Accurate indoor tracking — at the scale of centimetres — is the new gold standard for asset monitoring, robotics, and smart spaces. GPS is fundamentally limited indoors, but Ultra-Wideband (UWB) technology, paired with ESP32 microcontrollers, delivers the precision necessary for modern digital applications. This project demonstrates a complete, scalable UWB indoor positioning system built with Qorvo DWM3000 modules and ESP32 MCUs, leveraging robust two-way ranging and real-time location visualisation.​

Why Choose UWB over Traditional Indoor Positioning?

Traditional approaches such as Wi-Fi and Bluetooth offer only meter-level accuracy and are susceptible to signal reflection issues. UWB, by contrast, uses nanosecond-pulse radio and precise time-of-flight measurement between anchors and tags, achieving 10cm accuracy even in cluttered environments. This enables asset tracking, navigation, and automation in warehouses, hospitals, factories, and AR/VR applications where traditional systems fail.​

UWB Indoor Pointioning System Using ESP32

Project Overview and Architecture

Core Hardware:

  • ESP32-WROOM microcontroller for wireless networking and onboard processing.
  • Qorvo DWM3000 UWB module — fully integrated, supports IEEE 802.15.4z, and includes antennas for global operation.
  • Minimum three anchors, fixed at known locations, with the tag device moving in the environment.
  • Wi-Fi connectivity for real-time data streaming to a Python-based visualisation host.

Accuracy:

  • Sub-10cm, based on calibrations of antenna delay and least-squares trilateration algorithms.
  • Robust against multipath and NLOS (Non-Line-of-Sight) thanks to UWB physics and advanced filtering.​

Implementation Steps

1. Hardware Connections

  • Connect Qorvo DWM3000 modules via SPI with the ESP32 boards.
  • Establish anchor positions in the environment, ensuring line-of-sight if possible for maximum accuracy.
  • Connect ESP32 tags to power and local Wi-Fi.
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DWM3000 Connection

2. Firmware Setup

  • ESP32 tag firmware manages ranging with each anchor and sends JSON-formatted data over TCP to the host computer.
  • Anchor firmware handles incoming requests and performs timing protocols for double-sided two-way ranging.
  • Driver class for DWM3000 abstracts low-level SPI, register access, RF configuration, and analytics.
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3. Real-Time Trilateration and Visualisation

  • Python script running on the host listens for incoming data, parses anchor distances, and applies least-squares trilateration.
  • Matplotlib visualises real-time position on a floorplan, plots tag movement as a trace, and displays live signal strengths for all anchors.
  • Optional enhancements include additional anchors for 3D tracking, web-based dashboards, and multiple tag support.​
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Key Project Features

High Accuracy:

  • UWB positioning precision reaches 10cm or better after calibration.
  • Ranging resilient against signal multipath and NLOS effects.

Scalability & Flexibility:

  • Easy expansion with extra anchors, support for multi-tag deployments.
  • Modular firmware allows quick adaptation to tag, anchor, or network changes.

Security:

  • IEEE 802.15.4z-compliant DWM3000 module supports anti-spoofing and data integrity features for secure asset tracking.

Common Applications

  • Smart warehouse automation and AGV navigation
  • Healthcare asset tracking and personnel safety zones
  • Factory automation, tool inventory, and process monitoring
  • Indoor navigation for airports, shopping malls, or event venues
  • AR/VR spatial experiences need real-time object location

Troubleshooting & Optimisation Tips

  • Calibrate anchor positions and antenna delay for optimal performance.
  • Median filtering eliminates noise from sporadic measurements.
  • Monitor live RSSI for anchors and adjust antenna orientation or power as needed.
  • Expand with additional anchors or switch data visualisation to a browser dashboard for wider deployment.

Conclusion

This UWB Indoor Positioning System using ESP32 is a scalable solution that brings centimetre-level positional accuracy to environments unreachable by GPS. With state-of-the-art hardware, open-source firmware, and Python visualisation, engineers can deploy robust RTLS for assets, people, or robots — in factories, hospitals, or smart cities. Start building, iterate, and transform your indoor environments with next-gen spatial intelligence.​

For source code, full schematic, and visualisation tools, visit the official repositories. Feedback, troubleshooting, and upgrade ideas are welcome in the discussion below!