# Clutter Mitigation in Indoor Radar Sensors Using Sensor Fusion Technology

## TL;DR

This paper introduces a method to reduce clutter in indoor radar sensors by combining radar data with low-resolution camera images, improving detection accuracy.

## Contribution

The novel use of low-resolution camera data to suppress clutter in radar signals through sensor fusion is presented.

## Key findings

- The method improves signal-to-noise ratio by 2 dB in indoor radar tracking.
- Target detection rate increases by 8.6% in the 4–6 m range with clutter mitigation.
- Performance gains are observed under constrained velocity conditions.

## Abstract

A methodology utilizing low-resolution camera data is proposed to mitigate clutter effects on radar sensors in smart indoor environments. The proposed technique suppresses clutter in distance–velocity (range–Doppler) images obtained from millimeter-wave radar by estimating clutter locations using approximate spatial information derived from low-resolution camera images. Notably, the inherent blur present in low-resolution images closely corresponds to the distortion patterns induced by clutter in radar signals, making such data particularly suitable for effective sensor fusion. Experimental validation was conducted in indoor path-tracking scenarios involving a moving subject within a 10 m range. Performance was quantitatively evaluated against baseline range–Doppler maps obtained using radar data alone, without clutter mitigation. The results show that our approach improves the signal-to-noise ratio by 2 dB and increases the target detection rate by 8.6% within the critical 4–6 m range, with additional gains observed under constrained velocity conditions.