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Discover the mid-low precision FOG IMU system: a cost-effective, shock-resistant inertial navigation solution for UAVs, robotics, and marine applications. Learn about its modular design, quick startup, and high stability. In the fields of unmanned systems, intelligent manufacturing, and precise control, the inertial measurement unit (IMU) is becoming a crucial "invisible technology". Today, we will take you to deeply understand a solution that performs well in actual projects - a mid-low precision FOG IMU system designed based on open-loop fiber optic gyroscope (FOG) and MEMS accelerometer.This is not only an inertial sensing device, but also a perfect balance between miniaturization, high cost-effectiveness, and precise navigation. 1. Why Choose FOG IMU? As the traditional platform-based inertial navigation systems are gradually fading from the historical stage, strapdown inertial navigation systems (SINS) have become mainstream relying on mathematical modeling and digital computing.So, what are the core advantages of FOG IMU?(1) Resistance to shock and interference: Fiber optic gyros are naturally shock-resistant and can withstand high G forces, making them particularly suitable for harsh environments.(2) Quick startup: No need for complex initialization; plug and play once powered on.(3) Precise and cost-effective: While meeting navigation requirements, it also controls costs.(4) Easy integration: Small size, low power consumption, and easy embedding.Therefore, it is widely applied in fields such as unmanned aerial vehicles, robots, vehicle-mounted systems, and maritime navigation. 2. Highlights of System Architecture This FOG IMU adopts a modular design, consisting of a three-axis fiber optic gyroscope, a three-axis MEMS accelerometer, a data acquisition module, and a high-speed DSP, supplemented by temperature compensation and error modeling algorithms, to achieve stable output.The six sensitive axes are arranged in three-dimensional orthogonal manner, combined with a software compensation mechanism, to eliminate the influence of structural errors on navigation accuracy.Moreover, this system has also been verified through simulation, ensuring that it still meets the required accuracy for navigation calculations even when using low-precision sensors. 3. Data Acquisition Module: The "Neural Center" of IMU We have specially optimized the data acquisition link:(1) Analog signal conditioning: Two-stage amplification + analog filter, enhancing signal clarity.(2) High-precision ADC sampling: 10ms update cycle, ensuring rapid system response.(3) Temperature compensation channel: Integrated chip and environmental temperature monitoring, achieving full environmental adaptability.This module plays a crucial role in enhancing the overall accuracy of the system. 4. Performance and Real-World Feedback After the prototype deployment and system testing, the performance of this FOG IMU system is as follows:(1) Excellent stability of attitude angles(2) Static errors within the controllable range(3) Strong anti-interference performance, capable of adapting to rapid dynamic changesCurrently, this system has been put into use in a certain type of robot navigation platform, and the feedback is consistent and good. 5. Application Domain Outlook The FOG IMU system is ready to be applied in the following scenarios:(1) Navigation for unmanned aircraft and unmanned vehicles(2) Marine measurement systems(3) Industrial automation equipment(4) Attitude control for low-orbit satellites(5) Intelligent robots and precise positioningIn the future, we will also launch an upgraded version of the FOG IMU tailored for high-precision requirements such as UF-100A. Stay tuned for more updates!   UF100A Middle Precision Fiber Optic Gyroscope Based IMU    

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

Learn how to reduce magnetic sensitivity in FOG IMUs with advanced techniques like depolarization, magnetic shielding, and error compensation. Discover high-precision solutions for aviation and navigation systems. In high-precision inertial measurement units (IMUs), the fiber optic gyroscope (FOG) is one of the core components, and its performance is crucial for the positioning and attitude perception of the entire system. However, due to the Faraday effect of the optical fiber coil, FOG is extremely sensitive to magnetic field anomalies, which directly leads to the degradation of its zero bias and drift performance, thereby affecting the overall accuracy of the IMU. So, how is the magnetic sensitivity of FOG IMU generated? And how can this influence be effectively suppressed? This article will deeply analyze the technical paths to reduce the magnetic sensitivity of FOG from the perspective of theory to engineering practice. 1. FOG Magnetic Sensitivity: Starting from the Physical Mechanism The reason why FOG is sensitive to magnetic fields lies in the Faraday effect - that is, when linearly polarized light passes through a certain material, under the influence of a magnetic field, its polarization plane will rotate. In the Sagnac ring interference structure of FOG, this rotational effect will cause a phase difference between two beams propagating in opposite directions, thereby leading to measurement errors. In other words, the interference of magnetic fields is not static but dynamically affects the output of FOG in a drifting manner.Theoretically, an axial magnetic field perpendicular to the axis of the optical fiber coil should not trigger the Faraday effect. However, in reality, due to the slight inclination during the winding of the optical fiber, the "axial magnetic effect" is still triggered. This is the fundamental reason why the influence of magnetic fields cannot be ignored in high-precision applications of FOG. 2. Two major technical approaches to reducing FOG magnetic sensitivity (1) Improvements at the optical device level a. Depolarization technology By replacing polarization-preserving fibers with single-mode fibers, the magnetic field response can be reduced. Because single-mode fibers have a weaker response to the Faraday effect, the sensitivity is reduced at the source.b. Advanced winding processControlling the winding tension and reducing residual stress within the fibers can effectively reduce magnetic induction errors. Combined with an automated tension control system, it is the key to improving the consistency of polarization-preserving coils.c. New low-magnetic-sensitivity optical fibersAt present, some manufacturers have launched optical fiber materials with low magnetic response coefficients. When used in combination with ring structures, they can optimize the magnetic anti-interference ability at the material level. (2) System-level Anti-magnetic Measures a. Magnetic Error Modeling and CompensationBy installing magnetic sensors (such as flux gates) to monitor the magnetic field in real time and introducing compensation models in the control system, the output of FOG can be dynamically corrected.b. Multi-layer Magnetic Shielding StructureUsing materials such as μ-alloys to construct double-layer or multi-layer shielding cavities can effectively weaken the influence of external magnetic fields on FOG. Finite element modeling has confirmed that its shielding efficiency can be increased by tens of times, but it also increases the system weight and cost. 3. Experimental Verification: How significant is the influence of magnetic fields? In a set of experiments based on a three-axis turntable, researchers collected the drift data of FOG in both open and closed states. The results showed that when the magnetic field interference was enhanced, the drift amplitude of FOG could increase by 5 to 10 times, and obvious spectral interference signals (such as 12.48Hz, 24.96Hz, etc.) appeared.This further indicates that if no effective measures are taken, the accuracy of FOG will be greatly compromised in actual aviation, space, and other high electromagnetic environments. 4. Practical Recommendations: How to Enhance the Anti-Magnetic Capability of FOG IMU? In practical applications, we recommend the following combination strategies:(1) Select polarization-eliminating FOG structure(2) Use low-magnetic-response optical fibers(3) Introduce optical fiber winding equipment with automatic tension control(4) Install three-dimensional flux gates and build error models(5) Optimize the design of μ-alloy shielding shellsTaking the U-F3X80, U-F3X100 series launched by Micro-Magic as examples, the integrated optical gyroscopes inside them have maintained stable output even in the presence of magnetic interference through multiple technical improvements, making them the preferred solution among current aviation-grade IMUs.  5. Conclusion: Accuracy determines the application level, and magnetic sensitivity must be taken seriously In high-precision positioning, navigation and guidance systems, the performance of FOG IMU determines the reliability of the system. And magnetic sensitivity, as a problem that has been overlooked for a long time, is now becoming one of the "bottlenecks" of accuracy. Only through collaborative optimization from materials, structures to system level can we truly achieve high-precision output of IMU in complex electromagnetic environments. If you are confused about IMU selection or FOG accuracy issues, you might as well rethink from the perspective of magnetic sensitivity. Micro-Magic’s FOG IMU U-F3X80, U-F3X90, U-F3X100,and U-F300 are all composed of fiber optic gyroscopes. In order to improve the accuracy of FOG IMU, we can completely reduce the magnetic sensitivity of the fiber optic gyroscopes inside them by corresponding technical measures. U-F3X80 Fiber Optic Gyroscope IMU U-F3X90 Fiber Optic Gyroscope IMU U-F100A Middle Precision Fiber Optic Gyroscope  U-F3X100 Fiber Optic Gyroscope IMU      

Explore high-precision calibration for FOG IMU (Fiber Optic Gyro Inertial Measurement Unit) across full temperature ranges. Learn key error modeling techniques, 3D bidirectional rate/one-position calibration, and Piecewise Linear Interpolation (PLI) compensation for enhanced navigation accuracy in drones, autonomous vehicles, and robotics. How can FOG IMU (Inertial Measurement Unit based on Fiber Optic Gyroscope) maintain high precision in complex temperature environments? This article comprehensively analyzes its error modeling and compensation methods. 1. Introduction to FOG IMU: The "Brain" of Flight Navigation System In modern aircraft, especially in small rotor unmanned aerial vehicle systems, FOG IMU is the core component of the navigation information and attitude measurement system. The fiber optic gyroscope (FOG) based on the Sagnac effect has advantages such as high precision, strong shock resistance, and fast response, but it has poor adaptability to temperature changes. This can easily lead to measurement errors during the flight process where the dynamic environment changes drastically, thereby affecting the performance of the overall navigation system. 2. Error Sources: Analysis of Common Measurement Deviations of FOG IMU The errors of FOG IMU can be mainly classified into two types:(1) Angular velocity channel error: This includes installation error, proportional factor error, zero bias error, etc. (2) Acceleration channel error: Mainly caused by installation error, temperature drift and dynamic disturbance. These errors accumulate in the actual environment, seriously affecting the stability and accuracy of the flight control system. 3. Limitations of Traditional Calibration Methods Although traditional static multi-orientation calibration and angular velocity method can partially address the issue of errors, they have obvious shortcomings in the following aspects:(1) Unable to balance accuracy and computational efficiency(2) Inapplicable to full temperature range compensation(3) Dynamic disturbances affect the stability of calibrationThis requires a more intelligent and efficient error modeling and temperature compensation mechanism. 4. Detailed Explanation of the Three-Dimensional Positive and Negative Speed/One-Axis Attitude Calibration Method in the Full Temperature Range (1) Precise Calibration at Multiple Temperature PointsBy setting multiple temperature points ranging from -10°C to 40°C and conducting three-axis rotation calibration at each point, temperature-related error parameters can be collected.(2) Three-Dimensional Positive and Negative Speed Method: Precisely Simulating Real Flight ConditionsUsing a single-axis rate turntable and a high-precision hexahedral tool, positive and negative speed calibration in the X/Y/Z axis directions can be achieved, enhancing the system's adaptability to dynamic environments.(3) One-Axis Attitude Stabilization: Quickly Capturing System Zero OffsetWhile maintaining a static state, initial offsets under different temperatures are recorded to provide precise data support for subsequent error modeling. 5. Piecewise Linear Interpolation (PLI): A Precise Error Compensation Tool with Low Computational Load To meet the error compensation requirements of FOG IMU across the entire temperature range, this paper proposes the Piecewise Linear Interpolation algorithm (PLI), which has the following characteristics:(1) Low computational load: Suitable for embedded navigation systems with limited resources(2) Strong real-time compensation capability: Error is dynamically adjusted with temperature changes(3) Easy to deploy and upgradeCompared with the high-order least squares method, the PLI scheme ensures the compensation accuracy while significantly reducing the system's computational burden, making it suitable for real-time computing scenarios during flight. 6. Practical Verification: Outstanding Performance in Complex Flight Environments Through on-board field experiments, this method significantly enhanced the measurement accuracy and environmental adaptability of the system under various temperatures and dynamic disturbances, providing a solid navigation foundation for subsequent high-performance small rotorcraft flight platforms. 7. Conclusion: Mastering the error modeling and compensation of FOG IMU is the key to building a highly reliable flight platform. With the development of unmanned aerial vehicles and intelligent flight systems, the requirements for the accuracy of navigation systems have become increasingly stringent. By introducing the three-position positive and negative speed calibration and segmented linear interpolation compensation methods, the adaptability and accuracy of FOG IMU in the full temperature range and strong dynamic environment can be significantly improved. In the future, this technology is expected to play a greater role in autonomous driving, robot navigation, and high-precision map collection and other fields. Micro-Magic’s U-F3X80, U-F3X90, U-F3X100,and U-F300 , we can use full-temperature three-way positive and negative rate/one position calibration and PLI compensation method. According to the error characteristics of fiber optic gyro and quartz flexible accelerometer, the FOG inertial measurement unit error model is established, and the three-bit positive and negative rate/one-position calibration scheme is designed at each constant temperature point. The PLI algorithm is used to compensate the zero bias and scale factor temperature errors of the system in real time, reducing the calibration workload and the calculation amount of the compensation algorithm, and improving the system dynamics, temperature environment adaptability and measurement accuracy. U-F3X80 Fiber Optic Gyroscope IMU U-F100A Middle Precision Fiber Optic Gyroscope Based IMU U-F3X100 Fiber Optic Gyroscope IMU U-F3X90 Fiber Optic Gyroscope IMU