Sensor Fusion Visualizer

Sensor Fusion Visualizer

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

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

TrySelect Drone with Complementary alpha 0.92 and Interference/dropout 25%, then compare Weighted and Kalman.

ObserveError and residual traces respond differently when the fast sensor drifts or drops; Kalman model trust follows modeled uncertainty. Step confirms this readout.

ExplainFusion improves estimates when errors complement each other: time-aware filters combine prediction with measurements by uncertainty. Step exposes this mechanism.

Technical boundariesThe visualization omits full dynamics, correlated noise, asynchronous sampling, nonlinear observations, calibration, stability, and fault isolation.

Colour keyanalytics ml identitycurrent / primaryreference / datasuccesscautionerror / failure

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.

Controls

Indoor tracking

Scenariohidden value

Algorithmreliability

Viewtime series

PlaybackTruth

Sensor inputenable/disable

Conditionsstress test

Noise level x1.0 Higher noise makes instant readings less trustworthy.

Fast-sensor drift x0.6 Dead-reckoned sensors can look smooth while slowly moving away from truth.

Interference/dropout 25% Disturbs the absolute sensor during part of the run.

Complementary alpha 0.92 Higher alpha trusts the fast drifting estimate more.

Kalman model trust x1.0 Higher value lets the Kalman estimate move more between measurements.

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.

Diagnosis

improves estimate

Fusion helps The fused estimate beats the best individual sensor.

Current truth0.0 deg

Fast sensor0.0 deg

Absolute sensor0.0 deg

Context sensor0.0 deg

Fused estimate0.0 deg

Current error0.0 deg

Main trustAbsolute

Next actionKeep tuning

Weighted fusion uses known sensor quality instead of treating every reading as equally reliable.

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.