1267  Sensor Fusion Best Practices

Learning Objectives

After completing this chapter, you will be able to:

  • Identify and avoid common sensor fusion mistakes
  • Implement proper validation and outlier rejection
  • Design robust fusion systems with graceful degradation
  • Apply calibration and synchronization best practices

1267.1 Common Mistakes in Sensor Fusion

WarningThe 7 Pitfalls That Trip Up Even Experienced Engineers

1267.1.1 Pitfall 1: Blindly Trusting Fused Output

The Mistake: Sending fused output directly to actuators without validation.

# WRONG: Blindly trusting fused output
fused_position = kalman_filter.update(gps, imu)
send_to_autopilot(fused_position)  # Hope it's right!

Why itโ€™s wrong:

  • What if GPS gives wildly wrong reading (multipath error)?
  • What if IMU sensor fails (stuck at zero)?
  • Fusion amplifies garbage-in without sanity checks!

The Fix: Multi-Layer Validation

%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#16A085', 'secondaryColor': '#E67E22', 'tertiaryColor': '#ecf0f1'}}}%%
flowchart TB
    Input[New Measurement]

    Input --> Check1{Innovation Check<br/>Mahalanobis d2 < 5.99?}

    Check1 -->|No| Reject1[Reject Outlier]
    Check1 -->|Yes| Update[Update Filter]

    Update --> Check2{Physics Check<br/>velocity < max?}

    Check2 -->|No| Reset[Reset Filter]
    Check2 -->|Yes| Check3{Rate Check<br/>delta position < 10m/s?}

    Check3 -->|No| Reset
    Check3 -->|Yes| Valid[Valid Output]

    style Input fill:#2C3E50,stroke:#16A085,color:#fff
    style Valid fill:#27AE60,stroke:#2C3E50,color:#fff
    style Reject1 fill:#7F8C8D,stroke:#2C3E50,color:#fff
    style Reset fill:#7F8C8D,stroke:#2C3E50,color:#fff

Essential validation checks:

  1. Innovation consistency: Mahalanobis distance < chi-squared threshold
  2. Physics limits: Velocity < max_velocity
  3. Rate of change: Position canโ€™t jump > 10m in 1 second
  4. Cross-validation: Compare fused output against raw sensors
  5. Uncertainty monitoring: If covariance P grows unbounded, filter is diverging

Real-world example: Boeing 737 MAX MCAS system failed because it trusted a single sensor without fusion or validation -> two fatal crashes. Lesson: Always validate, never trust blindly!

CautionPitfall 2: Ignoring Sensor Timestamp Synchronization

The Mistake: Fusing sensor data using arrival timestamps instead of event timestamps.

Why It Happens: Network protocols deliver data โ€œas it arrives,โ€ and most database systems timestamp on insertion. Developers focus on fusion algorithms and forget that a GPS reading from 200ms ago cannot be directly fused with an accelerometer reading from 10ms ago.

The Fix: Implement proper time synchronization at three levels:

  1. Clock sync: Use NTP or PTP to synchronize all sensor node clocks to <10ms accuracy
  2. Event timestamping: Record when measurement occurred, not when received
  3. Temporal alignment: Use interpolation to align sensor readings before fusion

Warning sign: If your fused position oscillates rapidly despite smooth motion, check timestamp alignment first.

CautionPitfall 3: Assuming All Sensors Are Properly Calibrated

The Mistake: Deploying sensor fusion systems without verifying calibration, assuming factory calibration is sufficient.

The Fix: Implement a three-stage calibration verification:

  1. Incoming inspection: Test each sensor batch against reference standard
  2. In-situ calibration: Run 24-48 hour burn-in comparing against known references
  3. Runtime monitoring: Track sensor bias using fusion residuals

A temperature sensor with +2C uncorrected bias propagates through every fusion calculation, creating systematic errors that averaging cannot fix.

CautionPitfall 4: Wrong Q and R Noise Parameters

The Mistake: Using arbitrary values for process noise Q and measurement noise R.

Symptoms:

  • Q too small: Filter ignores measurements, overconfident in model
  • Q too large: Jerky estimates, follows noise
  • R too small: Jerky estimates, overweights measurements
  • R too large: Slow response, ignores sensor data

The Fix:

  • For R: Measure sensor variance empirically (stationary readings variance)
  • For Q: Model physical system dynamics, tune based on expected disturbances
  • Adaptive: Use innovation covariance monitoring to detect parameter mismatch
CautionPitfall 5: Not Handling Sensor Failures

The Mistake: Assuming all sensors always work correctly.

The Fix:

  1. Monitor sensor health: Check for stuck values, excessive noise, timeout
  2. Graceful degradation: Switch to reduced-sensor mode when failures detected
  3. Fault isolation: Identify which specific sensor failed
  4. Recovery: Reinitialize filter state when sensor recovers

Example: GPS failure -> switch to IMU-only dead reckoning with increased uncertainty.

CautionPitfall 6: Correlated Sensor Errors

The Mistake: Assuming sensor errors are independent when they share common error sources.

Examples:

  • Multiple Wi-Fi APs affected by same multipath
  • GPS and GLONASS sharing ionospheric errors
  • Temperature sensors on same PCB sharing thermal drift

The Fix:

  • Add different sensor types (diversity beats redundancy)
  • Model cross-correlation in covariance matrices
  • Use decorrelation techniques before fusion
CautionPitfall 7: Forgetting About Latency

The Mistake: Fusing high-latency sensors with low-latency sensors without compensation.

Example: Camera (100ms processing) + IMU (1ms) - by the time camera result is ready, IMU has moved 10 readings ahead.

The Fix:

  • Timestamp all measurements with event time, not processing time
  • Use state augmentation or measurement delay compensation
  • Predict camera measurement to current time using IMU data

1267.2 Validation Checklist

Before deploying any sensor fusion system, verify:

Check Pass Criteria
Calibration verified All sensors within 2-sigma of spec
Timestamp alignment Event times synchronized <10ms
Outlier rejection Mahalanobis test implemented
Physics constraints Impossible states rejected
Graceful degradation Works with sensor failures
Uncertainty tracking Covariance bounded and meaningful
Cross-validation Fused output between raw sensor values

1267.3 Chapter Summary

Multi-sensor data fusion combines measurements from multiple sensors to achieve higher accuracy, reliability, and robustness than any single sensor provides.

Key takeaways:

  1. Three fusion levels: Low-level (raw data), feature-level, decision-level
  2. Kalman filter: Optimal for linear systems with Gaussian noise
  3. Complementary filters: Simple, efficient for IMU orientation
  4. Particle filters: Handle non-linear, multi-modal distributions
  5. Validation is critical: Never trust fused output without sanity checks
  6. Calibration matters: Uncorrected bias propagates through all calculations
  7. Graceful degradation: Design for sensor failures from the start

Multi-sensor fusion is fundamental to building robust IoT systems that can make reliable decisions even when individual sensors are noisy or unreliable.

1267.4 Whatโ€™s Next