WiFi-Based Indoor Localization via Multi-Band Splicing and Phase Retrieval

Khalilsarai, Mahdi Barzegar; Stefanatos, Stelios; Wunder, Gerhard; Caire, Giuseppe

doi:10.1109/spawc.2019.8815534

Cited by 19 publications

(26 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use these properties to develop an algorithm that supports delay estimation from multiband CSI collected in arbitrary frequency bands. This is new compare to the algorithms proposed in [30], [31], [33] which are restricted to delay estimation from the CSI collected in the consecutive bands. Furthermore, the proposed algorithm is gridless and does not suffer from basis mismatch, which is different than algorithms presented in [31], [33].…”

Section: B Contributionsmentioning

confidence: 98%

“…The practical route to improved delay resolution is based on multiband channel probing [30]- [33]. Here, multiple frequency bands are used to increase the frequency aperture of CSI measurements.…”

Section: 𝑥(𝑡)mentioning

confidence: 99%

“…However, this approach does not exploit all structures present in the multiband CSI, which results in statistically inefficient estimation. In [31], [33], compressed sensing algorithms based on 1-norm regularized least squares (CS(L1)) are proposed. However, these algorithms consider the collection of CSI in consecutive bands and have limited resolution due to basis mismatch.…”

Section: 𝑥(𝑡)mentioning

confidence: 99%

“…We start by deriving the data model for multiband CSI, considering orthogonal frequency-division multiplexing (OFDM) training signals as used in WiFi networks. The first difference of our work compared to the state-of-the-art methods presented in [30], [31], [33] is the observation that by stacking the multiband CSI into Hankel matrices, the data model shows a multiple shift-invariance structure known from DOA estimation problems [37], [38]. We use these properties to develop an algorithm that supports delay estimation from multiband CSI collected in arbitrary frequency bands.…”

Section: B Contributionsmentioning

confidence: 99%

See 3 more Smart Citations

Delay Estimation for Ranging and Localization Using Multiband Channel State Information

Kazaz,

Janssen,

Romme

et al. 2021

Preprint

View full text Add to dashboard Cite

In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cramér-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from a multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.

show abstract

Section: B Contributionsmentioning

confidence: 98%

Section: 𝑥(𝑡)mentioning

confidence: 99%

Section: 𝑥(𝑡)mentioning

confidence: 99%

Section: B Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

Delay Estimation for Ranging and Localization Using Multiband Channel State Information

Kazaz,

Janssen,

Romme

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A few papers use multiple channels to locate Wi-Fi APs. A recent paper [43] performs channel impulse response (CIR) estimation by splicing measured CSI over multiple Wi-Fi bands. ToneTrack [42] is an indoor location system implemented on the WARP hardware radio platform.…”

Section: Applications Using Multiple Channelsmentioning

confidence: 99%

WiRIM: Resolution Improving Mechanism for Human Sensing With Commodity Wi-Fi

Shen

Guo

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The growing physical (PHY) layer capabilities of Wi-Fi have made it possible to use Wi-Fi signals for both communication and human sensing. Wi-Fi channel state information (CSI) in PHY layer can be obtained from commodity Wi-Fi devices. As CSI can detect the minute environment changes that alter signal propagation, it is thus capable of capturing the subtle human activities to provide cost-effective and easy-to-use human sensing. However, the limited bandwidth of each individual Wi-Fi channel fundamentally constrains the resolution of signals, resulting in poor performance of human sensing. In this paper, we present WiRIM, a resolution improving mechanism for Wi-Fi based human sensing. We design a channel switching and aggregation algorithm to extend the effective bandwidth of commodity Wi-Fi signals and improve the performance and efficiency of human sensing applications. With aggregated CSI, WiRIM constructs feature images which contain rich frequency, temporal and spatial characteristics, and then uses a deep learning method to process the extracted features. We propose a cross-location human activity recognition (CLHAR) scenario as a case study. The CLHAR scenario requires a high enough resolution of the Wi-Fi signals to accurately recognize different activities under the interference of tiny changes in human location. The experiments demonstrate the generality and effectiveness of the proposed mechanism.

show abstract