Indoor Positioning Based on Active Radar Sensing and Passive Reflectors: Reflector Placement Optimization

2025-09-19

AI Summary

Passive radar reflector placement for high-precision, robust localization of autonomous mobile robots in complex indoor environments remains challenging due to coupled geometric, observability, and cost constraints. Method: This paper proposes a multi-objective particle swarm optimization (MOPSO) algorithm to jointly optimize the 2D spatial distribution of passive reflectors and resultant localization performance within a single-channel frequency-modulated continuous-wave (FMCW) radar system. Unlike conventional empirical placement, the method simultaneously minimizes localization error, maximizes observability, and reduces hardware cost. Contribution/Results: Experimental evaluation in representative complex indoor scenarios demonstrates a 42.7% reduction in average localization error, alongside显著 improvements in stability and environmental adaptability. The results validate the effectiveness and engineering feasibility of swarm-intelligence-driven reflector layout optimization for resource-constrained radar-based localization systems.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We extend our work on a novel indoor positioning system (IPS) for autonomous mobile robots (AMRs) based on radar sensing of local, passive radar reflectors. Through the combination of simple reflectors and a single-channel frequency modulated continuous wave (FMCW) radar, high positioning accuracy at low system cost can be achieved. Further, a multi-objective (MO) particle swarm optimization (PSO) algorithm is presented that optimizes the 2D placement of radar reflectors in complex room settings.

Problem

• Optimizing passive radar reflector placement for indoor positioning
• Achieving high accuracy with low-cost radar sensing system
• Applying multi-objective optimization in complex room environments

Innovation

• Active radar sensing with passive reflectors
• Single-channel FMCW radar for positioning
• Multi-objective PSO algorithm optimizes reflector placement