Sensor Fusion Visualizer

Combine imperfect sensors, tune trust, and see when fusion beats the best single sensor.

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Animation Beginner Sensor fusion Trust weights

Sensor Fusion Visualizer

Combine noisy, drifting, and sometimes disturbed sensors to estimate one hidden value. The goal is not to average everything blindly; it is to decide which readings deserve trust right now.

Weightedfusion algorithm
0.00 degfused mean absolute error
0.00 degbest single sensor MAE
Checkingfusion decision
Use Strengths

Fast sensors react quickly; absolute sensors stop long-term drift; context sensors catch impossible values.

Trust Changes

A sensor that is normally useful can become unreliable during vibration, interference, or dropout.

Time Matters

Different update rates need buffering, interpolation, or prediction before readings can be compared.

Verify Output

Fusion is only better when its error is lower than the best available single sensor.

1

Hidden Truth

A real value changes over time but cannot be read directly.

2

Sensor Readings

Each sensor sees a biased, noisy, or delayed version.

3

Trust Model

The algorithm decides how much each reading should influence the estimate.

4

Fused Estimate

The readings are combined into one best current estimate.

5

Error Check

The result is compared with truth and the best single sensor.

Active Sensors3 sensorsReadings currently allowed into the estimate.
Best SensorFastLowest individual error over the run.
RMSE0.00 degFused error with larger mistakes penalized more.
Improvement0%MAE reduction compared with the best single sensor.

Fusion timeline

truth, sensors, and estimate
hidden truth fast sensor absolute sensor context sensor fused estimate
Hidden truthThe real value changes but is not directly visible.
Sensor behaviorEach sensor has a different failure pattern.
Fusion evidenceThe estimate is judged by error, not by smoothness alone.

Simple Mean

All active sensors are trusted equally.

estimate = average(readings)

Weighted Mean

Lower-variance sensors receive larger weights.

weight = 1 / variance

Complementary

Fast estimate handles motion while an absolute reference limits drift.

estimate = alpha fast + (1-alpha) absolute

Scalar Kalman

A recursive estimate predicts, then corrects with each active measurement.

K = P / (P + R)
Fusion Quick Reference

Sensor fusion combines measurements that fail in different ways. A good fused estimate is usually more accurate, more stable, or more fault tolerant than a single sensor.

  • Noise is random short-term variation.
  • Bias is a consistent offset.
  • Drift is bias that grows or changes over time.
  • Interference is context-specific corruption, such as vibration or magnetic disturbance.
Algorithm Selection

Use the simplest method that matches what you know about the sensors and the motion.

  • Simple mean is useful only when active sensors have similar quality.
  • Weighted mean is useful when sensor variances are known or estimated.
  • Complementary filters are common for fast drifting plus slower absolute references.
  • Kalman filters are useful when a state estimate, uncertainty, and measurement noise model are available.
Technical Accuracy Notes

This page uses a scalar teaching model. It estimates one value over time, such as angle, position, speed, or temperature. It is not a full 3D IMU attitude filter.

  • The complementary filter shown here blends a fast dead-reckoned estimate with a slower absolute estimate.
  • The Kalman filter is a scalar random-walk model with sequential measurement updates.
  • Weights use inverse variance, with extra variance added during interference.
  • MAE and RMSE are calculated over the full visible run.
  • Real fusion also needs sensor calibration, clock synchronization, coordinate alignment, failure detection, and validation data.
Implementation Checklist

A deployable fusion pipeline needs more than an algorithm name.

  • Calibrate each sensor before fusing it.
  • Timestamp readings and handle different update rates.
  • Estimate noise and bias under realistic operating conditions.
  • Reject impossible readings and detect stuck or missing sensors.
  • Compare fused output against independent ground truth before release.

Break Simple Averaging

Select Drone, increase drift and interference, then compare Simple against Weighted. Watch why equal trust becomes risky.

Tune Alpha

Select Complementary and move alpha. Too high follows the drifting fast sensor; too low follows noisy absolute readings.

Remove A Sensor

Disable the absolute sensor during Indoor navigation. Notice which algorithms degrade gracefully and which lose their anchor.

Sensor Calibration Process

Prepare individual sensor readings before they enter a fusion pipeline.

 Signal Conditioning Pipeline

Filter and scale raw signals before software estimation.

 Kalman Filter Predict-Update Cycle

Continue from this scalar model into a focused Kalman animation.