MATLAB Implementation of Inertial Navigation Simulation

Inertial navigation simulation plays a critical role in navigation system development, and MATLAB enables the implementation of a complete simulation workflow. The simulation typically consists of three core modules: trajectory generation, inertial measurement unit (IMU) simulation, and navigation solution with filtering algorithms.

Trajectory Generation Trajectory generation serves as the starting point of simulation, requiring the modeling of vehicle motion states including position, velocity, and attitude. The reference trajectory can be generated by setting initial conditions and motion models (such as constant velocity, acceleration, or maneuver turns). For more complex scenarios, motion equations in three-dimensional space can be implemented, considering Earth rotation and gravitational field effects to make the simulation more realistic. In MATLAB, this can be achieved using ode45 solver for differential equations or creating custom kinematic models with quaternion-based attitude representation.

IMU Data Simulation The core component of inertial navigation systems is the IMU (Inertial Measurement Unit), which contains accelerometers and gyroscopes. In simulation, ideal acceleration and angular rates are calculated based on the generated trajectory, with added noise and error models (including bias, scale factor errors, random walk, etc.) to simulate real sensor characteristics. This step directly impacts the accuracy assessment of subsequent navigation algorithms. MATLAB implementation typically involves creating sensor models using awgn function for noise addition and designing error propagation models using state-space representations.

Navigation Solution and Kalman Filtering Inertial navigation systems estimate position, velocity, and attitude by integrating IMU data, but due to error accumulation, the accuracy of pure inertial navigation degrades over time. Therefore, external observation information such as GPS is typically combined using Kalman filtering for integrated navigation. In simulation, loosely-coupled or tightly-coupled filter architectures can be designed, with navigation accuracy optimized by adjusting noise covariance matrices and observation models. MATLAB's Control System Toolbox provides kalman filter functions, while custom implementations can use Euler integration or Runge-Kutta methods for navigation mechanization equations.

By implementing the above modules in MATLAB, comprehensive performance evaluation of inertial navigation systems can be achieved, providing reliable simulation verification methods for practical engineering applications. The code structure typically includes main simulation loops, sensor data processing functions, and performance metric calculations using norm and covariance analysis tools.