Inferensys

Glossary

Real-Time Location System (RTLS)

A technology infrastructure used to automatically identify and track the precise geographic position of assets or shipments in real time, often combining GPS with indoor positioning systems.
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What is Real-Time Location System (RTLS)?

A technology infrastructure used to automatically identify and track the precise geographic position of assets or shipments in real time, often combining GPS with indoor positioning systems.

A Real-Time Location System (RTLS) is a technology infrastructure that automatically identifies and continuously tracks the precise geographic position of tagged assets, inventory, or personnel. Unlike passive RFID that logs a checkpoint read, RTLS provides continuous visibility, updating coordinates dynamically to enable immediate decision-making in complex operational environments.

RTLS architectures typically fuse Global Positioning System (GPS) data for outdoor transit with indoor positioning systems—such as Ultra-Wideband (UWB), Bluetooth Low Energy (BLE), or Wi-Fi triangulation—to maintain seamless coverage. In cold chain logistics, this hybrid approach enables geofencing alerts and precise in-transit visibility, ensuring that temperature-sensitive pharmaceuticals are localized instantly during an excursion event.

LOCATION INTELLIGENCE

Core RTLS Technologies

The foundational technologies that enable Real-Time Location Systems (RTLS) to automatically identify and track the precise geographic position of assets or shipments. These systems combine outdoor satellite positioning with indoor sensor networks to provide continuous visibility across the entire cold chain.

01

Global Navigation Satellite System (GNSS)

The umbrella term for satellite constellations—including GPS, GLONASS, Galileo, and BeiDou—that provide geospatial positioning with global coverage. GNSS receivers calculate precise latitude, longitude, and altitude by trilaterating signals from at least four satellites.

  • Accuracy: 3-5 meters for standard civilian receivers; sub-meter with augmentation
  • Limitation: Signals cannot penetrate buildings, tunnels, or refrigerated containers
  • Cold Chain Role: Primary tracking method for over-the-road and ocean freight

Modern multi-constellation receivers improve reliability by accessing 80+ satellites simultaneously, reducing time-to-first-fix in challenging environments.

3-5m
Standard Accuracy
80+
Available Satellites
02

Ultra-Wideband (UWB)

A radio technology that transmits extremely short pulses across a wide frequency spectrum to achieve centimeter-level positioning accuracy. Unlike narrowband systems, UWB's high bandwidth makes it highly resistant to multipath interference from metal racking and concrete walls.

  • Accuracy: 10-30 centimeters in three dimensions
  • Range: Up to 200 meters line-of-sight
  • Power: Low consumption suitable for battery-powered tags
  • Cold Chain Application: Tracking pallets and high-value biologics within pharmaceutical warehouses and distribution centers

UWB anchors installed at known coordinates triangulate the position of mobile tags using Time Difference of Arrival (TDoA) or Two-Way Ranging (TWR).

10-30cm
3D Accuracy
03

Bluetooth Low Energy (BLE) Beacons

A proximity-based positioning technology where small, battery-efficient transmitters broadcast signals to nearby receivers. Received Signal Strength Indication (RSSI) is used to estimate distance, while triangulation from multiple beacons determines position.

  • Accuracy: 1-5 meters (room-level granularity)
  • Deployment: Low infrastructure cost; beacons can run for years on coin-cell batteries
  • Angle of Arrival (AoA): Newer BLE 5.1+ specifications enable sub-meter accuracy by measuring signal direction
  • Cold Chain Use Case: Monitoring dwell time of temperature-sensitive goods in staging areas and cross-docks

BLE mesh networks allow hundreds of beacons to relay data through a single gateway, reducing infrastructure complexity.

1-5m
Room-Level Accuracy
04

Wi-Fi Positioning System (WPS)

A localization method that leverages existing 802.11 wireless infrastructure to determine device location without deploying dedicated hardware. WPS uses RSSI fingerprinting or Fine Timing Measurement (FTM) to calculate position.

  • Accuracy: 5-15 meters with standard access points; 1-2 meters with FTM (802.11mc)
  • Advantage: Zero additional hardware cost in facilities with dense Wi-Fi coverage
  • Limitation: Accuracy degrades in dynamic environments where access points are moved or reconfigured
  • Application: Coarse tracking of ambient warehouse zones and confirming shipment presence at loading docks

Fingerprinting requires an initial site survey to map signal strengths at known reference points, which the system later matches against real-time readings.

1-15m
Accuracy Range
05

Radio Frequency Identification (RFID)

A technology using electromagnetic fields to automatically identify and track tags attached to objects. Passive RFID tags harvest energy from the reader's signal, while Active RFID tags contain a battery for continuous broadcasting.

  • Passive UHF RFID: Read range up to 12 meters; ideal for portal-based chokepoint detection at dock doors
  • Active RFID: Range up to 100 meters; enables real-time zone-level location without line-of-sight
  • Cold Chain Integration: RFID tags with embedded temperature sensors log thermal history alongside location data
  • Key Metric: Read rate accuracy exceeding 99.9% at conveyor speeds

RFID excels in high-volume scanning scenarios where hundreds of tagged items pass through a checkpoint simultaneously, such as cold storage intake and dispatch.

99.9%
Read Accuracy
100m
Active RFID Range
06

Sensor Fusion Engine

The software layer that combines data from multiple positioning technologies—GNSS, UWB, BLE, Wi-Fi, and inertial sensors—into a single, coherent location stream. Sensor fusion algorithms compensate for the weaknesses of individual technologies.

  • Kalman Filtering: A recursive algorithm that estimates position by weighting sensor inputs based on their statistical uncertainty
  • Dead Reckoning: Uses accelerometers and gyroscopes to estimate movement when primary signals are lost
  • Seamless Transition: Automatically switches from outdoor GPS to indoor UWB as a shipment moves from truck to warehouse
  • Output: A unified x,y,z coordinate with a quantified confidence interval

This approach ensures continuous visibility across the entire cold chain journey, eliminating blind spots during modal transitions.

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Technology Handoff
RTLS EXPLAINED

Frequently Asked Questions

Clear, technically precise answers to the most common questions about Real-Time Location System architecture, enabling technologies, and operational deployment in cold chain logistics.

A Real-Time Location System (RTLS) is an infrastructure of hardware and software that continuously identifies and tracks the precise geographic or local position of tagged assets, shipments, or personnel, updating location data with minimal latency. Unlike passive tracking that logs history for later retrieval, RTLS provides continuous, live positional awareness. The system works by attaching active tags to objects; these tags emit signals—typically radio frequency (RF), ultrasound, or infrared—that are received by fixed reference points called anchors or readers deployed throughout a facility. The anchors measure signal properties such as Time of Flight (ToF), Angle of Arrival (AoA), or Received Signal Strength Indicator (RSSI). A location engine then applies multilateration or triangulation algorithms to compute the tag's coordinates, often achieving sub-meter accuracy. This positional data is streamed to a software platform that visualizes asset movement on a digital map, triggers geofence alerts, and integrates with enterprise systems like a Warehouse Management System (WMS) or Supply Chain Control Tower.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.