inertial integrated navigation system factory

Key Points Product: Micro-Magic Inc’s MEMS GNSS/INS, including the I3500 model for mapping applications. Features: Size: Compact and lightweight for easy integration Accuracy: 2.5°/hr bias instability, 0.028°/√hr angular random walk MEMS accelerometer: ±6g range, zero bias instability <30μg GNSS integration for absolute positioning Advantages: Cost-effective, low power consumption, flexible placement, ideal for various applications like UAVs and aircraft, enhancing navigation precision through the fusion of INS and GNSS data. Compared to other INS solutions, a MEMS GNSS/INS has a lower size, weight, power consumption and cost. MEMS-based INS are suitable for most applications, including but not limited to: Marine Surveying, Land Surveying, UGVs, Helicopters, Antenna Targeting, Surveying, Robotics, UAVs. This article highlights five key benefits of using MEMS GNSS/INS. What is MEMS GNSS/INS? 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.The advantages and disadvantages of INS and GNSS are complementary. Therefore, combining the two technologies leverages their strengths to provide continuous, high-bandwidth, long-term, and short-term precise, comprehensive navigation parameters. In INS/GNSS or GNSS/INS integrated navigation systems, GNSS measurements suppress the drift of inertial navigation, while INS smooths the GNSS navigation results and compensates for signal interruptions. Five Reasons for Use MEMS GNSS/INS The manufacturing processes for MEMS devices are highly cost-effective due to mass production techniques used in the semiconductor industry. This results in lower production costs, making MEMS INS more affordable for a wide range of aviation applications. A MEMS GNSS/INS is not as costly as a FOG-based (fibre optic gyroscope) INS Lightweight and small By nature, MEMS are built on a miniature scale and measure in micrometres. This makes a MEMS-based INS an ideal fit for vehicles or machines that need a small payload.Take aviation for example, the compact size of MEMS GNSS/INS devices makes them ideal for use in aircraft where space is at a premium. This allows for easier integration into existing systems and more flexibility in aircraft design, potentially freeing up space for additional equipment or cargo. The lightweight nature of MEMS INS contributes to overall weight reduction in aircraft, which is crucial for enhancing fuel efficiency and performance. Lighter navigation systems allow for better payload capacity and improved aircraft range. Flexible placement The more compact nature of MEMS technology also allows the INS to be mounted in variable positions. The compact and efficient nature of MEMS INS makes them suitable for integration with advanced electronics and automation systems. This adaptability supports the development of more sophisticated management systems and enhances the overall functionality of modern aircraft. Low power consumption MEMS technology has advanced to the point where it can reduce power used, utilising power cycling and low power modes. MEMS GNSS/INS devices are designed to consume less power compared to traditional INS solutions. This reduced power consumption is beneficial for the electrical system, leading to lower operational costs and increased energy efficiency. For battery-powered applications, such as unmanned aerial vehicles (UAVs) or smaller aircraft, the lower power consumption of MEMS INS extends mission durations and operational capabilities, enabling longer flights and reducing the need for frequent recharges. GNSS integration With any kind of inertial navigation system, a MEMS GNSS/INS isn’t able to determine absolute position. By itself, the MEMS INS is able to determine the relative position of the vehicle from a known starting point, accounting for distance travelled and orientation. When a MEMS INS is combined with GNSS (global navigation satellite system) it takes advantage of the satellite technology to accurately determine the absolute position on Earth. With these two navigational technologies working in tandem, the strengths of both enable a high level of accuracy. An Excellent Solution Micro-Magic Inc is at the forefront of inertial navigation technology and has recently introduced three GNSS-aided MEMS INS products with varying levels of accuracy (mapping level, tactical level, and industrial level). Notably, the mapping level MEMS INS I3500 features a 2.5°/hr bias instability and a 0.028°/√hr angular random walk, along with a high-precision MEMS accelerometer with a large range (±6g, zero bias instability <30μg). More importantly, in an integrated navigation system, the INS leverages its high short-term accuracy to provide GNSS with continuous and comprehensive navigation information. Conversely, GNSS helps estimate INS error parameters, such as bias, resulting in more precise observations and reduced INS drift. GNSS offers stable long-term accuracy, provides initial values for position and speed, and corrects accumulated errors in the MEMS INS through filtering. The ER-GNSS/MINS-01 stands out as an excellent solution. I3500 High Accuracy 3-Axis Mems Gyro I3500 Inertial Navigation System I3700 High Accuracy Agricultural Gps Tracker Module Consumption Inertial Navigation System Mtk Rtk Gnss Rtk Antenna Rtk Algorithm I6700 Fiber Optic Three Axis Integrated Inertial Navigation System For Intelligent Navigation Fog Gyro Sensor

Key Points Product: GNSS/INS Integrated Navigation Solutions Key Features: Components: Integrated system includes GNSS receiver, Inertial Measurement Unit (IMU), and optional sensors like LiDAR or odometers. Function: Maintains accuracy and stability during GNSS signal loss using additional sensors or motion state constraints like ZUPT. Applications: Ideal for urban navigation, mining, oil logging, and other environments with potential signal obstructions. Inertial Navigation: Utilizes gyroscopes and accelerometers to measure position, velocity, and acceleration. Conclusion: The integrated system’s design is evolving, with solutions that enhance robustness in challenging environments while balancing cost and complexity. In a GNSS/INS integrated navigation system, GNSS measurements play a critical role in correcting the INS. Therefore, the proper functioning of the integrated system depends on the continuity and stability of the satellite signals. However, when the system operates under overpasses, tree canopies, or within urban buildings, the satellite signals can easily be obstructed or interfered with, potentially leading to a loss of lock in the GNSS receiver.This article discusses solutions for maintaining the accuracy and stability of GNSS/INS integrated navigation systems when satellite signals are lost. When the satellite signal is unavailable for an extended period, the lack of GNSS corrections causes the INS errors to accumulate rapidly, especially in systems with lower-precision inertial measurement units. This issue leads to a decline in the accuracy, stability, and continuity of the integrated system’s operation. Consequently, it is essential to address this problem to enhance the robustness of the integrated system in such complex environments. 1.Two Main Solutions to Address Signal Loss of GNSS/INS Currently, there are two main solutions to address the scenario of satellite signal loss. Solution 1: Integrate Additional Sensors On one hand, additional sensors can be integrated into the existing GNSS/INS system, such as odometers, LiDAR, astronomical sensors, and visual sensors. Thus, when satellite signal loss renders the GNSS unavailable, the newly added sensors can provide measurement information and form a new integrated system with the INS to suppress the accumulation of INS errors. The issues with this approach include increased system costs due to the additional sensors and potential design complexity if the new sensors require complex filtering models. Fig.1 System overview of the GNSS IMU ODO LiDAR SLAM integrated navigation system. Solution 2: ZUPT Technology On the other hand, a positioning model with motion state constraints can be established based on the motion characteristics of the vehicle. This method does not require adding new sensors to the existing integrated system, thus avoiding extra costs. When GNSS is unavailable, the new measurement information is provided by the motion state constraints to suppress the INS divergence. For example, when the vehicle is stationary, zero-velocity update (ZUPT) technology can be applied to suppress the accumulation of INS errors. ZUPT is a low-cost and commonly used method to mitigate INS divergence. When the vehicle is stationary, the vehicle’s speed should theoretically be zero. However, due to the accumulation of INS errors over time, the output speed is not zero, so the INS output speed can be used as a measurement of the speed error. Thus, based on the constraint that the vehicle’s speed is zero, a corresponding measurement equation can be established, providing measurement information for the integrated system and suppressing the accumulation of INS errors. Fig.2 The flowchart of the ZUPT-based GNSSIMU tightly coupled algorithm with CERAV. However, the application of ZUPT requires the vehicle to be stationary, making it a static zero-velocity update technology that cannot provide measurement information during normal vehicle maneuvers. In practical applications, this requires the vehicle to frequently stop from a moving state, reducing its maneuverability. Additionally, ZUPT requires accurate detection of the vehicle’s stationary moments. If detection fails, incorrect measurement information may be provided, potentially leading to the failure of this method and even causing the integrated system’s accuracy to decline or diverge. Conclusion The loss of satellite signals can cause rapid error accumulation in the INS, particularly in complex environments like urban areas. Two main solutions are presented: adding additional sensors, such as LiDAR or visual sensors, to provide alternative measurements, or using motion state constraints like Zero-Velocity Update (ZUPT) technology to correct INS errors. Each approach has its own advantages and challenges, with sensor integration increasing costs and complexity, while ZUPT requires the vehicle to be stationary and accurately detected to be effective. Micro-Magic Inc is at the forefront of inertial navigation technology and has recently introduced three GNSS-aided MEMS INS products with varying levels of accuracy ( industrial level,tactical level, and Navigation level). Notably, the Industrial level MEMS GNSS/INS I3500 features a 2.5°/hr bias instability and a 0.028°/√hr angular random walk, along with a high-precision MEMS accelerometer with a large range (±6g, zero bias instability <30μg). I3500 High Accuracy 3-Axis Mems Gyro I3500 Inertial Navigation System   I3700 High Accuracy Agricultural Gps Tracker Module Consumption Inertial Navigation System Mtk Rtk Gnss Rtk Antenna Rtk Algorithm    

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