1506  Indoor Positioning Technologies

1506.1 Learning Objectives

By the end of this chapter, you will be able to:

  • Compare Indoor Positioning Technologies: Evaluate Wi-Fi, BLE, UWB, and ultrasonic systems for accuracy and cost
  • Apply Trilateration Calculations: Compute position estimates from beacon distance measurements
  • Design Sensor Fusion Systems: Combine GPS, Wi-Fi, BLE, and IMU for seamless indoor-outdoor transitions
  • Troubleshoot Multipath Effects: Identify and mitigate RF interference in indoor environments

1506.2 Prerequisites

1506.3 Why Indoor Positioning is Different

GPS doesn’t work indoors because:

  • Satellite signals are extremely weak (~10^-16 watts received)
  • Building materials attenuate signals by 20-40 dB
  • Multipath from walls, ceilings, and furniture creates severe interference
  • No line-of-sight to satellites in most buildings

This requires alternative technologies designed for indoor environments.

1506.4 Indoor Positioning Technologies

%% fig-cap: "Indoor Positioning Technology Comparison"
%% fig-alt: "Comparison of indoor positioning technologies by accuracy and infrastructure requirements"

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graph TB
    subgraph Proximity["Proximity Detection (Zone-Level)"]
        BLE[BLE Beacons<br/>1-3m accuracy<br/>$5-15/beacon<br/>1-5mW power]
        RFID[RFID/NFC<br/>Centimeters<br/>Requires contact]
    end

    subgraph Zone["Zone Positioning (Room-Level)"]
        WiFi[Wi-Fi Fingerprinting<br/>3-5m accuracy<br/>Uses existing APs<br/>Labor-intensive survey]
        Cell[Cellular<br/>100-1000m<br/>Tower-based]
    end

    subgraph Precise["Precise Positioning (Sub-meter)"]
        UWB[UWB<br/>10-30cm accuracy<br/>$15-30/tag<br/>Requires anchors]
        Ultrasound[Ultrasonic<br/>3cm accuracy<br/>Expensive<br/>LOS required]
    end

    subgraph Tradeoffs["Key Trade-offs"]
        T1[Accuracy vs. Cost]
        T2[Infrastructure vs. Opportunistic]
        T3[Power vs. Update Rate]
    end

    BLE --> WiFi
    WiFi --> UWB

    style BLE fill:#16A085,stroke:#2C3E50,stroke-width:3px,color:#fff
    style WiFi fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff
    style UWB fill:#2C3E50,stroke:#16A085,stroke-width:3px,color:#fff
    style Ultrasound fill:#E67E22,stroke:#2C3E50,stroke-width:2px,color:#fff

Figure 1506.1: Comparison of indoor positioning technologies by accuracy and infrastructure requirements.
Technology Accuracy Infrastructure Cost Power Use Case
BLE Beacons 1-3m Deploy beacons Low Very Low Retail, navigation
Wi-Fi RSSI 3-5m Use existing Free Medium Zone detection
UWB 10-30cm Deploy anchors High Medium Asset tracking, AR
Ultrasonic 3cm Deploy sensors Very High High Precision tracking
Camera/Vision Variable Deploy cameras High High Analytics, AR

1506.5 BLE Beacon Positioning

BLE beacons continuously broadcast identifier packets. Smartphones detect these signals and estimate distance based on Received Signal Strength Indication (RSSI).

Diagram showing BLE beacon deployment for indoor positioning with trilateration from multiple beacon RSSI values
Figure 1506.2: BLE iBeacons for Indoor Positioning

1506.5.1 RSSI-Based Distance Estimation

The relationship between RSSI and distance follows a path loss model:

\[ \text{RSSI} = \text{TxPower} - 10n \cdot \log_{10}(d) \]

Solving for distance:

\[ d = 10^{\frac{\text{TxPower} - \text{RSSI}}{10n}} \]

Where: - TxPower = Measured RSSI at 1 meter (typically -59 dBm) - n = Path loss exponent (2.0 in free space, 2.5-4.0 indoors) - d = Distance in meters

NoteWorked Example: BLE Beacon Trilateration in a Retail Store

Scenario: A retail store deploys BLE beacons for indoor customer navigation. The positioning system uses trilateration from RSSI measurements to estimate shopper location.

Given:

  • Beacon A at position (0, 0) meters, measured RSSI = -65 dBm
  • Beacon B at position (10, 0) meters, measured RSSI = -72 dBm
  • Beacon C at position (5, 8) meters, measured RSSI = -68 dBm
  • Calibrated path loss model: RSSI = -59 dBm at 1 meter, path loss exponent n = 2.5
  • Distance formula: d = 10^((TxPower - RSSI) / (10 x n))

Steps:

  1. Calculate distance from Beacon A:
    • d_A = 10^((-59 - (-65)) / (10 x 2.5)) = 10^(6/25) = 10^0.24 = 1.74 meters
  2. Calculate distance from Beacon B:
    • d_B = 10^((-59 - (-72)) / (10 x 2.5)) = 10^(13/25) = 10^0.52 = 3.31 meters
  3. Calculate distance from Beacon C:
    • d_C = 10^((-59 - (-68)) / (10 x 2.5)) = 10^(9/25) = 10^0.36 = 2.29 meters
  4. Apply trilateration equations:
    • Circle A: x^2 + y^2 = 1.74^2 = 3.03
    • Circle B: (x-10)^2 + y^2 = 3.31^2 = 10.96
    • Circle C: (x-5)^2 + (y-8)^2 = 2.29^2 = 5.24
    • Solving: Position estimate = (1.5, 0.8) meters
  5. Apply error bounds:
    • With RSSI variance of +/-3 dBm, distance error is +/-30%
    • Position uncertainty: +/-1.2 meters (95% confidence)

Result: Shopper estimated at (1.5, 0.8) meters with 1.2 meter accuracy, placing them in the “Electronics” zone near Beacon A.

Key Insight: BLE trilateration accuracy depends heavily on path loss exponent calibration. A path loss exponent error of 0.3 (using n=2.2 instead of n=2.5) would shift the position estimate by 0.8 meters. Always calibrate path loss in the actual deployment environment, not from datasheet values.

1506.6 Wi-Fi Fingerprinting

Wi-Fi fingerprinting maps RSSI patterns to physical locations by pre-surveying the space.

Diagram explaining Wi-Fi fingerprinting where RSSI patterns from multiple access points create unique location signatures
Figure 1506.3: Wi-Fi Signal Fingerprinting

1506.6.1 Two-Phase Process

Offline Phase (Survey): 1. Walk through the space with a device 2. At each known location, record RSSI from all visible access points 3. Build a fingerprint database mapping RSSI vectors to locations

Online Phase (Positioning): 1. Device scans visible APs and measures RSSI 2. Compare current RSSI vector to database entries 3. Find closest match(es) to estimate position

1506.6.2 Challenges with Fingerprinting

CautionWhy Wi-Fi Fingerprinting Degrades Over Time
  1. Environmental changes: Moved furniture, added walls, relocated APs
  2. Temporal variations: Different occupancy (empty vs. crowded) affects propagation
  3. Device variations: Different phones have different RSSI calibration
  4. Maintenance burden: Requires periodic re-surveying (every 3-6 months)

Mitigation strategies: - Crowd-sourced calibration (users’ movements gradually update database) - Hybrid approaches (combine fingerprinting with dead reckoning) - Machine learning (automatically detect and adapt to environmental changes)

1506.7 Ultra-Wideband (UWB) Positioning

UWB uses extremely short pulses (nanoseconds) across a wide frequency spectrum (3.1-10.6 GHz), enabling precise time-of-flight measurements.

Key advantages: - 10-30cm accuracy: Far exceeds BLE/Wi-Fi - Multipath resistant: Short pulses separate direct and reflected paths - Low interference: Spread spectrum doesn’t interfere with other systems

Applications: - Hospital equipment tracking (room-level precision) - Apple AirTags (precise “find my” direction finding) - Autonomous robot navigation - Augmented reality anchoring

%% fig-cap: "UWB Positioning System Architecture"
%% fig-alt: "Diagram showing UWB positioning with anchors and tag"

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graph TB
    subgraph Anchors["UWB Anchors (Known Positions)"]
        A1[Anchor 1<br/>Position: 0,0,2.5m]
        A2[Anchor 2<br/>Position: 10,0,2.5m]
        A3[Anchor 3<br/>Position: 10,8,2.5m]
        A4[Anchor 4<br/>Position: 0,8,2.5m]
    end

    subgraph Tag["UWB Tag (Unknown Position)"]
        T[Asset Tag<br/>Measures ToF to<br/>each anchor]
    end

    subgraph Calc["Position Calculation"]
        ToF[Time of Flight<br/>d = c × Δt]
        Trilat[3D Trilateration<br/>4 anchors for<br/>x, y, z position]
        Result[Position: 4.2, 3.7, 1.0m<br/>Accuracy: ±15cm]
    end

    A1 <-->|TWR| T
    A2 <-->|TWR| T
    A3 <-->|TWR| T
    A4 <-->|TWR| T

    T --> ToF
    ToF --> Trilat
    Trilat --> Result

    style T fill:#E67E22,stroke:#16A085,stroke-width:3px,color:#fff
    style Result fill:#16A085,stroke:#2C3E50,stroke-width:3px,color:#fff
    style A1 fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style A2 fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style A3 fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style A4 fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff

Figure 1506.4: Diagram showing UWB positioning with anchors at known positions and tag at unknown position.

1506.8 Sensor Fusion for Seamless Positioning

Real-world applications often require positioning that works both indoors and outdoors. Sensor fusion combines multiple positioning technologies to provide seamless coverage.

NoteWorked Example: Sensor Fusion for Seamless Indoor-Outdoor Positioning

Scenario: A delivery driver app must track location seamlessly as the driver moves from outdoor parking (GPS) to indoor warehouse (Wi-Fi/BLE) for package pickup.

Given:

  • Outdoor GPS position: (37.7749 N, -122.4194 W) with HDOP = 1.2, accuracy = 4.8 meters
  • GPS signal lost upon entering building (< 4 satellites visible)
  • Indoor Wi-Fi fingerprint match: Warehouse Zone B, confidence 78%
  • BLE beacon proximity: Beacon “Dock-3” at -62 dBm (estimated 1.5m distance)
  • IMU dead reckoning: 12.3 meters walked at heading 045 since GPS loss
  • Building floor plan constraint: Driver must be in accessible walkway

Steps:

  1. Detect GPS degradation:
    • Satellite count drops from 8 to 3 over 5 seconds
    • HDOP increases from 1.2 to 6.5 (poor geometry)
    • Trigger: Switch to indoor positioning mode when satellites < 4 AND HDOP > 4
  2. Initialize indoor position from last GPS fix:
    • Last valid GPS: (37.7749, -122.4194) at building entrance
    • Transform to local coordinates: (0, 0) meters at entrance door
  3. Fuse dead reckoning with Wi-Fi fingerprint:
    • IMU estimate: (8.7, 8.7) meters from entrance (12.3m at 45 degrees)
    • Wi-Fi fingerprint center: (10.0, 12.0) meters (Zone B centroid)
    • Kalman filter weighted average: (9.1, 9.8) meters
    • IMU weight: 0.6 (recent, low drift), Wi-Fi weight: 0.4 (lower confidence)
  4. Refine with BLE proximity:
    • Beacon Dock-3 located at (8.5, 10.0) meters
    • Proximity constraint: Driver within 1.5m of Dock-3
    • Updated position: (8.8, 9.9) meters
  5. Apply map constraints:
    • Check position against floor plan
    • Position (8.8, 9.9) is in valid walkway - no correction needed
    • If position were in wall/shelf, snap to nearest valid location

Result: Driver position estimated at (8.8, 9.9) meters from building entrance with 2.1 meter accuracy. This corresponds to “Loading Dock 3” in warehouse system, enabling automatic package assignment.

Key Insight: Sensor fusion requires careful weighting based on each source’s reliability. GPS provides absolute position outdoors but fails indoors. IMU provides relative movement but drifts over time (1-3% of distance traveled). Wi-Fi fingerprinting gives zone-level position but requires pre-surveyed database. BLE beacons provide proximity anchoring. The Kalman filter dynamically adjusts weights as sensor reliability changes, enabling sub-3-meter positioning across the indoor-outdoor transition.

%% fig-cap: "Sensor Fusion Architecture for Indoor-Outdoor Positioning"
%% fig-alt: "Diagram showing how multiple positioning sources are combined"

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graph TB
    subgraph Outdoor["Outdoor Sources"]
        GPS[GPS/GNSS<br/>5-10m accuracy<br/>High confidence outdoors]
        Cell[Cell Tower<br/>100-1000m<br/>Fallback only]
    end

    subgraph Indoor["Indoor Sources"]
        WiFi[Wi-Fi Fingerprint<br/>3-5m accuracy<br/>Zone-level]
        BLE[BLE Beacons<br/>1-3m accuracy<br/>Proximity anchoring]
    end

    subgraph Continuous["Continuous Sources"]
        IMU[IMU Dead Reckoning<br/>Accelerometer + Gyro<br/>Drift over time]
        Baro[Barometer<br/>Floor detection<br/>±0.5m vertical]
    end

    subgraph Fusion["Kalman Filter Fusion"]
        KF[Kalman Filter<br/>Weight by reliability<br/>Update continuously]
        Map[Map Matching<br/>Constrain to valid paths<br/>Snap to hallways]
    end

    GPS --> KF
    Cell --> KF
    WiFi --> KF
    BLE --> KF
    IMU --> KF
    Baro --> KF

    KF --> Map
    Map --> Output[Fused Position<br/>2-5m accuracy<br/>Seamless transition]

    style KF fill:#2C3E50,stroke:#16A085,stroke-width:3px,color:#fff
    style Output fill:#16A085,stroke:#2C3E50,stroke-width:3px,color:#fff
    style GPS fill:#16A085,stroke:#2C3E50,stroke-width:2px,color:#fff
    style BLE fill:#E67E22,stroke:#16A085,stroke-width:2px,color:#fff

Figure 1506.5: Diagram showing how multiple positioning sources are combined through Kalman filter fusion.

1506.9 Knowledge Check

Question 1: You’re deploying BLE beacons in a museum for indoor wayfinding. You place beacons 30 meters apart, but visitors’ apps show their location jumping erratically between distant exhibits. What is the most likely cause?

Indoor RF propagation suffers from multipath effects—signals bounce off walls, floors, ceilings, metal objects, and people. A beacon 5m away (direct path) might have weaker RSSI than one 20m away (reflected path with constructive interference). This causes erratic location estimates. Solutions: (1) Increase beacon density (every 5-10m for better triangulation), (2) Particle filters to smooth out noisy measurements over time, (3) Fingerprinting: Pre-map RSSI values at known locations, (4) Sensor fusion: Combine BLE with accelerometer, compass, and map constraints.

Question 2: A hospital deploys a real-time location system (RTLS) to track medical equipment. They need room-level accuracy (know which specific room equipment is in). Which technology provides the best accuracy for this application?

Ultra-Wideband (UWB) provides 10-30cm accuracy, far exceeding other indoor positioning technologies. UWB uses extremely short pulses across wide frequency spectrum (3.1-10.6 GHz), enabling precise time-of-flight measurements resistant to multipath interference. Hospital RTLS requirements: locate equipment in specific rooms (exam room 3A vs. 3B), track movement through doorways, asset utilization analytics. UWB is ideal despite higher cost ($10-30/tag vs. $2-5 for BLE). GPS doesn’t work indoors.

Question 3: You’re implementing Wi-Fi fingerprinting for indoor positioning in an office building. After initial deployment, accuracy degrades significantly. What is the most likely cause?

Wi-Fi fingerprinting maps RSSI patterns to physical locations during a calibration phase. Any environmental changes invalidate this mapping: moved furniture, added walls, relocated access points, different occupancy patterns. Wi-Fi fingerprinting is fundamentally brittle—requires periodic recalibration (every 3-6 months). This maintenance burden is why UWB and BLE beacons are often preferred for production deployments—they’re more robust to environmental changes.

1506.10 Summary

Indoor Positioning Technologies:

  • BLE Beacons: Low cost, 1-3m accuracy, requires beacon deployment
  • Wi-Fi Fingerprinting: Uses existing infrastructure, 3-5m accuracy, labor-intensive survey
  • UWB: High precision (10-30cm), multipath resistant, higher cost
  • Ultrasonic: Best accuracy (3cm) but expensive and requires line-of-sight

Key Trade-offs:

  • Accuracy vs. Cost: UWB is most accurate but most expensive
  • Infrastructure vs. Opportunistic: Wi-Fi reuses existing APs, BLE/UWB require deployment
  • Power vs. Update Rate: Frequent positioning updates drain batteries

Sensor Fusion Principles:

  • Combine multiple technologies for seamless indoor-outdoor coverage
  • Weight sources by reliability (GPS high outdoors, low indoors)
  • Use Kalman filters to smooth transitions and handle sensor failures
  • Apply map constraints to prevent impossible positions (inside walls)

1506.11 What’s Next

Continue to Location Privacy and Regulations to learn about privacy-preserving design patterns, E911 mandates, and ethical considerations when collecting location data.