1266  Sensor Fusion Architectures

Learning Objectives

After completing this chapter, you will be able to:

  • Design centralized, distributed, and hierarchical fusion architectures
  • Apply the Dasarathy taxonomy for fusion classification
  • Choose appropriate fusion architectures for IoT applications
  • Understand trade-offs between architecture options

1266.1 Fusion Architecture Types

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flowchart TB
    subgraph Centralized["Centralized Fusion"]
        CS1[Sensor 1] --> CF[Central<br/>Fusion Node]
        CS2[Sensor 2] --> CF
        CS3[Sensor 3] --> CF
        CF --> CO[Optimal<br/>Result]
    end

    subgraph Distributed["Distributed Fusion"]
        DS1[Sensor 1] --> DF1[Local<br/>Fusion 1]
        DS2[Sensor 2] --> DF1
        DS3[Sensor 3] --> DF2[Local<br/>Fusion 2]
        DS4[Sensor 4] --> DF2
        DF1 & DF2 --> DG[Global<br/>Fusion]
        DG --> DO[Scalable<br/>Result]
    end

    subgraph Hierarchical["Hierarchical Fusion"]
        HS1[Sensor 1] --> HL1[Level 1<br/>Fusion]
        HS2[Sensor 2] --> HL1
        HS3[Sensor 3] --> HL2[Level 2<br/>Fusion]
        HS4[Sensor 4] --> HL2
        HL1 & HL2 --> HL3[Level 3<br/>Global]
        HL3 --> HO[Balanced<br/>Result]
    end

    style CF fill:#E67E22,stroke:#2C3E50,color:#fff
    style DF1 fill:#16A085,stroke:#2C3E50,color:#fff
    style DF2 fill:#16A085,stroke:#2C3E50,color:#fff
    style DG fill:#2C3E50,stroke:#16A085,color:#fff
    style HL1 fill:#16A085,stroke:#2C3E50,color:#fff
    style HL2 fill:#16A085,stroke:#2C3E50,color:#fff
    style HL3 fill:#2C3E50,stroke:#16A085,color:#fff

1266.1.1 Architecture Comparison

Architecture Pros Cons Use Case
Centralized Optimal fusion, simple Single point of failure, high bandwidth Small-scale systems
Distributed Scalable, fault-tolerant, low bandwidth Sub-optimal, complex Large-scale IoT networks
Hierarchical Balanced, modular Moderate complexity Smart buildings, smart cities

1266.2 Data Fusion Taxonomy (Dasarathy Classification)

The Dasarathy taxonomy provides a formal framework for classifying sensor fusion systems based on their input and output abstraction levels.

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flowchart TB
    subgraph DAI_DAO["DAI-DAO: Data In -> Data Out"]
        D1[Sensor 1 Raw] --> Fuse1[Weighted<br/>Average]
        D2[Sensor 2 Raw] --> Fuse1
        Fuse1 --> Out1[Fused Raw Data]
    end

    subgraph FEI_FEO["FEI-FEO: Feature In -> Feature Out"]
        F1[Feature Set 1] --> Fuse2[Feature<br/>Concatenation]
        F2[Feature Set 2] --> Fuse2
        Fuse2 --> Out2[Combined Features]
    end

    subgraph DEI_DEO["DEI-DEO: Decision In -> Decision Out"]
        Dec1[Decision 1<br/>90% pedestrian] --> Vote[Voting/<br/>Bayesian]
        Dec2[Decision 2<br/>85% obstacle] --> Vote
        Vote --> Out3[Final Decision<br/>BRAKE!]
    end

    style D1 fill:#2C3E50,stroke:#16A085,color:#fff
    style D2 fill:#2C3E50,stroke:#16A085,color:#fff
    style F1 fill:#16A085,stroke:#2C3E50,color:#fff
    style F2 fill:#16A085,stroke:#2C3E50,color:#fff
    style Dec1 fill:#E67E22,stroke:#2C3E50,color:#fff
    style Dec2 fill:#E67E22,stroke:#2C3E50,color:#fff
    style Out1 fill:#27AE60,stroke:#2C3E50,color:#fff
    style Out2 fill:#27AE60,stroke:#2C3E50,color:#fff
    style Out3 fill:#27AE60,stroke:#2C3E50,color:#fff

1266.2.1 The Five Dasarathy Categories

Category Input -> Output Description IoT Example
DAI-DAO Data -> Data Raw values combined Two temperature sensors averaged
DAI-FEO Data -> Feature Raw data to features Accel + gyro raw -> motion features
FEI-FEO Feature -> Feature Features merged Wi-Fi RSSI + BLE features combined
FEI-DEO Feature -> Decision Features classified HR + motion -> “User sleeping”
DEI-DEO Decision -> Decision Decisions fused Camera + Radar -> BRAKE!

1266.2.2 When to Use Each Category

  • DAI-DAO: Need single “best” raw measurement from redundant sensors
  • DAI-FEO: Raw data needs transformation into meaningful metrics
  • FEI-FEO: Combining complementary features from different sensor types
  • FEI-DEO: Features need classification into categorical outputs
  • DEI-DEO: Multiple independent classifiers vote on final decision

1266.3 Smart Home Multi-Sensor Example

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flowchart LR
    subgraph Sensors["Sensors (Raw Data)"]
        WiFi[Wi-Fi RSSI<br/>-65 dBm]
        ZB[Zigbee Motion<br/>Active]
        Temp[Temperature<br/>24C]
        Light[Light Level<br/>200 lux]
    end

    subgraph DAI["DAI-FEO Stage"]
        WiFi --> Loc[Location<br/>Feature]
        ZB --> Occ[Occupancy<br/>Feature]
    end

    subgraph FEI["FEI-FEO Stage"]
        Loc --> Combined[Combined<br/>Context]
        Occ --> Combined
        Temp --> Combined
        Light --> Combined
    end

    subgraph DEO["FEI-DEO Stage"]
        Combined --> Decision{Room<br/>Occupied?<br/>Comfort<br/>Optimal?}
    end

    subgraph Action["Actuation"]
        Decision --> HVAC[Adjust HVAC]
        Decision --> Lights[Dim Lights]
    end

    style WiFi fill:#2C3E50,stroke:#16A085,color:#fff
    style ZB fill:#16A085,stroke:#2C3E50,color:#fff
    style Temp fill:#E67E22,stroke:#2C3E50,color:#fff
    style Decision fill:#27AE60,stroke:#2C3E50,color:#fff

1266.4 Architecture Trade-offs

TipTradeoff: Early Fusion vs Late Fusion
Factor Early Fusion (Data-Level) Late Fusion (Decision-Level)
Information preservation Maximum Minimal
Computational cost Very high Low
Sensor heterogeneity Difficult Easy
Timing requirements Strict sync needed Relaxed (async OK)
Failure handling Harder Easier

Choose Early Fusion when:

  • Sensors measure complementary aspects (RGB + depth)
  • Cross-sensor correlations are important
  • Maximum accuracy is required

Choose Late Fusion when:

  • Sensors are heterogeneous with different rates
  • Graceful degradation is required
  • Computational resources are limited
TipTradeoff: Sensor-Level vs Central Fusion
Factor Sensor-Level Fusion Central Fusion
Latency Lower - local decisions Higher - network traversal
Bandwidth Lower - compressed data Higher - raw data
Accuracy May be limited locally Full dataset access
Reliability Graceful degradation Single point of failure

Choose Sensor-Level Fusion when:

  • Network bandwidth is constrained
  • Local latency requirements are strict
  • Privacy requires data to stay on-device

Choose Central Fusion when:

  • Sensors are heterogeneous and require complex correlation
  • Historical context is needed
  • Computational resources at edge are limited

1266.5 Worked Example: Autonomous Forklift Safety System

Scenario: Design fusion architecture for autonomous forklift pedestrian detection.

Requirements:

  • <200ms latency from detection to brake

  • 99.9% pedestrian recall

  • <1 false alarm per hour

Sensors:

  • 2D LIDAR (10 Hz, 360 deg, 30m range)
  • Stereo camera (30 Hz, 90 deg FOV)
  • Ultrasonic array (20 Hz, 180 deg, 5m range)

Architecture Decision: Cascaded fusion

  1. Stage 1 (LIDAR, 40ms): Fast point cloud clustering, flag all potential pedestrians
  2. Stage 2 (Camera, 80ms): Run CNN only on LIDAR-flagged regions
  3. Stage 3 (Ultrasonic, 20ms): Confirm close-range detections

Fusion Logic:

Alert if:
  (LIDAR AND Camera) OR
  (Ultrasonic AND Camera) OR
  (LIDAR confidence > 0.95)

Result: 99.4% recall, 0.7 false alarms/hr, 155ms latency

Key Insight: Cascaded fusion leverages each sensor’s strengths - LIDAR for reliable detection, camera for classification, tracking for occlusion robustness.

1266.6 Summary

Sensor fusion architectures determine system performance:

  • Centralized: Optimal but single point of failure
  • Distributed: Scalable and fault-tolerant
  • Hierarchical: Balanced approach for complex systems
  • Dasarathy taxonomy: Classifies fusion by input/output abstraction levels
  • Trade-offs: Early vs late fusion, sensor-level vs central

1266.7 What’s Next