Wi-Fi RTLS — como funciona, onde se encaixa.

Wi-Fi RTLS leverages access points (APs) to estimate the position of Wi-Fi-emitting devices — phones, laptops, BLE-tag-via-AP, or dedicated Wi-Fi RTLS tags.

Three measurement techniques are used. RSSI (Received Signal Strength): device's signal strength at multiple APs feeds a position estimate — accuracy 5–10 m typically.

AoA (Angle of Arrival): newer APs with antenna arrays measure signal angle — accuracy 1–3 m.

RTT (Round-Trip Time, IEEE 802.11mc / FTM): APs and devices exchange timing measurements for distance, accuracy 1–2 m on supported devices. The defining property: leverages existing Wi-Fi rather than building dedicated RTLS infrastructure.

Position estimation runs on the AP controller or a cloud platform (Aruba Central, Cisco Spaces, Mist Cloud). APs report signal measurements from each device they hear; the platform combines measurements across multiple APs to triangulate position.

For BLE tags, modern Wi-Fi APs include BLE radios that report tag advertisements and signal strength alongside Wi-Fi clients — so the same infrastructure does both Wi-Fi and BLE positioning.

For accuracy, environment matters more than technology: an unobstructed open warehouse with dense AP placement can hit metre-level RTT accuracy; a typical office with sparse APs and partition walls might struggle to deliver better than 5 m.

Three categories are mature. Workplace and venue analytics: occupancy, foot-traffic, dwell-time analysis using existing Wi-Fi. Most large enterprises already have the infrastructure; the marginal cost is platform subscription.

Asset tracking at zone level: tagged equipment and people accuracy of a few metres is enough for many enterprise use cases — find the laptop, locate the maintenance tech, count occupants in a room.

Customer-experience analytics in retail and hospitality: customer-flow heatmaps, dwell, area utilisation — anonymised analytics from existing Wi-Fi.

Wi-Fi vs BLE-AoA: BLE-AoA wins on accuracy (sub-metre vs 1–10 m); Wi-Fi RTLS wins on infrastructure economics. Wi-Fi vs UWB: UWB wins on accuracy by an order of magnitude; UWB requires dedicated infrastructure; Wi-Fi rides existing kit.

Wi-Fi vs cellular positioning: cellular is outdoor / wide-area; Wi-Fi is indoor / building-scale. Wi-Fi vs visual SLAM: completely different categories — visual SLAM tracks a camera-equipped device's own position; Wi-Fi RTLS tracks devices via infrastructure observations.

Three constraints are real. Accuracy ceiling: even with RTT and dense AP placement, Wi-Fi RTLS rarely hits sub-metre. For workflow attribution, tactical applications or sub-decimetre needs, BLE-AoA or UWB are required.

Device support for RTT: 802.11mc Fine Timing Measurement requires supporting devices (newer Android phones, recent APs); coverage is limited.

Wi-Fi infrastructure quality: positioning quality is bounded by AP density and placement, which were typically designed for connectivity, not positioning. Retrofitting positioning often means adding APs.

Aruba (HPE): Aruba Meridian for location services, Aruba Central platform with BLE-integrated APs. Strong in workplace and large-venue. Cisco: Cisco Spaces (formerly DNA Spaces) integrates with Catalyst and Meraki Wi-Fi infrastructure.

Juniper Mist: AI-driven Wi-Fi platform with built-in BLE-AoA. Specialist platforms: Inpixon, Wifarer, RetailNext for retail and venue analytics on Wi-Fi vendor infrastructure. Padrões: IEEE 802.11mc / FTM for RTT; vendor-specific implementations for AoA and BLE integration.

Use cases where existing Wi-Fi infrastructure dominates the economics and accuracy needs are zone-level: workplace and venue analytics; broad asset and people visibility at room level; customer-flow analytics in retail; supplementary positioning in hybrid stacks.

We don't recommend Wi-Fi RTLS for sub-metre accuracy needs (BLE-AoA or UWB), for environments without existing Wi-Fi coverage (dedicated RTLS infrastructure is more cost-effective), or for use cases requiring high-update-rate tracking (BLE-AoA or UWB).