Multi-Sensor Navigation Simulator

Interactive simulation of IMU, Doppler surrogate, and acoustic beacon fusion with Extended Kalman Filter state estimation and real-time drift visualization.

Sensor Configuration

IMU Active
DVL Surrogate Active
Acoustic Beacon Active
Depth Sensor Active

Navigation Trajectory

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Final Position Error (m)
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Effective Drift (%/hr)
--
Distance Traveled (m)
--
Max Uncertainty (m)

Position Error Evolution

Position Uncertainty (3σ)

Sensor Contributions

Navigation Filter Theory

The navigation system employs an Extended Kalman Filter (EKF) with a 15-state vector including position, velocity, attitude, IMU biases, and DVL scale factor.

State Vector: x = [pNED, vNED, q, ba, bg, sDVL]T

Sensor Fusion Benefits

  • IMU Only: ~1-2% distance drift per hour (unbounded)
  • IMU + DVL: ~0.1-0.2% distance drift (velocity-bounded)
  • IMU + DVL + Acoustic: <2m absolute accuracy (position-bounded)

Error Propagation Model

IMU drift follows a random walk model where position error grows quadratically with time in the absence of aiding sensors:

σp(t) = σa · t² / 2 + σg · v · t

Where σa is accelerometer noise density and σg is gyroscope noise density. DVL aiding bounds velocity errors, while acoustic fixes provide absolute position corrections.