KF-Loc: A Kalman Filter and Machine Learning Integrated Localization System Using Consumer-Grade Millimeter-Wave Hardware

Vashist, Abhishek; Li, Maojia Patrick; Ganguly, Amlan; PD, Sai Manoj; Hochgraf, Clark; Ptucha, Raymond; Kwasinski, Alexis; Kühl, Michael

doi:10.1109/mce.2021.3101060

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Many times, SNR-based fingerprinting is also at the core of some mmWave localization works, especially in combination with machine learning and deep learning techniques. The authors of [21], [136] propose machine learning regression models for localization in warehouses. SNR information is collected from Talon AD7200 routers.…”

Section: F Rssi and Tofmentioning

confidence: 99%

“…Location information can be extremely useful in different indoor setups [19], [20]. For example, in factories and industrial environments, location information can be exploited to enhance ultra-reliable low-latency communications (URLLC) for industrial IoT and smart manufacturing [21], [22]. Accurate localization and sensing can benefit healthcare scenarios for patient tracking and lifesign/behavior monitoring, help people navigate in indoor areas, provide trajectory suggestions through relevant waypoints in museums, malls, and company headquarters, as well as support missioncritical applications such as disaster relief and indoor security.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

Shastri

Valecha

Bashirov

et al. 2022

IEEE Commun. Surv. Tutorials

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The commercial availability of low-cost millimeterwave (mmWave) communication and radar devices is starting to improve the adoption of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifthgeneration (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices.This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We overview key concepts about mmWave signal propagation and system design, detailing approaches, algorithms and applications for mmWave localization and sensing. Several dimensions are considered, including the main objectives, techniques, and performance of each work, whether they reached an implementation stage, and which hardware platforms or software tools were used.We analyze theoretical (including signal processing and machine learning), technological, and implementation (hardware and prototyping) aspects, exposing under-performing or missing techniques and items towards enabling a highly effective sensing of human parameters, such as position, movement, activity and vital signs. Among many interesting findings, we observe that device-based localization systems would greatly benefit from commercial-grade hardware that exposes channel state information, as well as from a better integration between standardcompliant mmWave initial access and localization algorithms, especially with multiple access points (APs). Moreover, more advanced algorithms requiring zero-initial knowledge of the environment would greatly help improve the adoption of mmWave simultaneous localization and mapping (SLAM). Machine learning (ML)-based algorithms are gaining momentum, but still require the collection of extensive training datasets, and do not yet generalize to any indoor environment, limiting their applicability. Manuscript received xxxx xx, xxxx . . .

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Section: F Rssi and Tofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

Shastri

Valecha

Bashirov

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…The authors of [22] used Kernel-based machine learning algorithms to forecast the positions of vehicles. An MMW positioning method combining Kalman filtering and machine learning was presented to forecast a robot's static location [23]. A guide for MMW localization and user selection using a very large antenna array and model-based neural networks was developed in [24].…”

Section: Introductionmentioning

confidence: 99%

MLP-mmWP: High-Precision Millimeter Wave Positioning Based on MLP-Mixer Neural Networks

Zheng

Huang

2023

Sensors

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Millimeter wave (MMW) communication, noted for its merit of wide bandwidth and high-speed transmission, is also a competitive implementation of the Internet of Everything (IoE). In an always-connected world, mutual data transmission and localization are the primary issues, such as the application of MMW application in autonomous vehicles and intelligent robots. Recently, artificial intelligence technologies have been adopted for the issues in the MMW communication domain. In this paper, MLP-mmWP, a deep learning method, is proposed to localize the user with respect to MMW communication information. The proposed method employs seven sequences of beamformed fingerprints (BFFs) to estimate localization, which includes line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions. As far as we know, MLP-mmWP is the first method to apply the MLP-Mixer neural network to the task of MMW positioning. Moreover, experimental results in a public dataset demonstrate that MLP-mmWP outperforms the existing state-of-the-art methods. Specifically, in a simulation area of 400 × 400 m2, the positioning mean absolute error is 1.78 m, and the 95th percentile prediction error is 3.96 m, representing improvements of 11.8% and 8.2%, respectively.

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Indoor Millimeter Wave Localization Using Multiple Self-Supervised Tiny Neural Networks

Shastri,

Garcia-Saavedra,

Casari

2024

IEEE Commun. Lett.

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KF-Loc: A Kalman Filter and Machine Learning Integrated Localization System Using Consumer-Grade Millimeter-Wave Hardware

Cited by 5 publications

References 28 publications

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

MLP-mmWP: High-Precision Millimeter Wave Positioning Based on MLP-Mixer Neural Networks

Indoor Millimeter Wave Localization Using Multiple Self-Supervised Tiny Neural Networks

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