inertial navigation system

Key Points Product: I3500 GNSS-Aided MEMS INS Key Features: Components: Cost-efficient MEMS IMU, dual-antenna satellite positioning module, magnetometers, and barometer. Function: Provides high-precision navigation data, maintaining performance during GNSS outages. Applications: Suitable for drones, autonomous navigation, surveying, and motion analysis. Inertial Navigation: Combines inertial measurements for position, velocity, and attitude calculation. Conclusion: The I3500 exemplifies the integration of MEMS INS and GNSS, enhancing navigation reliability and accuracy across various sectors.   MINS/GNSS integrated navigation, refers to the fusion of information from both MINS (MEMS INS) and GNSS (Global Navigation Satellite System). This integration combines the strengths of both systems to complement each other and achieve accurate PVA (Position, Velocity, Attitude) results. Classification of MEMS Inertial Navigation Systems After more than 30 years of development, MEMS inertial technology has advanced rapidly and seen wide application. Various practical MEMS inertial devices and MEMS INS have emerged, finding extensive use in fields such as aerospace, maritime, and automotive industries. Tactical-grade MEMS gyroscopes (with bias stability of 0.1°/h to 10°/h, 1σ) and high-precision MEMS accelerometers (with bias stability of 10⁻⁵g to 10⁻⁶g, 1σ) have marked the entry of tactical-grade MEMS INS into the model application stage. Generally, MEMS inertial systems can be classified into three levels: Inertial Sensors Assembly (ISA), Inertial Measurement Unit (IMU), and Inertial Navigation System (INS), as illustrated in Figure 1. Fig.1 Three Levels Of Mems Ins (2) MEMS ISA: Comprised solely of three MEMS gyroscopes and three MEMS accelerometers, it lacks the capability to operate independently. MEMS IMU: Builds on the MEMS ISA by adding A/D converters, mathematical processing chips, and specific programs, enabling it to independently collect and process inertial information. MEMS INS: Further expands on the MEMS IMU by incorporating coordinate transformation, filtering processes, and auxiliary modules, which typically include magnetometers and GNSS receiver boards. Auxiliary sensors like magnetometers are particularly significant in aiding MEMS INS alignment and enhancing performance. The three newly launched MEMS INS (Micro-Magic Inc-Mechanical System Inertial Navigation System) models by Ericco, shown in the image below, are suitable for applications in drones, flight recorders, intelligent unmanned vehicles, roadbed positioning and orientation, channel detection, unmanned surface vehicles, and underwater vehicles. Fig.2 The Three Newly Launched Mems Ins Models By Ericco How GNSS-Aided MEMS INS Works GNSS provides users with all-weather, high-precision absolute position and time information, while inertial navigation systems (INS) offer high short-term resolution and strong autonomy. Their complementary characteristics enhance overall performance: INS can leverage its high short-term accuracy to provide GNSS with more continuous and complete navigation information, while GNSS can help estimate INS error parameters like bias, thus obtaining more precise observations and reducing INS drift. Fig.3 Three Levels Of Mems Ins Specifically, GNSS uses signals from orbiting satellites to calculate position, time, and velocity. As long as the antenna has a line-of-sight connection with at least four satellites, GNSS navigation achieves excellent accuracy. When satellite visibility is obstructed by obstacles like trees or buildings, navigation becomes unreliable or impossible. INS calculates relative position changes over time using angular rate and acceleration information from the inertial measurement unit (IMU). The IMU comprises six complementary sensors arranged on three orthogonal axes. Each axis has an accelerometer and a gyroscope. Accelerometers measure linear acceleration, while gyroscopes measure rotational rate. With these sensors, the IMU can accurately measure its relative motion in 3D space. INS uses these measurements to compute position and velocity. Another advantage of IMU measurements is that they provide angular solutions about the three axes. INS converts these angular solutions into local attitudes (roll, pitch, and yaw), providing this data along with position and velocity. Fig.4 The Inertial Measurement Unit Body Coordinate System Real-Time Kinematic (RTK) is a mature high-precision positioning algorithm of GNSS, capable of achieving centimeter-level accuracy in open environments. However, in complex urban environments, signal obstructions and interferences reduce the ambiguity fixing rate, leading to decreased positioning capability. Therefore, researching GNSS RTK and INS integrated positioning systems is crucial for fields such as autonomous navigation, surveying and mapping, and motion analysis. I3500 newly launched by Micro-Magic Inc is a Cost-efficient GNSS aided MEMS INS with a highly reliable MEMS IMU and a dual-antenna full-system full-band positioning and directional satellite module. It also integrates magnetometers and a barometer, which can calculate the size of the attitude Angle and help the drone navigate to the desired altitude. Conclusion Integrating MEMS Inertial Navigation Systems (INS) with GNSS technology significantly enhances navigation accuracy by combining their strengths. MEMS INS, with its rapid advancement, is now widely used in aerospace, maritime, and automotive industries. GNSS provides precise positioning, while MEMS INS ensures continuous navigation, even during GNSS outages. The I3500 by Micro-Magic Inc exemplifies this integration, offering high-precision navigation data, ideal for autonomous navigation, surveying, and motion analysis. In summary, GNSS and MEMS INS integration revolutionizes navigation by improving accuracy, reliability, and versatility across various applications.   I3500 High Accuracy 3-Axis Mems Gyro I3500 Inertial Navigation System    

In the design of navigation and control systems, AHRS (Attitude and Heading Reference System) and INS (Inertial Navigation System) are two key technical modules. Although they are both based on inertial measurement units (IMUs), their processing methods, output results, and application scopes are essentially different. This article will compare AHRS and INS in depth from the dimensions of system composition, sensor fusion algorithm, mathematical model, error source analysis, and typical applications, to provide theoretical and application support for engineering practice and research. 1. System Structure Overview AHRS System Structure AHRS systems are usually composed of three types of sensors:Three-axis gyroscopes (Angular Rate Sensors)；Three-axis accelerometers (Linear Acceleration Sensors)；Three-axis magnetometers (Earth Magnetic Field Sensors) These data are fused through a filtering algorithm to estimate the current three-dimensional posture (expressed in Euler angles or quaternions). INS system structure INS systems are usually composed of IMU (gyroscope + accelerometer), and realize navigation functions through integral calculation: Integrate acceleration to get velocity, and then integrate to get position; Integrate angular velocity to calculate attitude changes. INS can be integrated into an "autonomous navigation system" to achieve continuous positioning for a certain period of time even in an environment where GPS is not available. 2. Core Mathematical Formulas and Calculation Process 1. Attitude estimation (AHRS) Assume that the three-axis angular velocity isUsing quaternionRepresents the posture, then the posture update formula is as follows: Combined with the magnetometer and accelerometer, attitude error correction is achieved through complementary filtering or extended Kalman filtering (EKF). Schematic diagram of attitude error correction formula (complementary filtering):             2. Inertial Navigation (INS) The core of INS is to integrate acceleration twice: Speed ​​calculation: Position calculation: Since the IMU data contains noise and bias, the integration process will lead to the accumulation of errors (drift): To this end, INS is often fused with GPS, vision, or UWB to constrain error drift. 3. Error model analysis Error Source AHRS INS Gyroscope Bias Causes slow attitude drift, correctable via magnetometer Accumulates into significant drift in attitude, velocity, and position Accelerometer Error Affects gravity direction estimation Severely impacts position estimation; long-term errors grow quadratically Magnetometer Interference Impacts yaw (heading) estimation Generally unaffected (no magnetometer used) Numerical Integration Error First-order integration with manageable errors Second-order integration leads to significant errors Algorithm Robustness High (mature attitude decoupling algorithms) Moderate; requires robust filtering and error modeling support 4. Comparison of Sensor Fusion Algorithms Algorithm Type Typical Usage in AHRS Typical Usage in INS Complementary Filtering Fast attitude fusion for low-computational-power devices Rarely used (insufficient precision) Kalman Filter (EKF) Fuses gyro, accelerometer, and magnetometer to correct errors Fuses gyro, accelerometer, and external references (e.g., GPS) Zero-Velocity Update (ZUPT) Not used Commonly applied in pedestrian navigation to reduce drift SLAM/Visual-Inertial Navigation Not applicable Combined with visual sensors to enhance navigation accuracy   5. Comparison of Typical Application Scenarios Application AHRS INS Small UAVs ✅ For attitude control & heading estimation ✅ Used for path planning or in GPS-denied environments VR/AR Headsets ✅ Provides head orientation tracking ❌ Not required (position accuracy unnecessary) Autonomous Vehicles ❌ Attitude alone insufficient for navigation ✅ Critical for high-precision map matching and dead reckoning in GPS-denied zones Rocket Guidance ❌ Insufficient precision for standalone use ✅ High-precision INS required in high-dynamic environments Underground/Underwater ❌ Magnetometer failure in such environments ✅ Combines with sonar/UWB for precise navigation 6. Summary: A5000 vs I3700: Practical application of high-precision sensors in AHRS and INS A5000 – High-precision MEMS AHRS attitude sensor A5000 is a highly integrated digital output high-precision AHRS (attitude and heading reference system). Its core features include: Built-in three-axis high-precision accelerometer, gyroscope and magnetometer Use 6-state Kalman filter for sensor fusion to enhance the robustness of attitude estimation Output includes heading angle (Yaw), pitch angle (Pitch), roll angle (Roll) and angular velocity, acceleration information Suitable for attitude perception scenarios such as drones, robots, mining vehicles, AGVs, agricultural automation equipment, etc. Miniature design, suitable for space-constrained applications   I3700 – Full-featured Inertial Navigation System (INS) In contrast, the I3700 is an inertial navigation system for high-dynamic autonomous navigation applications, integrating a high-performance IMU module and supporting fusion with external signals (such as GPS). Its key features include: Output attitude angle + velocity + 3D position, supporting long-term navigation Suitable for scenarios that require full autonomous navigation capabilities, such as underground mines, GPS-free environments, precision agriculture or marine unmanned systems Supports multiple data interfaces, compatible with SLAM, GPS, and UWB fusion systems   With a powerful digital signal processing unit, it has excellent stability and long-term drift control capabilities A5000 Heading 9 Axis Navigation System Navigational Guided System Low Price High Accuracy   I3700 High Accuracy Agricultural Gps Tracker Module Consumption Inertial Navigation System Mtk Rtk Gnss Rtk Antenna Rtk Algorithm