I/Q Density-based Angle of Arrival Estimation for Bluetooth Indoor Positioning Systems

Yen, Hung-Yi; Tsai, Zhong-Ting; Chen, Yuan-Ching; Shen, Li-Hsiang; Chiu, Chun-Jie; Feng, Kai-Ten

doi:10.1109/vtc2021-spring51267.2021.9448850

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…Then, the raw I/Q sample data collected from T time steps is given by R ¼ fP 1 , P 2 , : : : , P T g, where P t ¼ fI t,1 , Q t,1 , : : : , I t,N , Q t,N g. In this way, a regular data flow is generated to the subsequent deep neural network for position estimation. While the phase differences of I/Q samples can well capture the signal direction information and are widely adopted by previous research, [9,13] the correlations of the amplitude (signal gain) of I/Q data sampled by each antenna can also provide useful information to construct unique features. Based on these considerations, we adopt the amplitude and phase difference of I/Q sample data to construct the features for each location.…”

Section: Ips With Rf Fingerprintsmentioning

confidence: 99%

“…[12] Some researchers have investigated fitting methods for I/Q sample data. [13] They developed a phase difference density-based classifier (PDDC) to choose the dominant cluster of phase difference values calculated from I/Q samples. In other studies, [12,14] the authors used the Kalman filter to deal with the noise and other errors in the measurements.…”

mentioning

confidence: 99%

“…Second, the existing fingerprint-based solutions are developed mainly based on the phase difference and RSS, [9,13] which are usually used for geometry-based algorithms. The I/Q sample data calculated from CTE based on multiantenna switching is not fully utilized.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering

Lyu,

Chan,

Hung

et al. 2023

Advanced Intelligent Systems

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Indoor positioning system (IPS) technologies have widespread applications in logistics, intelligent manufacturing, healthcare monitoring, etc. The recently released Bluetooth low‐energy (BLE) 5.1 specification enables in‐phase and quadrature‐phase (I/Q) data measurements. It allows angle of arrival estimation and becomes a natural choice for IPS implementation. Conventional BLE 5.1 IPSs use multiple anchors to provide massive redundancy to improve system robustness. It however demands effective approaches to leverage redundancy. Besides, interference due to various environmental factors can introduce severe errors to I/Q data and affect positioning accuracy. Facing these challenges, herein, a novel deep learning‐based multianchor BLE 5.1 IPS is proposed. The system aggregates measurements from multiple anchors and makes them available at regular time steps. Then, a novel attentional filtering network tailored to infer high‐quality I/Q sample data is developed and a spatial regularization loss incorporating spatial location relationships to strengthen the feature embedding discrimination is proposed. Two multianchor BLE 5.1 I/Q sample datasets are developed and released for public download. Numerical experiments are carried out to compare the proposed method with previous BLE 5.1 IPS methods and methods utilizing other radio frequency data. Results indicate that the proposed method consistently achieves submeter accuracy and significantly outperforms the state‐of‐the‐art approaches.

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Section: Ips With Rf Fingerprintsmentioning

confidence: 99%

mentioning

confidence: 99%

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Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering

Lyu,

Chan,

Hung

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…This is very different from the outdoor applications we consider, demonstrating new capabilities for this low costequipment. Most previous experimental work has also focused on the estimation of a single azimuth angle for positioning in the horizontal plane using linear arrays or pairs of linear arrays such as the Texas Instruments BOOSTXL-AOA [2], [4], [6]- [8]. An exception is [3] where both a uniform rectangular array and an array of the same type as used in this paper were tested.…”

Section: Introductionmentioning

confidence: 99%

Outdoor Navigation Using Bluetooth Angle-of-Arrival Measurements

Sollie¹,

Gryte²,

Bryne³

et al. 2022

Preprint

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<p>The introduction of direction finding in the Bluetooth standard enabled the use of antenna arrays for locating Bluetooth devices, using carrier phase measurements to estimate the direction from the array to a moving device. In this work, this feature is utilized for outdoor localization. We show how using repeated measurements from all array elements, instead of only the initial single-element reference samples as often suggested, can contribute to an improved estimate of the signal's unknown carrier frequency offset, thereby improving the direction estimation performance. To run the direction-of-arrival estimation in real-time with high angular resolution on an embedded computer we propose a pseudo-spectrum peak search strategy that combines a coarse search, where the resolution is decided based on the array's main lobe width, with a local nonlinear optimization for estimate refinement. We consider practical aspects relating to the phase sampling configuration and demonstrate direction estimation at up to 700m range with insignificant packet loss within 500m, and without significant loss of angular precision even when the received signal is near the receiver's signal strength sensitivity threshold. In an open outdoor environment, using a square antenna array with 12 elements, the azimuthal performance is found to be very consistent with range, with noise standard deviation typically around 1°. While the elevation is significantly affected by multipath at lower elevation angles, with visible disagreement between frequency channels, it is shown to be consistent with simulations of ground reflection multipath.</p>

show abstract

“…This is very different from the outdoor applications we consider, demonstrating new capabilities for this low cost-equipment. Most previous experimental work has also focused on the estimation of a single azimuth angle for positioning in the horizontal plane using linear arrays or pairs of linear arrays such as the Texas Instruments BOOSTXL-AOA [2], [4], [6]- [8]. An exception is [3] where both a uniform rectangular array and an array of the same type as used in this paper were tested.…”

Section: Introductionmentioning

confidence: 99%

Outdoor Navigation Using Bluetooth Angle-of-Arrival Measurements

et al. 2022

View full text Add to dashboard Cite

The introduction of direction finding in the Bluetooth standard enabled the use of antenna arrays for locating Bluetooth devices, using carrier phase measurements to estimate the direction from the array to a moving device. In this work, this feature is utilized for outdoor localization. We show how using repeated measurements from all array elements, instead of only the initial single-element reference samples as often suggested, can contribute to an improved estimate of the signal's unknown carrier frequency offset, thereby improving the direction estimation performance. To run the direction-of-arrival estimation in real-time with high angular resolution on an embedded computer we propose a pseudospectrum peak search strategy that combines a coarse search, where the resolution is decided based on the array's main lobe width, with a local nonlinear optimization for estimate refinement. We consider practical aspects relating to the phase sampling configuration and demonstrate direction estimation at up to 700m range with insignificant packet loss within 500m, and without significant loss of angular precision even when the received signal is near the receiver's signal strength sensitivity threshold. In an open outdoor environment, using a square antenna array with 12 elements, the azimuthal performance is found to be very consistent with range, with noise standard deviation typically around 1°. While the elevation is significantly affected by multipath at lower elevation angles, with visible disagreement between frequency channels, it is shown to be consistent with simulations of ground reflection multipath.

show abstract

I/Q Density-based Angle of Arrival Estimation for Bluetooth Indoor Positioning Systems

Cited by 6 publications

References 16 publications

Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering

Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering

Outdoor Navigation Using Bluetooth Angle-of-Arrival Measurements

Outdoor Navigation Using Bluetooth Angle-of-Arrival Measurements

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