Location Engine

The software component performing position calculations based on raw measurements from anchors and tags. Implements positioning algorithms like trilateration, triangulation, or fingerprinting to convert signal data into coordinates. May include filtering, prediction, and optimization to improve accuracy and smooth movements. Core component of RTLS architecture determining system performance.

Software component performing position calculation from raw measurements (signal strengths, arrival times, angles) received from RTLS infrastructure. Location engine implements positioning algorithms converting signal data into coordinate estimates. Engine inputs vary by technology: UWB engines consume arrival time measurements from multiple anchors, BLE/Wi-Fi engines process RSSI values from multiple access points, AoA engines analyze phase differences from antenna arrays. Location engine processing includes: (1) Data collection - gathering measurements from infrastructure devices for each tag update. (2) Filtering - removing spurious measurements (outliers, interference, low-quality signals). (3) Algorithm execution - applying positioning mathematics (trilateration for ranging systems, fingerprint matching for RSSI systems, angulation for AoA systems). (4) Kalman filtering - smoothing position estimates using movement models and historical positions. (5) Zone mapping - associating coordinates with defined zones or areas. (6) Quality assessment - calculating accuracy estimates and confidence metrics. Industrial location engines typically run on: centralized servers (handling 1000-5000 tags), edge servers (100-1000 tags), or distributed processing at anchor level (50-200 tags per anchor). Engine performance metrics include: throughput (position calculations per second), latency (processing time from raw data to position output, typically 10-100ms), accuracy (position error relative to ground truth), and reliability (successful position rate vs. timeout or error rate). Engine configuration parameters include: positioning algorithm selection, filter coefficients, quality thresholds, and technology-specific settings. Proper engine tuning critical for optimal performance - balance between responsiveness (low latency, quick response to movement) and stability (avoiding jitter from noisy measurements).