How to Implement Kalman Filter for MPU6050 Sensor Fusion on STM32

Implementing a Kalman Filter for Sensor Fusion of MPU6050 Data on STM32

The Kalman filter is a powerful mathematical tool used for sensor fusion, allowing you to combine multiple sensor readings to produce more accurate estimates. This tutorial will guide you through the implementation of a Kalman filter to fuse data from the MPU6050 accelerometer and gyroscope on an STM32 microcontroller.

Prerequisites

• Basic understanding of C programming
• Familiarity with STM32 microcontrollers
• MPU6050 sensor module
• Development environment set up (e.g., STM32CubeIDE)
• Knowledge of I2C communication

Parts/Tools

• STM32 microcontroller (e.g., STM32F4, STM32F1)
• MPU6050 sensor module
• Connecting wires
• Computer with STM32CubeIDE installed
• Power supply (USB or battery)

Steps

1. Setup the hardware
   • Connect the MPU6050 to the STM32 using I2C:

     
     SDA (MPU6050) ↔ SDA (STM32)
     SCL (MPU6050) ↔ SCL (STM32)
     VCC (MPU6050) ↔ 3.3V (STM32)
     GND (MPU6050) ↔ GND (STM32)
                     

2. Initialize the STM32 project
   • Create a new project in STM32CubeIDE.
   • Enable I2C in the configuration settings.
   • Initialize necessary libraries (e.g., HAL for STM32).
3. Implement MPU6050 I2C Read Functions
   • Add functions to read raw accelerometer and gyroscope data:

     
     #define MPU6050_ADDR 0x68
     #define ACCEL_X_REG 0x3B
     #define GYRO_X_REG 0x43
     
     uint8_t readByte(uint8_t reg) {
         uint8_t data;
         HAL_I2C_Mem_Read(&hi2c1, MPU6050_ADDR << 1, reg, 1, &data, 1, HAL_MAX_DELAY);
         return data;
     }
     
     void readMPU6050(int16_t *ax, int16_t *ay, int16_t *az, int16_t *gx, int16_t *gy, int16_t *gz) {
         *ax = (readByte(ACCEL_X_REG) << 8) | readByte(ACCEL_X_REG + 1);
         *ay = (readByte(ACCEL_X_REG + 2) << 8) | readByte(ACCEL_X_REG + 3);
         *az = (readByte(ACCEL_X_REG + 4) << 8) | readByte(ACCEL_X_REG + 5);
         *gx = (readByte(GYRO_X_REG) << 8) | readByte(GYRO_X_REG + 1);
         *gy = (readByte(GYRO_X_REG + 2) << 8) | readByte(GYRO_X_REG + 3);
         *gz = (readByte(GYRO_X_REG + 4) << 8) | readByte(GYRO_X_REG + 5);
     }
                     

4. Implement the Kalman Filter
   • Define the Kalman filter structure and functions:

     
     typedef struct {
         float q; // Process noise covariance
         float r; // Measurement noise covariance
         float x; // Value
         float p; // Estimation error covariance
         float k; // Kalman gain
     } KalmanFilter;
     
     void KalmanInit(KalmanFilter *kf, float q, float r) {
         kf->q = q;
         kf->r = r;
         kf->x = 0; // Initial value
         kf->p = 1; // Initial error covariance
     }
     
     float KalmanUpdate(KalmanFilter *kf, float measurement) {
         // Prediction update
         kf->p += kf->q;
         
         // Measurement update
         kf->k = kf->p / (kf->p + kf->r);
         kf->x += kf->k * (measurement - kf->x);
         kf->p *= (1 - kf->k);
         
         return kf->x;
     }
                     

5. Integrate Sensor Data with Kalman Filter
   • Use the Kalman filter to process accelerometer and gyroscope data:

     
     KalmanFilter kf_accel, kf_gyro;
     KalmanInit(&kf_accel, 0.1, 1); // Adjust parameters as needed
     KalmanInit(&kf_gyro, 0.1, 1);
     
     int16_t ax, ay, az, gx, gy, gz;
     while (1) {
         readMPU6050(&ax, &ay, &az, &gx, &gy, &gz);
         float filtered_accel = KalmanUpdate(&kf_accel, ax);
         float filtered_gyro = KalmanUpdate(&kf_gyro, gx);
         // Use filtered_accel and filtered_gyro for further processing
     }
                     

Troubleshooting

• Sensor not responding: Check the I2C connections, ensure correct power supply, and verify the I2C address.
• Inconsistent data: Adjust the Kalman filter parameters (q and r) for better accuracy based on your application.
• Compilation errors: Ensure all necessary libraries and headers are included in your project.

Conclusion

Implementing a Kalman filter for sensor fusion on an STM32 microcontroller allows you to achieve more accurate motion tracking from the MPU6050 sensor. With this tutorial, you have learned how to set up the hardware, read sensor data, and integrate a Kalman filter effectively. Experiment with different parameters to optimize performance for your specific application.