal error in 60 min.

Sensors and System for Vehicle Navigation

“…The study [3] presented by Fukuda, G. et al focuses on the use of consumer-grade sensors for ship navigation. Generally, global navigation satellite system (GNSS) spoofing poses a significant threat to maritime logistics.…”

Application of Initial Bias Estimation Method for Inertial Navigation System (INS)/Doppler Velocity Log (DVL) and INS/DVL/Gyrocompass Using Micro-Electro-Mechanical System Sensors

“…In INS/DVL, when GPS position correction is unavailable, the initial bias of the angular rate and acceleration sensors significantly affect the position estimation accuracy. In a previous study [ 8 ], initial bias estimation was performed using angular velocity and acceleration estimates from the KF, but adjusting the parameters of the KF was time consuming. Furthermore, using this adjustment to perform an estimate with similar accuracy on the next voyage was expected to be difficult for the reasons discussed above.…”

“…Therefore, we decided to investigate a more reproducible method for initial bias estimation, which has a large impact on position estimation error in INS/DVL that does not directly use values from the KF. The advantages and disadvantages of INS/GPS with MEMS IMU can be summarized as follows: (a) GPS corrections can be used to obtain highly accurate position estimates; (b) the speed can be estimated without drift errors using GPS speed corrections; (c) the necessary accuracy for roll and pitch can be estimated using only a MEMS IMU [ 8 ]; and (d) improving the accuracy of heading estimation using only an IMU on a vessel with drift is difficult. Considering the advantages (a–c), the angular velocity and acceleration estimates from the trajectory generator (TG) [ 10 ] for simulating inertial navigation systems could be used for initial bias estimation.…”

Toward Structural Learning and Enhanced YOLOv4 Network for Object Detection in Optical Remote Sensing Images

“…The real‐time automatic object detection for earth observation has a wide range of applications, such as marine environment monitoring, search, and rescue, [ 1 ] military target positioning, [ 2 ] military reconnaissance, [ 3 ] navigation, etc. [ 4 ] Massive data captured by different types of sensors are used to detect multi‐scale objects, including synthetic aperture radar (SAR), [ 5,6 ] thermal infrared, [ 7 ] and optical vision. [ 8,9 ] Among them, the high‐resolution ORS can be openly obtained from Google Earth, with rich details and interpretable feature expressions to support accurate and real‐time object detection.…”