ArticleSensors (Basel, Switzerland)2021
Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Domain-Adversarial Neural Network for UWB NLOS Identification in Multiple Environments.Sensors (Basel, Switzerland) · 2026Article
- Improving Fingerprint-Based Positioning by Using IEEE 802.11mc FTM/RTT Observables.Sensors (Basel, Switzerland) · 2022Article
- A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives.Sensors (Basel, Switzerland) · 2022Review
- A Survey of Recent Indoor Localization Scenarios and Methodologies.Sensors (Basel, Switzerland) · 2021Review
- Novel Solutions to the Three-Anchor ToA-Based Three-Dimensional Positioning Problem.Sensors (Basel, Switzerland) · 2021Article
- High Dynamic Weak Signal Tracking Algorithm of a Beidou Vector Receiver Based on an Adaptive Square Root Cubature Kalman Filter.Sensors (Basel, Switzerland) · 2021Article
- Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi.Sensors (Basel, Switzerland) · 2021Article
- Ultrawideband (UWB)-based precise short-range localization for wireless power transfer to electric vehicles in parking environments.PeerJ. Computer science · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.
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Registered trials
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