The Case for Efficient and Robust RF-Based Device-Free Localization

Xu, Chenren; Firner, Bernhard; Zhang, Yanyong; Howard, Richard

doi:10.1109/tmc.2015.2493522

Cited by 45 publications

(16 citation statements)

References 31 publications

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“…Indoor positioning technologies represent a rapidly growing market, and thus are attracting significant research interest [13], [25]. While the GNSS is eminently suitable for outdoor localizations, its performance degrades drastically in an in-door environment.…”

Section: ) Wi-fi Based Indoor Localization and Activity Recognitionmentioning

confidence: 99%

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Liu

Masouros

Petropulu

et al. 2020

IEEE Trans. Commun.

1,188

602

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For the sake of enhancing the exploitation of the permanently allocated, but potentially under-utilized spectral resources, sharing the frequency bands between radar and communication systems has attracted substantial attention. More explicitly, there is increasing demand for sharing both the frequency band and the hardware platform between these two functionalities, but naturally, its success critically hinges on highquality joint sensing and communications. In this paper, we firstly overview the application scenarios and the research progress in the area of communication and radar spectrum sharing, with particular emphasis on: 1) Radar-communication coexistence; 2) Dual-functional radar-communication (DFRC) systems. In the remainder of the paper, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna aided user equipment (UE) operating in a mmWave channel, which in the meantime actively detects multiple targets. Note that part of the targets also play the role of scatterers for the communication signal. Given this framework, we then propose a novel scheme for joint target search and communication channel estimation relying on the omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design an HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the potential interference imposed on the UE signals by invoking the successive interference cancellation (SIC) technique, even when the radar and communication signals share the equivalent signal-to-noise ratio (SNR). The feasibility and the efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, our discussions are summarized by overviewing the open problems in the research field of CRSS.

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Section: ) Wi-fi Based Indoor Localization and Activity Recognitionmentioning

confidence: 99%

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Liu

Masouros

Petropulu

et al. 2020

IEEE Trans. Commun.

1,188

602

View full text Add to dashboard Cite

show abstract

“…Then they proposed another device-free localization system with particle filtering [29]. Xu et al [30] formulated the device-free localization problem with probabilistic classification approaches based on discriminant analysis and mitigated the errors caused by multipath effect. Because the fingerprinting localization method performs well in multipath environments, in this paper, we also refer to the fingerprinting localization method and propose a device-free localization system using an ANN model.…”

Section: Related Workmentioning

confidence: 99%

Device‐Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks

Sun

Zhang

Wang

et al. 2018

Wireless Communications and Mobile Computing

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Currently, localization has been one of the research hot spots in Wireless Sensors Networks (WSNs). However, most localization methods focus on the device-based localization, which locates targets with terminal devices. This is not suitable for the application scenarios like the elder monitoring, life detection, and so on. In this paper, we propose a device-free wireless localization system using Artificial Neural Networks (ANNs). The system consists of two phases. In the off-line training phase, Received Signal Strength (RSS) difference matrices between the RSS matrices collected when the monitoring area is vacant and with a professional in the area are calculated. Some RSS difference values in the RSS difference matrices are selected. The RSS difference values and corresponding matrix indices are taken as the inputs of an ANN model and the known location coordinates are its outputs. Then a nonlinear function between the inputs and outputs can be approximated through training the ANN model. In the on-line localization phase, when a target is in the monitoring area, the RSS difference values and their matrix indices can be obtained and input into the trained ANN model, and then the localization coordinates can be computed. We verify the proposed device-free localization system with a WSN platform. The experimental results show that our proposed device-free wireless localization system is able to achieve a comparable localization performance without any terminal device.

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“…[14]- [16]. However, with the growth of LBS, recently device free localization is drawing significant interest to meet some emerging application demands [8], [19], [21]. In device free localization, a transceiver free target can be localized by utilizing the feature pattern of the wireless signal that is being interfered by the presence of the subject.…”

Section: Introductionmentioning

confidence: 99%

“…Indoor localization with device free approach has been explored in various methods [8], [9], [19], [20], [22]- [24]. These methods consider either RSS value of wireless signals or only the amplitude of CSI.…”

Section: Introductionmentioning

confidence: 99%

A Multi-View Discriminant Learning Approach for Indoor Localization Using Amplitude and Phase Features of CSI

Sanam

Godrich

2020

IEEE Access

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Location Based Service (LBS) is one of the important aspects of a smart city. Accurate indoor localization plays a vital role in LBS. The ability to localize various subjects in the area of interest facilitates further ubiquitous environments. Specifically, device free localization using wireless signals is getting increased attention as human location is estimated using its impact on the surrounding wireless signals without any active device tagged with subject. In this paper, we propose MuDLoc, the first multi-view discriminant learning approach for device free indoor localization using both amplitude and phase features of Channel State Information (CSI) from multiple Access Points (APs). The same location oriented CSI data can be observed by different APs, thus generating multiple distinct even heterogeneous samples. Multi-view learning is an emerging technique in machine learning which improve performance by utilizing diversity from different view data. In MuDLoc, the localization is modeled as a pattern matching problem, where the target location is predicted based on similarity measure of CSI features of an unknown location with those of the training locations. MuDLoc implements Generalized Interview and Intra-view Discriminant Correlation Analysis (GI 2 DCA), a discriminative feature extraction approach that incorporates interview and intra-view class associations while maximizing pairwise correlations across multi-view data sets. Experimental results from two cluttered environments show that MuDLoc can estimate location with high accuracy which outperforms other benchmark approaches.

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The Case for Efficient and Robust RF-Based Device-Free Localization

Cited by 45 publications

References 31 publications

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Device‐Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks

A Multi-View Discriminant Learning Approach for Indoor Localization Using Amplitude and Phase Features of CSI

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