Design of Dead Reckoning Positioning and Navigation System with Kalman Filter Implementation

In this paper, we present a program design for Dead Reckoning (DR) positioning and navigation that operates effectively in indoor environments. The implementation involves calculating position updates through inertial measurement unit (IMU) data integration, including accelerometer and gyroscope readings. We enhance the system's accuracy by implementing a Kalman filter algorithm that processes raw sensor data, reduces measurement noise, and optimally estimates position states. The Kalman filter implementation follows a standard predict-update cycle where system dynamics are modeled through state transition matrices, and measurement corrections are applied using observation models. Our simulation results indicate that the program design demonstrates outstanding performance, with the Kalman filter effectively minimizing cumulative errors typical in dead reckoning systems. The code structure includes modular components for sensor data acquisition, motion state prediction, and measurement correction, making it suitable for practical applications in indoor navigation scenarios.