Abstract
We present a low-cost sensor-fusion system combining a 10.525 GHz CW Doppler microwave sensor with an 8 × 8 Time-of-Flight (ToF) infrared sensor for short-range object tracking. Data are acquired and processed in a sequential fusion pipeline: ToF-based Convolutional Neural Networks (CNNs) estimate object presence, coordinates, and cross-section, while Doppler histograms yield radial velocity; outputs are then fused at the decision level. A dataset of 31,367 frames was collected. The system tracks objects (≥35 cm2) at speeds up to 10 m/s within 5–250 cm, achieving 98% detection and 84% positioning accuracy. This approach offers radar-like capabilities at a reduced cost, enabling applications in industrial, and consumer-electronics domains.
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