Applications

Quickly get the product information in one minute   Attitude and Heading Reference System (AHRS) is a key navigation device that uses multi-sensor data fusion to real-time calculate the three-dimensional attitude (pitch angle, roll angle) and heading angle of a carrier. Its core technologies involve fields such as microelectromechanical systems (MEMS), inertial navigation, signal processing, and nonlinear optimization. This article will explore technical aspects from three dimensions: mathematical models, algorithm implementation, and error compensation.   Principles and kinematic equations of AHRS   The core principle of AHRS is multi-sensor data fusion, which compensates for the limitations of a single sensor through complementary sensors.   1. Sensor composition:   a. Gyroscope: It measures angular velocity using the Coriolis effect and integrates it to obtain attitude changes, but there is zero bias drift (accumulated error over time). b. Accelerometer: measures specific force (gravitational acceleration+motion acceleration) and can be used for attitude calibration (roll, pitch) at static or constant speed. c. Magnetometer: measures the direction of the geomagnetic field, provides absolute heading (yaw angle), but is susceptible to hard/soft magnetic interference. d. Optional GPS: Assist in correcting position and velocity errors.   2. Kinematic equations   Differential equation for carrier angular velocity and attitude update:                           Among them, represents quaternion multiplication and is the angular velocity measured by the gyroscope (in rad/s)   The core architecture and algorithm of AHRS   The core challenge of AHRS lies in how to integrate data from gyroscopes (with excellent dynamic response but drift), accelerometers (with high static accuracy but subject to motion interference), and magnetometers (providing absolute heading but susceptible to interference). The mainstream algorithms are as follows:   1. Kalman Filter   Based on the state space model, the attitude is iteratively estimated through prediction (gyroscope integration) and update (accelerometer/magnetometer observation). The construction of the state vector is as follows, including attitude error angle  and gyroscope bias .   The residual of gravity vector measured by accelerometer and geomagnetic field measured by magnetometer are used as observation values, and the following observation equation is constructed: In covariance tuning, the noise covariance  of the accelerometer is usually set to , and the noise covariance  of the magnetometer is set to .   2. Complementary Filter Algorithm   Weighted fusion of high-frequency gyroscope data and low-frequency accelerometer/magnetometer data. Its advantage is that it has a small computational load and is suitable for embedded systems; The disadvantage is that parameter tuning relies on experience and has limited dynamic performance. The high-frequency part uses gyroscope integration, and low frequency calibration using accelerometers/magnetometers:   Time constant , usually takes   3. Gradient descent optimization algorithm   There are two main gradient descent optimization algorithms. The Mahony algorithm is based on quaternion nonlinear complementary filtering and corrects gyroscope bias through a PI controller; The Madgwick algorithm optimizes quaternions directly by minimizing the error function between sensor measurements and predictions, resulting in high computational efficiency and suitability for low-power scenarios.   Among them,  is the convergence rate factor, with typical values ranging from 0.1 ~ 0.5 .   Challenges and Countermeasures of AHRS Engineering Implementation   1. Sensor error and calibration   The zero bias of the gyroscope needs to be estimated and compensated online (such as through static state initialization); Motion acceleration can disrupt the measurement of gravity direction, therefore, dynamic interference from accelerometers needs to be detected through high pass filtering or motion state detection; The influence of temperature changes on gyroscopes and accelerometers needs to be corrected by establishing a temperature compensation model; The interference of magnetometer requires hard/soft magnetic calibration (ellipse fitting or calibration field based algorithm).   2. Dynamic environmental adaptability   High frequency vibration causes an increase in accelerometer noise, requiring mechanical isolation or digital filtering. When performing rapid maneuvers (such as drone rolling), the accelerometer fails and a pure gyroscope needs to work for a short period of time.   3. Real time performance and computing resources   High dynamic scenarios require algorithms to complete iterations in milliseconds (such as drone control cycles <10ms). Embedded platforms such as STM32 require optimization of floating-point operations or adoption of fixed-point number processing.   4. Multi sensor synchronization and latency   The collection of sensor data requires strict time synchronization, otherwise the fusion error will increase. The transmission delay of communication interfaces (such as SPI/I2C) needs to be compensated.   5. Initial alignment and robustness   The system needs to converge quickly during startup (such as by initializing the accelerometer/magnetometer in a stationary state). The system design requires robust design against outliers (such as instantaneous interference from magnetometers).   Future development direction   a. Deep learning assisted fusion: using neural networks to model complex errors and nonlinear characteristics. b. Multi source fusion enhancement: Combining vision (VIO), GNSS, or barometer to improve reliability in complex environments. c. Progress in MEMS technology: Higher precision low-noise gyroscopes (such as MEMS optical gyroscopes) will reduce algorithm burden. d. Edge computing optimization: algorithm lightweight for embedded AI chips (such as ARM Cortex-M7).   Conclusion   The technological evolution of AHRS is essentially a deep interweaving of mathematics, physics, and engineering practice. From real-time solving of quaternion differential equations to noise suppression of MEMS sensors, every technical detail directly affects the final performance of the system. With the improvement of edge computing capability and the practicality of high-precision sensors, the next generation of AHRS will achieve nanometer level angular vibration perception and fully autonomous anti-interference capability, giving unmanned systems space cognitive accuracy beyond human beings. A5500 Whatever you needs, Micro-Magic is at your side. U6488 Whatever you needs, Micro-Magic is at your side. A5000 Whatever you needs, Micro-Magic is at your side.  

The main reason for choosing dual power supplies for sensors is to meet the requirements of signal integrity, circuit operating conditions, and measurement accuracy. This includes the following aspects: 1. Handling bidirectional signals (AC signals) Some sensors (such as vibration, sound, and acceleration sensors) output signals that are bidirectional, meaning they fluctuate above and below the zero reference point (positive and negative voltages). If only a single power supply is used, the negative half-cycle signal will be truncated, resulting in distortion. At this time, using positive and negative power supplies (such as ±5V, ±12V) to provide symmetrical voltages enables the signal to fluctuate centered at 0V, retaining the complete information of the positive and negative halves to avoid signal distortion. 2. Internal circuit operation requirements Many sensors integrate operational amplifiers or analog circuits for signal amplification or processing within their internal circuits. These devices require positive and negative power supplies to achieve: Simplified circuit design: In a single power supply system, when processing AC signals, a "virtual ground" needs to be set, which introduces additional noise and design complexity. While positive and negative power supplies naturally use "ground" (0V) as the reference point, without the need for additional bias circuits, this simplifies the design and reduces noise. Maximizing dynamic range: Many high-performance sensors and operational amplifiers require dual power supplies to fully utilize their input/output range, avoid non-linear distortion, and improve the linearity and dynamic range of the system. 3. Anti-interference and noise suppression The dual power supply design can reduce common-mode noise interference. Common-mode noise refers to the voltage difference between two signal lines to ground. Using positive and negative power supplies can effectively reduce this voltage difference, thereby reducing common-mode noise interference, especially in industrial environments, symmetrical power supply helps to improve the signal's signal-to-noise ratio.

Quickly get the product information in one minute In today's booming development of autonomous driving, drones, and robotics technology, high-precision and highly reliable navigation systems have become the core "brain" of intelligent devices. The MEMS inertial technology combined with dual antenna satellite navigation system I3700 launched by Micro-Magic Inc utilizes high-precision MEMS gyroscopes, accelerometers, and multi-mode multi frequency GNSS receivers to achieve fast and high-precision orientation and integrated navigation functions. It real-time calculates the position, heading, attitude, velocity, and other information of the carrier, resists obstruction and multipath interference, and achieves long-term, high-precision, and high reliability navigation in mountainous tunnels, urban canyon environments, automobiles, high-speed railways, and other areas. Support GNSS real-time RTK function, provide standardized user universal protocol, and have good scalability. Provide convenient navigation solutions for multi domain applications in all scenarios. At the same time, the I3700 has passed IP68 protection and CE certification, and can still work normally in harsh environments, meeting the requirements of industrial grade reliable design.   Technical features and highlights of I3700 1. The structural layout of array MEMS-IMU improves the accuracy of attitude calculation To solve the problem of large errors in attitude calculation, an attitude calculation system based on array MEMS-IMU data fusion is proposed. The multi-information vector optimization method is used to fuse the data of the array MEMS-IMU, and the dynamic Kalman filtering method is used to calculate the attitude of the moving carrier. Improve attitude calculation capability to a higher level of accuracy in complex dynamic movements. 2. Support the reception and calculation of GNSS signals across the entire system and frequency band Built in dual antenna positioning and directional GNSS module, supporting all major satellite navigation systems worldwide, capable of high-precision and fast positioning and orientation. Simultaneously compatible with all frequency points, ensuring optimal positioning performance at any location worldwide. The built-in satellite receiver has strong anti-interference ability and can still provide accurate positioning in complex electromagnetic environments. 3. High performance integrated navigation Inertial + GNSS navigation technology combined with self-developed high-precision integrated navigation algorithm achieves a horizontal positioning accuracy of 0.8cm+1ppm and a heading accuracy of 0.2°. Even if GNSS signals are temporarily lost (such as in tunnels or underground garages), stable navigation data can still be continuously output through inertial sensors (gyroscope zero bias stability of 2.5°/h, acceleration zero bias of 30μg). At the same time, multi-source data fusion algorithms support external odometry, DVL and other auxiliary sensors to enhance localization robustness in complex environments. 4. Flexible and easy-to-use interface ecosystem Full interface coverage supports RS232/422, CAN (SAE J1939), PPS synchronization signals, compatible with 4G DTU differential access, easy to interface with external devices such as LiDAR and cameras. Output NMEA, RTCM, binary protocols, and J1939 standard messages, with diverse protocols to meet industrial and vehicle communication needs. 5. Rapid deployment and intelligent configuration The plug and play (PnP) mode makes usage more flexible, with default configurations adapted to mainstream scenarios and support for one click setting of working modes (onboard, shipborne, aircraft, base station). A user-friendly development interface that supports real-time data recording and visual debugging, shortening the integrated development cycle   Application scenario: Accurately empowering thousands of industries 1. Drones and unmanned vehicles: In logistics distribution and inspection operations, the I3700 provides centimeter level positioning and stable attitude output to ensure precise control in complex paths.    2. Smart agriculture: combined with agricultural machinery auto drive system, the navigation error between ridges is less than 2cm, greatly improving the efficiency of sowing and fertilization.    3. Ocean exploration and unmanned ships: IP68 protection design to cope with humid salt spray environments, dual antenna directional function to ensure ship heading accuracy, and assist in hydrological surveying and scientific research tasks.    4. Industrial robots: In the scenarios of warehouse AGV and factory handling, millimeter level motion trajectory tracking is achieved by coordinating with external devices through synchronous input and output functions.   I3700 provides multiple interface versions (MI0/MI1/MI2) to meet the needs of different industries. Standard development kit and detailed technical documentation, supporting customized services. It can connect to CORS stations such as Qian Xun and Mobile, or build its own RTK reference station to achieve high-precision service coverage nationwide.   The I3700 accurately measures the world and controls the future with reliability. Whether you are a developer chasing the forefront of technology or a practitioner deeply involved in the industry, the I3700 will become your reliable partner for exploring intelligent navigation.   I3700 Whatever you needs, Micro-Magic is at your side.  

1. Working Principle and Types of Acceleration Sensors An acceleration sensor is a common type of sensor that can measure the acceleration and tilt angle of objects, and is widely used in industries, healthcare, sports, and other fields. Acceleration sensors typically consist of sensing elements, signal processing circuits, and interface circuits, and can sense the acceleration of an object or detect changes in the motion state, converting these data into electrical signals for output. Currently, the market offers two main types of acceleration sensors: analog sensors and digital sensors.The characteristic of acceleration digital sensors is their ease of integration with digital systems. However, sometimes to achieve higher accuracy, lower noise, and to meet different dynamic response requirements, while also controlling costs, we tend to prefer analog output acceleration sensors.Figure 1 shows the processing flow of the signal from a typical analog output accelerometer. Generally, to improve integration and reduce costs, the ADC sampling and digital filtering processing are integrated into the MCU or DSP internally.     2. Design of the Pre-ADC Anti-aliasing Low-pass Filter Adding an anti-aliasing low-pass filter at the front end of the ADC (analog-to-digital converter) is a crucial design in the signal sampling system. According to the Nyquist sampling theorem, the ADC sampling frequency fs must be at least twice the highest frequency fmax of the signal (i.e., fs ≥ 2fmax) to accurately reproduce the original signal without distortion. If the input signal contains components with frequencies exceeding fs/2 (referred to as the Nyquist frequency), these high-frequency components will be "folded" into the low-frequency range, forming false signals (aliasing). Aliasing permanently contaminates the useful signal and cannot be eliminated through subsequent processing. The actual signal may contain noise or useless high-frequency components (such as electromagnetic interference, harmonics), which may exceed fs/2, and even if the input signal itself has a limited bandwidth, the sampling process of the ADC (especially discretization) will introduce quantization noise. The anti-aliasing low-pass filter can reduce the impact of high-frequency noise. The anti-aliasing low-pass filter is actually an RC (resistor-capacitor) low-pass filter, and its cutoff frequency fc is usually set slightly lower than fs/2 but slightly higher than the effective bandwidth of the signal (such as the bandwidth of an accelerometer of 100 Hz, fc is selected as 150 Hz), ensuring that only signals with frequencies below the Nyquist frequency pass through. If the ADC sampling clock frequency is 2 KHz, then the cutoff frequency fc should be set not higher than 1 KHz. The formula for calculating the cutoff frequency fc is fc = 1/(2π×R×C). Using the anti-aliasing low-pass filter to reduce background noise and thereby improve the resolution of the accelerometer. Generally, in the design, the bandwidth is limited to the lowest frequency required by the application to maximize the resolution and dynamic range of the accelerometer.In practical use, if ADI's ADXL103 or ADXL203 is used, the internal low-pass filter is already integrated. The cutoff frequency (-3dB point) is determined by the external capacitor C connected to the output terminal, and only by connecting a capacitor in parallel to the output pin and forming a low-pass filter with the internal output resistor can the anti-aliasing and noise suppression functions be achieved. The actual application circuit is shown in Figure 2, and its corresponding fc = 5 µF/C.     3. Digital Filtering The analog signal passes through an anti-aliasing low-pass filter and is sent to the ADC module. Under the drive of the sampling clock, a continuous data stream is generated. At this time, the data inevitably still contains noise. To filter out the noise, digital filtering technology needs to be adopted to process the obtained data. Compared with analog filters, digital filters usually have more stable frequency responses, can precisely suppress out-of-band signals, have good repeatability, and can be implemented either in pure software or by using hardware acceleration of FIR (Finite Impulse Response) or IIR (Infinite Impulse Response) filters. Digital filtering can be either purely software-based or implemented using hardware acceleration of FIR or IIR.Due to the high order of FIR filters, they consume more computing resources and are more suitable for running on DSP or high-performance MCUs. Their difference equation expression is shown in the following figure.    For a 120th-order low-pass FIR filter using a Kaiser window, the stopband attenuation usually reaches over 60dB. Compared to IIR filters, FIR filters have a wider transition band and larger group delay, and their real-time response is not as fast as IIR filters.If the computing resources are limited on an embedded platform or to obtain a steeper transition band, IIR filters can also be used. Compared to FIR filters, IIR filters have higher computational efficiency (can achieve high performance with lower orders), have nonlinear phase changes, and are acceptable for accelerometers, but they may be unstable. The pole positions need to be carefully optimized. The general expression of the difference equation for IIR filters is as shown in the following figure.    A 4th-order elliptic IIR low-pass digital filter usually can achieve a stopband attenuation of over 60dB when the passband ripple is 0.5dB.Sometimes, in order to obtain a smooth output result, the output data within a certain window of the aforementioned filter is subjected to recursive average filtering to reduce the influence of noise. Given a signal sequence x[n] containing N samples, where n is the index of the sample (from 0 to N-1). Moving average filtering is performed by sliding a fixed-length window of length M over the signal sequence and calculating the average of the samples within the window. For each position k of the sliding window, the filtered output y[k] can be calculated using the following formula:   The size M of the sliding window determines the degree of smoothing. A larger window can more effectively smooth the signal, but it may result in a delayed response; a smaller window can respond to changes in the signal more quickly, but the smoothing effect may be poorer. Usually, when the ADC sampling clock is 2000Hz, M is set to 10.

Quickly get the product information in one minute In the rapidly changing technological era, precise inertial navigation technology has become a core requirement in fields such as autonomous driving, aerospace, and ocean exploration. The G-F120H high-precision fiber optic gyroscope launched by Micro Magic Inc, with its outstanding performance and reliability, provides stable and accurate navigation solutions for global users, empowering industries to ascend to new heights of intelligence. The G-F120H high-precision fiber optic gyroscope has become a leader in navigation grade applications through the integrated design of optical and electronic components. Taking into account the weight and size of the product, it exhibits excellent inertial performance. By adopting advanced integrated optical technology and FPGA closed-loop electronic circuits, G-F120H achieves better accuracy, noise control, and efficiency than similar technologies. In addition, the internal calibration function of the product further optimizes the thermal suppression effect, while the separation design between electronic devices and FOG sensitive ring components ensures wide environmental adaptability.   Core advantage: Breaking through technological boundaries 1. Ultimate precision, steadfast reliability Zero bias stability is as low as 0.002 °/h (1σ, 100s), ensuring high consistency of attitude measurement during long-term operation. Full temperature zero bias repeatability ≤0.05°/h, operating in extreme environments (-40℃ to +65℃) with always-on performance. Random walk coefficient ≤0.001°/√ hr, effectively suppressing noise interference and resulting in purer data output. 2. Rugged durability, fearlessly conquering challenges. Through rigorous mechanical testing, ensure the accuracy and reliability of product operation in complex environments. Random vibration (20Hz~500Hz, each axis vibrates for 15 minutes), the absolute value of the zero-bias value during vibration and the average zero bias value before and after vibration is less than 0.05 °/h. 30g mechanical impact (half sine wave, 10ms), the zero-position change value before and after impact is less than 0.02°/h, ensuring the reliability of high dynamic scenarios such as onboard and airborne. 3. High speed communication, seamless integration Supports RS-422 bidirectional serial communication, with a transmission rate of up to 460.8kbps and strong compatibility. The data frame contains 32-bit valid gyroscope data and 14-bit temperature data, combined with parity check to ensure the integrity and real-time transmission of information.   Application scenarios empower diverse fields (1) Drones and Aviation: Provide sub-degree accuracy (0.05°) for onboard heading and attitude systems to ensure flight safety and stability.    (2) Ocean navigation: The ideal choice for marine gyrocompasses, with anti-magnetic interference (magnetic field sensitivity ≤ 0.02°/h/Gs) and adaptability to complex marine environments.    (3) Industrial automation: precise positioning (1cm accuracy) and speed measurement (0.03m/s accuracy), empowering high-precision motion control for AGVs, robots, and others.   Quality assurance: defined by technical standards The G-F120H series strictly follows international technical specifications and offers three models, G-F120H-A/B/C, to meet the accuracy requirements of different scenarios: (1) G-F120H-C: Flagship model with optimal zero bias stability and full temperature repeatability, suitable for aerospace grade high-precision missions.    (2) G-F120H-B: Balancing performance and cost, suitable for mid to high end markets such as automotive and marine applications.    (3) G-F120H-A: Economical choice, performance still exceeds industry benchmarks, covering industrial automation needs.   Choose Micro Magic, Unlock a Precision-Driven Future As a leading global provider of inertial navigation solutions, Micro Magic Inc is driven by innovation and continues to push the limits of technology. The G-F120H fiber optic gyroscope is not only a device, but also a reliable partner for you to move towards the era of intelligence.   G-F120 Whatever you needs, Micro-Magic is at your side.   --

The voltage output type sensor typically outputs a voltage range of 0-5V or 0-10V. If we choose not to use an external ADC conversion chip due to cost considerations but instead sample the ADC module within the MCU chip, but as the voltage range of the AD acquisition in STM32 is 0-3.3V, in this case, we need the sampling circuit as shown in the figure below. In the figure, R1 and R2 form a resistor voltage divider circuit, which converts the input voltage ranging from 0 to 5V into a voltage range of approximately 0 to 3V. The subsequent rail-to-rail operational amplifier voltage follower plays the role of impedance matching, isolating the sensor from the ADC sampling module and reducing signal attenuation. To prevent damage to the subsequent ADC module circuit caused by overvoltage and negative voltage, clamp protection diodes are added to the power supply and ground respectively, ensuring that the input voltage of the ADC module is always within the range of -0.7V to 3.3V + 0.7V. At the same time, to suppress the influence of high-frequency noise, an RC low-pass filter needs to be added before ADC sampling. The cutoff frequency of the low-pass filter should be selected according to the bandwidth of the signal. For example, if the signal bandwidth is 100Hz, the cutoff frequency can be set to 100Hz or slightly higher, such as 1kHz. If R2 is 1.5K and C1 is 100nF, then the cutoff frequency fc is approximately equal to 1KHz.  

If the communication between the sensor module and the user system is carried out using UART, SPI or IO methods, generally, the interface voltage level of the sensor module is 3.3V, and the voltage level of the user system is also 3.3V. Usually, a direct connection method is feasible. However, if the interface voltage level of the user system is 1.8V or 5V, when the module interacts with the single-chip microcomputer system for data exchange, due to the mismatch of the voltage levels of the two communicating parties, it may lead to communication failure, current backflow, abnormal power consumption, voltage abnormality and other problems. This article will introduce several common level matching methods, and users can choose specifically according to the actual situation. 1. Use level conversion chips Supply the two required conversion power supplies to the two sides of the conversion chip, and then connect the required input and output signals of the conversion to the input and output of the chip. All conversion parts are completed by the chip internally. The following figure shows the level conversion circuit using SN74LVC2T45DCTR. The advantages of this scheme are that it is very fast, has strong driving capability, and is easy to use. The disadvantages are that the cost is relatively high. 2. Conversion of levels using MOSFETs or transistors As shown in the figure below, this is a bidirectional level conversion circuit. First, let's analyze the situation where data is sent from 3.3V to 5V. When the UART1_TX terminal is at a high voltage, the MOSFET Q1 is in the cut-off state, and the UART2_RX terminal is pulled up to its power supply voltage. When the UART1_TX terminal is at a low voltage, the MOSFET Q1 conducts, and the UART2_RX terminal is pulled down to a low voltage level by Q1, completing the level conversion. Second, let's analyze the situation where data is sent from 5V to 3.3V. When the UART2_TX terminal is at a high voltage, both the MOSFET Q2 and the body diode are in the cut-off state, and the UART1_RX is pulled up to a high voltage by R3. When the UART2_TX outputs a low voltage, the MOSFET does not conduct, but the body diode of the MOSFET pulls the UART1_RX down to a low voltage level. At this point, Vgs is greater than the turn-on voltage, and the MOSFET conducts, further lowering the voltage of UART1_RX. MOSFETs can also be replaced with transistors. The advantage of this solution is its low cost, while the disadvantage is that the baud rate of the data generally cannot exceed 400 kbps. 3. Using resistors for voltage division to convert levels This solution only uses one type of component - resistors, as shown in the figure below. When the 3.3V level module sends data to the right, it only passes through the current-limiting resistor, and the level at the receiving end of the client is within the range. When the 5V level client sends data to the left, it uses two resistors for voltage division, and the voltage at the receiving end on the left is 5V * 2K / (1K + 2K) ≈ 3.3V. The advantage of this solution is that it has extremely low cost and is convenient for PCB board layout. The disadvantage is that it has weak driving capability and cannot achieve very high speed. Generally, the baud rate applied in this way does not exceed 100 kbps.

Quickly get the product information in one minute   In the field of industrial automation and intelligent monitoring, accurate data collection and reliable equipment status analysis are key to ensuring production safety and efficiency. The ACM1000 digital MEMS vibration sensor launched by Micro-Magic Inc has become an ideal choice in the field of vibration monitoring due to its high precision, multi parameter output, and strong environmental adaptability.   ACM1000 adopts a low-noise, low drift, and low-power three-axis MEMS sensor with high-frequency and low-noise characteristics, which is particularly suitable for high-resolution vibration measurement applications. It can detect machine faults as early as possible in equipment status monitoring applications. The product not only has excellent performance, but also has extremely low power consumption. In addition, this sensor can provide accurate and reliable tilt measurement in high impact and high vibration environments without causing sensor saturation.   The highlights of the ACM1000 product include the following aspects: 1. Multi parameter integrated measurement The ACM1000 can simultaneously output vibration velocity (0-50mm/s), vibration angle (0-180°), amplitude (displacement 0-30mm), vibration frequency (1-100Hz), and temperature data of three axes (X, Y, Z), fully covering the monitoring requirements of equipment vibration status. 2. High precision and low noise By using digital filtering technology and monocrystalline silicon capacitive sensors, noise interference is effectively reduced, and the measurement accuracy reaches:  Vibration speed: ±1mm/s; Vibration angle: ±0.001°/s; Vibration displacement: ± 0.001mm. 3. Industrial grade durable design Wide temperature range (-40℃~+85℃), suitable for extreme environments. Resistant to 20000g of impact and 10grms of vibration, meeting the requirements of harsh industrial scenarios. Mean time between failures≥45000 hours to ensure stable and reliable system operation. 4. Flexible configuration and easy integration Supports multiple interfaces such as RS232/RS485/TTL/RS422/CAN, compatible with Modbus protocol. Address codes (0x01~0xFF) can be set, supporting multi-sensor networking and achieving multi-point monitoring. Built in magnetic base and screw mounting holes for easy deployment.   Accurately monitor every vibration, prevent problems before they occur, and help upgrade industrial intelligence. Choose the ACM1000 to equip your equipment with the "Smart Eye"!     ACM-1000 Whatever you needs, Micro-Magic is at your side.   --

In industries where accuracy, reliability and adaptability to high temperature and vibration-resistant environments are critical, the AC-6 series high-performance quartz flexure accelerometers from Micro-Magic Inc are undoubtedly a disruptive product. Designed specifically for the most challenging high temperature and vibration-resistant environments, this advanced accelerometer is an ideal choice for oil and gas drilling, geophysical exploration and other fields with its unparalleled accuracy and stability. The AC-6 quartz flexible accelerometer product adopts unique miniaturization, high temperature and vibration resistant design, advanced packaging technology and dedicated circuit. Users can select the appropriate sampling resistor through calculation to achieve high-precision output. And according to user requirements, built-in temperature sensors are used to compensate for local values and scaling factors, reducing the impact of environmental temperature.   How does AC-6 solve the problems of stability and reliability in high temperature environments? In order to solve the problem of low working life of the accelerometer probe under high temperature, the connection process is improved by using gold wire bonding to connect the terminal post to the gold-plated film on the quartz pendulum. A multi-chip module thick-film hybrid integrated circuit process is adopted for the servo circuit. The substrate used is aluminum oxide ceramic and the conductor is metal gold, which solves the problem of long-term stability under high temperature and has a small error. In the test phase, in order to meet the high-temperature working environment of petroleum logging, the accelerometer underwent high-temperature aging at 180°C for more than 96 hours to ensure long-term stability during high-temperature operation.     AC-6 also has the following significant characteristics: Ø Both static and dynamic testing can achieve high-precision measurement, with bias stability (1σ, one month)≤150μg and resolution as low as 30μg. Ø The measurement range reaches ±30g, meeting the acceleration measurement requirements in extreme environments. Bandwidth of 800~2500Hz, suitable for high-frequency dynamic measurement scenarios. Ø The anti-vibration ability reaches 25G (20~2000Hz), and the anti-impact ability is 1000g (0.5ms half sine wave), ensuring stable operation under harsh conditions.   The AC-6 quartz accelerometer has become a benchmark product in the field of industrial measurement due to its high precision, high reliability, and excellent adaptability to high temperature and harsh environments. Its mature technology and wide range of applications have validated its outstanding performance. Whether it is static testing in extreme environments or high-frequency dynamic testing, AC-6 can provide users with reliable solutions.   

1. RS422 communication principle: (1) RS422 working mode: RS422 supports full-duplex communication mode, that is, data can be transmitted bidirectionally at the same time, that is, two pairs of differential lines are used for sending and receiving respectively, which improves the efficiency and flexibility of communication. (2) Signal level: RS422 uses differential signal to transmit data, that is, two signal lines (one is a positive signal line and the other is a negative signal line) are used to transmit data. It has strong anti-interference ability and can achieve long-distance and high-speed communication. Its differential signal level standard is as follows: ① Transmitter: When transmitting logic "1", the voltage difference between line A and line B is +2V to +6V; when transmitting logic "0", the voltage difference between line A and line B is -2V to -6V. ② Receiving end: It can recognize differential voltages as low as ±200mV. When the voltage of line A is higher than the voltage of line B by more than 200mV, it is recognized as logic "1"; when the voltage of line A is lower than the voltage of line B by more than 200mV, it is recognized as logic "0". (3) Transmission distance: At 115200 baud rate, the maximum transmission distance of RS485 is usually about 1200 meters. Usually the baud rate is inversely proportional to the transmission distance, but the actual distance may vary due to the following factors: ① Transmission line quality: High-quality shielded twisted pair can reduce signal attenuation and interference and extend the transmission distance; ② Electromagnetic environment interference: An environment with strong electromagnetic interference will shorten the transmission distance; ③ Terminal matching resistance: Correctly installing the terminal resistance (usually 120 ohms) can reduce signal reflection, ensure signal integrity, and improve communication quality. If a longer distance is required, consider reducing the baud rate or using a repeater. (4) Load capacity of the transmitter: One RS422 driver can drive up to 10 receivers. (5) Connection method of terminal matching resistor: RS422 can be connected without terminal matching resistor when the distance is short (generally not more than 300 meters). When communicating over long distances, a 120 ohm resistor can be connected at the end of the signal receiving end as the terminal resistor. The terminal resistor can absorb the reflected waves on the network and effectively enhance the signal strength.   (6) Protection of RS422 circuit and suppression of interference: Generally, anti-static (ESD) protection, current limiting protection and suppression of common mode noise are required. The electrical principle is shown in the figure below:   Anti-static (ESD) protection: Connect TVS diodes to the ground on the A and B differential lines respectively to prevent static crosstalk to the subsequent circuit and damage to components. Current limiting protection: Connect a small resistance resistor in series on the A and B differential lines to prevent the signal line from short-circuiting or overcurrent from damaging the interface chip. Suppression of common mode noise: Connect common mode chokes L1 and L2 in series on the A and B differential lines to suppress common mode interference on the line and improve the anti-interference ability of the system. The common mode inductor impedance selection range is 120Ω/100MHz~2200Ω/100MHz, and the typical value is 1000Ω/100MHz. The sending and receiving A and B differential lines are connected to the ground and connected to capacitors to provide a low-impedance return path for interference to suppress common-mode high-frequency noise. The capacitance value selection range is 22PF~1000pF, and the typical value is 100pF. 2. Precautions for on-site use: (1) RS422 signal lines cannot be routed together with strong power lines, and the principle of separation of strong and weak electricity must be followed. (2) The correct connection method of signal lines and ground lines, the A and B lines of the sending end are connected to the A and B lines of the receiving end, and the ground line must also be connected. The signal ground can be an additional unshielded twisted pair or the shielding layer of a shielded twisted pair. The RS422 bus must be reliably grounded at a single point, that is, there can only be one point grounded on the entire RS422 bus, not multiple points, because the reason for grounding is to keep the voltage on the ground wire (usually the shielded wire is used as the ground wire) consistent to prevent common mode interference. If multiple points are grounded, it will be counterproductive. (3) Signal interference causes unstable communication and solutions: If there is sometimes no communication connection on site, this may be that the signal is interfered with, thus affecting the continuity of the signal. This requires the use of shielded twisted pair cables for long-distance transmission, and a 120-ohm impedance matching resistor is connected in parallel at the receiving terminal. (4) Selection of shielded cable: Choose to use ordinary Category 5e shielded twisted pair cables, i.e. network cables.   3. Common problems and solutions of RS422 communication:   Problem Possible cause Solution Communication failure Wrong line connection Ensure A+ and B- are correctly connected   Data loss Transmission distance is too long or rate is too high Reduce baud rate or use relay amplifier Severe interference Electromagnetic interference Use shielded twisted pair and ensure good grounding   Signal reflection No terminal matching Add 120Ω terminal resistor at the end of the bus Multi-device communication abnormality Exceeding the maximum number of receiving devices Reduce the number of receiving terminals or use RS-485 instead

Wheel speed sensors (wheel speed sensors) are often used as auxiliary sensors in inertial navigation systems (INS) to improve navigation accuracy and suppress accumulated errors of inertial sensors. The wheel speed gauge calculates the longitudinal speed of the vehicle by measuring the wheel speed (combined with tire radius and slip ratio correction), providing independent speed information. When GPS signals are lost (such as in tunnels or underground garages), INS can continuously estimate the vehicle's position through dead reckoning by combining the speed data of the wheel speed meter, and compare it with the accelerometer integration results of INS to correct the speed error. In heading calculation, the wheel speed gauge can indirectly calculate the rate of change of heading angle by measuring the speed difference between the left and right wheels (wheel speed difference), providing compensation for the calibration of inertial navigation heading angle. Taking the change of heading angle as an example, this article briefly introduces how to indirectly calculate the rate of change of heading angle through the data obtained from the wheel speed meter.   1.  Basic principles   Assuming that the vehicle has a rigid body and only moves in a horizontal plane (ignoring pitch and roll), there is no lateral slip at the contact point between the tires and the ground (only considering longitudinal rolling), and the left and right wheelbase (wheel spacing) is a fixed value. The relationship between wheel speed and linear speed is shown in the following equation:       When the vehicle turns, the left and right wheels move around the same instantaneous center of rotation (ICR), The vehicle angular velocity   is the rate of change in heading angle. The difference in linear velocity between the left and right wheels is generated by the rotation of the vehicle around the ICR, satisfying the following relationship:   where：  : the wheel spacing ，  : the turning radius Therefore, there is a difference in linear velocity between the left and right wheels：    The rate of change in heading angle (angular velocity) is:                           2.  Factors affecting the measurement accuracy of wheel speed gauge and algorithm correction   In practical applications, the following issues may lead to inaccurate measurement data of the wheel speed gauge, affecting the error compensation of the azimuth angle in INS. a)   Track B The actual wheelbase may vary due to load or suspension deformation and requires regular calibration. The above formula assumes that the left and right wheels are symmetrical, so the model needs to be adjusted for asymmetric vehicles. b)   Slip and error compensation In wet and slippery road surfaces or off-road environments, or when the vehicle accelerates or brakes rapidly, it may cause tire slippage, resulting in a wheel speed difference that does not match the true angular velocity. Therefore, it is necessary to combine the acceleration and angular velocity data of IMU (Inertial Measurement Unit) to detect the slip state; Dynamically adjust the weight of the wheel speed gauge through multi-sensor fusion algorithm (Kalman filter algorithm) to reduce the impact of slip-on navigation.   3.   Actual use cases   I3500 is an integrated navigation system (GNSS/INS) produced by Micro-Magic Inc, consisting of high-performance MEMS sensors, high-precision GNSS systems, and high-performance microprocessors. Can be connected to an external odometer, DVL and other auxiliary navigation information, with built-in high reliability integrated navigation algorithms, can output real-time information such as speed, position, and attitude of the carrier.     Data input/output Parameters Describe Data Output NMEA/RTCM/Novtel SPAN Binary Protocol Data Content Euler angle, velocity, position, acceleration, angular velocity Fusion Algorithm Extended Kalman Filter External Sensor Mileage meter, GNSS, DTU Integrated navigation accuracy index Position Position Accuracy Velocity Accuracy Pitch/Roll Accuracy   Mileage Meter Access 1cm 0.03m/s 0.1° 1m 0.1m/s 0.1° 6m 0.1m/s 0.2°   Conclusion   The wheel speed gauge complements the inertial navigation system by providing independent speed information, significantly improving the navigation reliability of vehicles in complex environments. Its core values are reflected in: a. Short term accuracy: high-frequency speed data suppresses INS error accumulation. b. Redundant design: Maintain basic navigation capability in the event of GPS failure. c. Cost effectiveness: Significant improvement in navigation performance achieved at a lower cost. In the future, with the advancement of multi-sensor fusion algorithms such as deep learning assisted filtering, The application of wheel speed sensors in autonomous driving and unmanned systems will be further deepened. 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  

"An in-depth analysis of the testing methods for the bias (zero bias) and scale factor of quartz flexible accelerometers is provided, including specialized techniques such as four-point rolling test and two-point test, as well as the calculation formula for temperature sensitivity. This is applicable to high-precision applications such as inertial navigation and spacecraft." The bias (zero bias) and scale factor of quartz flexible accelerometers directly determine the measurement accuracy and long-term stability of the accelerometer, especially in high-precision application scenarios such as inertial navigation and attitude control. Therefore, they are two key performance indicators for evaluating quartz accelerometers.   The core significance of bias (zero bias) lies in its inherent system error of the accelerometer, which directly leads to the fundamental deviation of all measurement results. For example, if the zero bias is 1 mg, the measured value will add this error regardless of the actual acceleration. Zero bias will also drift with factors such as time, temperature, and vibration (zero bias stability). In inertial navigation systems, zero drift is continuously amplified through integration operations, resulting in cumulative errors in position and velocity. The temperature characteristics of quartz materials can also cause zero bias to change with temperature (zero bias temperature coefficient), so temperature compensation algorithms are needed to suppress this effect in high-precision applications. Scale factor refers to the proportional relationship between the output signal of an accelerometer and the actual input acceleration. The error in scale factor can directly lead to proportional distortion of the measurement results. The stability of scale factor directly affects system performance in high dynamic range or variable temperature environments. In the acceleration integration operation of inertial navigation, the scale factor error will be integrated twice, further amplifying the position error.   Therefore, the reason why bias and scale factor have become key performance indicators of quartz flexible accelerometers is that they are both fundamental error sources and key constraints on long-term stability. In system level applications, the performance of these two directly determines whether the accelerometer can meet the requirements of high precision and high reliability, especially in scenarios such as unmanned driving, spacecraft, submarine navigation, etc. where there is zero tolerance for errors   The bias test can be conducted through two methods: four point rolling test (0°，90°，180°，270°positions) or two-point test (90°，270°positions). The scale factor test can be conducted through three methods: four point rolling test (0°，90°，180°，270°positions), two-point test (90°，270°positions), and vibration test. Taking the four-point rolling test method as an example, this article explains how to obtain the bias and scale factor of an acceleration sensor.     1. Testing methods for bias and scaling factors:   a) Install the accelerometer on a specific test bench (multi tooth indexing head). b) Start the test bench c) Rotate the test bench clockwise to the 0°position, stabilize it, and record the output of multiple sets of tested products according to the specified sampling frequency. Take the arithmetic mean as the measurement result; d) Rotate the test bench clockwise to the 90°position, stabilize it, and record the output of multiple sets of tested products according to the specified sampling frequency. Take the arithmetic mean as the measurement result; e) Rotate the test bench clockwise to the 180°position, stabilize it, and record the output of multiple sets of tested products according to the specified sampling frequency. Take the arithmetic mean as the measurement result; f) Rotate the test bench clockwise to the 270°position, stabilize it, and record the output of multiple sets of tested products according to the specified sampling frequency. Take the arithmetic mean as the measurement result; g) Rotate the test bench clockwise to the 360°position, then counterclockwise to make the rotation angles at 270°, 180°, 90°, and 0°positions. After stabilization, record the output of multiple sets of tested products according to the specified sampling frequency, and take the arithmetic mean as the measurement result. h) Calculate the bias and scaling factor of the tested product using the following formula (1) and (2). K0 =    -------------------------------------- (1)   K1 =   -------------------------------------- (2)        Where:         K0 -------Bias         K1 -------Scale factor         -------The total average of forward and reverse readings at 0°position         -----The total average reading of forward and reverse rotation at 90°position         --- The total average reading of forward and reverse rotation at180° position         --- The total average of readings for forward and reverse rotation at 270°position   2. Test method for bias temperature sensitivity and scale factor temperature sensitivity a) Start the test bench b) Calculate the bias and scaling factors at each temperature point using the formulas (1) and formulas (2) at room temperature, the upper limit operating temperature specified by the accelerometer, and the lower limit temperature specified by the accelerometer. c) Calculate the temperature sensitivity of the accelerometer using the following formula (3) and (4):      ---------------------(3) where： ---- Bias temperature sensitivity ----Bias of upper limit temperature of sensor ----Bias of sensor room temperature -----Bias of the lower limit temperature of the sensor ------Upper limit temperature ------Room temperature -------Lower limit temperature        ---------------------(4) Where: ----Scale factor temperature sensitivity ------Scale factor ----Scale factor for the upper limit temperature of the sensor ----Scale factor of sensor room temperature -----Scale factor for the lower limit temperature of the sensor ------Upper limit temperature ------Room temperature -------Lower limit temperature AC-1 Quartz Flexible Accelerometer   AC-4 Quartz Flexible Accelerometer