Research on Algorithm of Airborne Dual-Antenna GNSS/MINS Integrated Navigation System

Abstract

In view of the difficulties regarding that airborne navigation equipment relies on imports and the expensive domestic high-precision navigation equipment in the manufacturing field of Chinese navigable aircraft, a dual-antenna GNSS (global navigation satellite system)/MINS (micro-inertial navigation system) integrated navigation system was developed to implement high-precision and high-reliability airborne integrated navigation equipment. First, the state equation and measurement equation of the system were established based on the classical discrete Kalman filter principle. Second, according to the characteristics of the MEMS (micro-electric-mechanical system), the IMU (inertial measurement unit) is not sensitive to Earth rotation to realize self-alignment; the magnetometer, accelerometer and dual-antenna GNSS are utilized for reliable attitude initial alignment. Finally, flight status identification was implemented by the different satellite data, accelerometer and gyroscope parameters of the aircraft in different states. The test results shown that the RMS (root mean square) of the pitch angle and roll angle error of the testing system are less than 0.05° and the heading angle error RMS is less than 0.15° under the indoor static condition. A UAV flight test was carried out to test the navigation effect of the equipment upon aircraft take-off, climbing, turning, cruising and other states, and to verify the effectiveness of the system algorithm.

Improving Vehicle Heading Angle Accuracy Based on Dual-Antenna GNSS/INS/Barometer Integration Using Adaptive Kalman Filter

“…Moreover, the error caused by the change in g is minimal. Based on the chosen system state and the above-mentioned reasons, we can obtain the error-state equation through combining ( 4)-( 9) [18] .…”

“…At the same time, increasing the number of GNSS receivers results in an increase in costs. As technology evolves, dual-antenna GNSS receivers, utilizing baseline orientation to determine heading angles, are widely applied for integration with an IMU [17,18]. A wheel speed sensor (WSS) is another option to replace the second GNSS receiver because it can measure the speed of individual wheels.…”

“…Inspired by [18], a dual-antenna GNSS receiver is added to this study to improve the vehicle heading angle accuracy and supply the initial heading. The barometer that calculates elevation changes by obtaining the difference in air pressure values at different locations is one of the common sensors in consumer-grade devices.…”

The accuracy of vehicle position and heading angle improvement based on dual-antenna GNSS/INS/Barometer integration using extended Kalman filter

“…When the vehicle is travelling in a straight line, the heading angle tends to diverge due to the lack of observations in the lateral direction [23]. In order to suppress the heading angle divergence and improve accuracy, GNSS dual antennas were introduced into the navigation system due to the noncumulative errors [24]. Owing to the presence of angular outputs from both systems, the mounting angles of GNSS and INS must to be correctly estimated.…”

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

“…Madyastha [ 28 ] proposed a Kalman filtering method based on attitude error states. The error state Kalman filter (ESKF) is designed for covariance estimation, which serves as a weight for the inertial odometry optimization process [ 29 , 30 , 31 ]. These results validate the ESKF’s effectiveness for error−state estimation, although a full−state estimation based on ESKF is not available yet.…”