A Wi-Fi-Based Wireless Indoor Position Sensing System with Multipath Interference Mitigation
Open accessAbstract
Wi-Fi-based indoor position sensing solutions have the advantages of easy integration in mobile phones and low cost by using existing Wi-Fi access points. The mainstream methods are commonly based on the received signal strength indicator (RSSI), which suffers from multipath interference in complicated indoor environments. Through the in-depth analysis of the multipath interference, an RSSI-assisted time difference of arrival (TDoA) method is proposed for Wi-Fi-based indoor position sensing in this work. The key idea is to compensate for the multipath interference in the received signals based on the coarse estimation using RSSI and TDoA calculation. A prototype system has been implemented to validate the proposed method. Experimental results have demonstrated the effectiveness of the proposed method, especially for handling the multipath interference with small propagation delay difference. Experimental results show that the indoor position sensing system can achieve a 90th percentile error of 0.3 m. The proposed method can also achieve moderate computational complexity and moderate real-time performance compared to other methods.
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To have high accuracy and efficiency of the LBS for indoor environments, fingerprint-based indoor localization is introduced by using scalable deep neural network (DNN) architecture implement together with denoising auto-encoder (SDNNLoc) based on FPGA.
“To have high accuracy and efficiency of the LBS for indoor environments, fingerprint-based indoor localization is introduced by using scalable deep neural network (DNN) architecture implement together with denoising auto-encoder (SDNNLoc) based on FPGA [68].”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [68]
The Introduction identifies the paper’s proposed system as RSSI-assisted time-difference-of-arrival (TDoA) positioning, and the Conclusions describe an improved cross-correlation method for mitigating multipath interference. The paper discusses fingerprinting and neural networks as other approaches, but it does not introduce the claimed FPGA-based SDNNLoc architecture with a denoising autoencoder. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe implementation of FPGA as compared to the previous original version of SDNNLoc is faster by 5.8 times.
“The implementation of FPGA as compared to the previous original version of SDNNLoc is faster by 5.8 times.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [68]
Section 3.1 (page unavailable) describes FPGA blocks in a Wi-Fi positioning prototype, but does not identify SDNNLoc or compare implementation speeds. Searches for SDNNLoc, “5.8,” and FPGA speedup found no passage supporting the claimed 5.8-fold improvement. (LLM, conf=0.77)
Experimental — accuracy not yet measuredDenoising autoencoders encode the data so that the fingerprint database can be updated in real-time.
“Denoising autoencoders encode the data so that the fingerprint database can be updated in real-time.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [68]
The Introduction (page unavailable) describes building a fingerprint database offline and using it for online position matching. Searches for denoising autoencoders and real-time database updates found no passage connecting them or describing such an update. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe usage of the DNN method introduces noise interference to the input datasets, which avoids the over-fitting problem for training.
“The usage of the DNN method introduces noise interference to the input datasets, which avoids the over-fitting problem for training.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [68]
The Introduction mentions neural-network fingerprinting methods, but does not say that a DNN adds noise to input datasets or prevents overfitting. The paper’s proposed method and Conclusions instead concern RSSI-assisted mitigation of multipath interference. Searches for DNN noise injection and overfitting found no passage addressing the claim; page numbers were unavailable in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Xie, T., Jiang, H., Zhao, X., & Zhang, C.
- Affiliations
- Tsinghua University (CN)
- Corresponding author
- Jiang, Hanjun
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-09-14
- Volume
- 19
- Issue
- 18
- Pages
- 3983
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6767237
- OpenAlex
- W2972778631
- Cited by
- 32
- References
- 45
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 6 · 2023: 2 · 2022: 4 · 2021: 5 · 2020: 5 · 2019: 1
- FWCI
- 1.35
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61774092)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Physical Sciences › Engineering › Aerospace Engineering › GNSS positioning and interference
- Keywords
- time difference of arrival, received signal strength indicator, indoor positioning, mobile devices, propagation delay estimation
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