1309  Multi-Sensor Data Fusion

1309.1 Multi-Sensor Data Fusion

This section provides a stable anchor for cross-references to sensor fusion content across the book.

1309.2 Overview

Multi-sensor data fusion combines data from multiple sensors to produce more accurate, reliable, and complete information than any single sensor could provide. In IoT systems, sensor fusion is essential for applications like autonomous vehicles, robotics, navigation, and activity recognition.

TipMinimum Viable Understanding: Data Quality Through Sensor Fusion

Core Concept: Individual sensors lie in predictable ways - GPS drifts indoors, accelerometers accumulate bias, magnetometers suffer interference. Sensor fusion combines multiple imperfect measurements to produce estimates more accurate than any single sensor alone.

Why It Matters: Single-sensor systems fail catastrophically in real-world conditions. A drone relying solely on GPS loses position indoors; one using fused GPS + IMU + barometer maintains accuracy. Data quality is not about perfect sensors - it is about intelligent combination of imperfect ones.

Key Takeaway: Start with complementary filters (simple, computationally cheap) for combining fast/noisy sensors with slow/accurate ones. Graduate to Kalman filters when you need optimal uncertainty tracking.

1309.3 Chapter Series

This comprehensive topic is covered across 8 focused chapters:

1309.3.1 1. Introduction to Sensor Fusion

Fundamentals of sensor fusion, the three fusion levels, real-world examples, and beginner-friendly explanations.

  • What is sensor fusion and why it matters
  • The three levels: raw data, feature, decision
  • Smartphone compass and self-driving car examples
  • Prerequisites and how fusion fits into IoT analytics

1309.3.2 2. Kalman Filters for Sensor Fusion

The optimal algorithm for linear state estimation with Gaussian noise.

  • State-space models and predict-update cycle
  • Kalman gain and uncertainty propagation
  • Worked examples: temperature tracking, GPS+accelerometer fusion
  • Parameter tuning (Q, R) for optimal performance

1309.3.3 3. Complementary Filters and IMU Fusion

Efficient orientation estimation for drones, wearables, and robotics.

  • Complementary filter principle and alpha tuning
  • Gyroscope drift correction with accelerometer
  • Madgwick filter and quaternion representation
  • 9-DOF IMU fusion with magnetometer

1309.3.4 4. Particle Filters for Indoor Localization

Non-linear, non-Gaussian state estimation for complex environments.

  • Propagate-correct-resample algorithm
  • Mall navigation worked example
  • When to use particle filters vs Kalman
  • Map integration and constraints

1309.3.5 5. Sensor Fusion Architectures

System design patterns for multi-sensor systems.

  • Centralized, distributed, and hierarchical architectures
  • Dasarathy taxonomy (DAI-DAO, FEI-FEO, DEI-DEO)
  • Trade-offs: early vs late fusion, sensor-level vs central
  • Autonomous forklift safety system example

1309.3.6 6. Real-World Sensor Fusion Applications

Practical examples from smartphones to autonomous vehicles.

  • Smartphone screen rotation (3-sensor fusion)
  • Activity recognition with feature extraction
  • MFCC audio processing for IoT
  • Autonomous vehicle multi-sensor fusion

1309.3.7 7. Sensor Fusion Best Practices

Common pitfalls and how to avoid them.

  • The 7 critical mistakes in sensor fusion
  • Multi-layer validation and outlier rejection
  • Calibration and timestamp synchronization
  • Graceful degradation design

1309.3.8 8. Sensor Fusion Practice Exercises

Hands-on learning with exercises, videos, and resources.

  • Kalman filter implementation exercise
  • Complementary filter for IMU orientation
  • Multi-sensor data quality assessment
  • Video tutorials and library references

1309.4 Quick Start Guide

Your Goal Start Here
New to fusion Introduction
Need optimal state estimation Kalman Filters
Building drone/wearable IMU Fusion
Indoor positioning Particle Filters
Designing system architecture Architectures
Looking for examples Applications
Debugging fusion issues Best Practices
Hands-on practice Exercises

1309.5 Key Concepts Summary

Technique Best For Complexity Accuracy
Weighted Average Redundant sensors Low Good
Complementary Filter IMU orientation Low Good
Kalman Filter Linear, Gaussian Medium Optimal
Extended Kalman Weakly nonlinear Medium Very Good
Particle Filter Any distribution High Excellent

1309.6 What’s Next

After completing this chapter series:

Sensing Foundation:

Data Management:

Architecture:

Learning Hubs: