SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

Xianjun Liu,Zixuan Wang,Xixiang Liu,Lei Wang,Yongjiang Huang

doi:10.1109/access.2021.3069469

Abstract

Motivated by the problem that water-track Doppler Velocity Log (DVL) cannot effectively suppress the error accumulation of strap-down inertial navigation system (SINS), this paper proposes a novel SINS/DVL integrated navigation algorithm for deep and long cruising range Human Occupied Vehicle (HOV). Such algorithm decomposes the navigation process into two tightly coupled working modes: alignment mode and navigation mode. In the alignment mode, HOV is controlled to perform horizontal circular motion for several minutes to realize Self-Aided SINS. Combining the precise navigation solutions provided by Self-Aided SINS and measurements from DVL with water track, recursive least square (RLS) algorithm is adopted to estimate heading misalignment angle between SINS and DVL and horizontal ocean current velocity. In the navigation mode, both horizontal ocean current velocity obtained in the alignment mode and DVL measurements are utilized to assist SINS, thus enabling SINS/DVL/Current integrated navigation. A square trajectory with a navigation-grade inertial measurement unit (IMU) is simulated to evaluate the proposed SINS/DVL integrated navigation algorithm. Simulation results show that the proposed SINS/DVL integrated navigation is capable of suppressing SINS error divergence effectively and efficiently. In addition, the feasibility and effectiveness of Self-Aided SINS based on horizontal circular motion is also verified by field test with real IMU data.

Highlights

Human Occupied Vehicle (HOV) has become an indispensable platform for deep-sea research and exploration, since it is the only equipment that can directly send scientists and researchers to such extreme depth to perform underwater missions [1]–[3]
Accurate navigation and localization is still significantly challenging for deep-diving HOV, especially in midwater where both Global Positioning System (GPS) and Doppler Velocity Log (DVL) are unavailable
This paper proposes a novel strap-down inertial navigation system (SINS)/DVL integrated navigation scheme for HOV operated in midwater as shown in Fig.2, which includes two tightly coupled working modes: alignment mode and navigation mode

Summary

INTRODUCTION

Human Occupied Vehicle (HOV) has become an indispensable platform for deep-sea research and exploration, since it is the only equipment that can directly send scientists and researchers to such extreme depth to perform underwater missions [1]–[3]. Benefiting from the development of maneuvering control technology, steady-state maneuvering characteristics of underwater vehicle may make it possible for us to avoid the barriers of existing underwater navigation sensor technology and find a feasible midwater navigation scheme [17]–[19] To this end, this paper proposes a novel SINS/DVL integrated navigation scheme for HOV operated in midwater as shown in Fig., which includes two tightly coupled working modes: alignment mode and navigation mode. Combined with DVL measurements (working in water tracking mode) and precise navigation solution of Self-Aided SINS, local ocean current velocity can be estimated effectively and efficiently. When HOV is controlled to perform periodic physical maneuvers, the true horizontal velocity (VN , VE ), relative to the horizontal speed error (δVN , δVE ), can be regarded as high-frequency signals That make it possible for extracting available auxiliary information from pure SINS output results to constitute self-assisted navigation system. The detailed descriptions of delay correction Highpass filter (DC-HPF) are summarized as follows: 1) Designing reasonable IIR digital high-pass filter specifications fp, fs, Ap, As based on spectrum analysis and obtain transfer function of the digital filter H (z); 2) Calculating the leading time t (ω ) by Eq (14) and the number of delay buffers N by Eq (15); 3) Delaying the output of T-HPF for N bit

VELOCITY-AIDED SINS

Findings

VIII. CONCLUSION