Sensor Fusion Using Kalman Filter for Two-Sensor Signal Integration

This implementation involves two sensors collecting distinct signals that require integration to derive more accurate data. To achieve this, we employ a Kalman filter algorithm which optimally combines sensor readings while reducing noise. One sensor is a dual-channel accelerometer that detects object acceleration and converts it into digital signals through analog-to-digital conversion (ADC). The other sensor serves complementary functions, potentially measuring orientation, velocity, or position. The Kalman filter implementation typically involves two main stages: prediction (using system dynamics) and update (incorporating sensor measurements). Key functions would include state transition matrix calculation, covariance propagation, and Kalman gain computation. By fusing data from both sensors through this algorithmic approach, we obtain enhanced accuracy in motion state estimation, significantly improving object tracking and dynamic behavior analysis. The MATLAB implementation would likely involve functions like 'kalman' for filter design and sensor data matrix operations for measurement fusion.