Mobile Phone Passive Positioning through the Detection of Uplink Signals for Search and Rescue

Gao, Yuhui; Deng, Zhongliang; Zhang, Yao; Sun, Shihua; Li, Zhen

doi:10.3390/s19204526

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Compared with the traditional array signal processing method, the sparse representation DOA estimation algorithm requires fewer snapshots and fewer array elements. OFDM is often used in communication systems, such as WiFi, long term evolution (LTE), 5th generation mobile communication technology (5G) and unmanned aerial vehicle (UAV) video signals [ 20 , 21 , 22 , 23 , 24 , 25 ]; therefore, the DOA estimation of OFDM signal is discussed in this paper.…”

Section: Discussionmentioning

confidence: 99%

DOA Estimation Based on Weighted l1-norm Sparse Representation for Low SNR Scenarios

Zuo

Xie

Zhang

et al. 2021

Sensors

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In this paper, a weighted l1-norm is proposed in a l1-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios. The weighted matrix is determined by optimizing the orthogonality of subspace, and the weighted l1-norm is used as the minimum objective function to increase the signal sparsity. Thereby, the weighted matrix makes the l1-norm approximate the original l0-norm. Simulated results of orthogonal frequency division multiplexing (OFDM) signal demonstrate that the proposed algorithm has s narrower main lobe and lower side lobe with the characteristics of fewer snapshots and low sensitivity of misestimated signals, which can improve the resolution and accuracy of DOA estimation. Specifically, the proposed method exhibits a better performance than other works for the low SNR scenarios. Outdoor experimental results of OFDM signals show that the proposed algorithm is superior to other methods with a narrower main lobe and lower side lobe, which can be used for DOA estimation of UAV and pseudo base station.

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Section: Discussionmentioning

confidence: 99%

DOA Estimation Based on Weighted l1-norm Sparse Representation for Low SNR Scenarios

Zuo

Xie

Zhang

et al. 2021

Sensors

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show abstract

“…Additionally, different forms of signal embedding techniques for JSAC systems have been described in [76], where JSAC was considered a solution to the spectrum congestion problem. Furthermore, [77]- [79] discussed how the physical downlink shared channel (PDSCH) and physical uplink shared channel (PUSCH) can be used for sensing purposes. In [80], a LoRa-assisted point-to-point JSAC model was proposed for a remote structural health monitoring system of bolted joints in smart city environments.…”

Section: A Point-to-point Jsacmentioning

confidence: 99%

Toward Joint Radar, Communication, Computation, Localization, and Sensing in IoT

Kim

Choi

2022

IEEE Access

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Since the 1960s, joint sensing and communication (JSAC) has been proposed as an attractive technique with advantages of enhanced spectral and hardware efficiency along with low latency. However, in those old days, complex transceiver designs hindered the massive adoption of JSAC in many applications. Nevertheless, thanks to advancing wireless technologies in recent years, JSAC has recently attracted substantial attention for a wide range of civil and military applications. In particular, JSAC enables simultaneous sensing and communication functionalities with the full cooperation of both operations in shared resources such as hardware as well as radio resources (i.e., frequency, time, space and so on). Note that sensing functionality is associated with other sub-functionalities of radio-detectionand-ranging (Radar), computation, and localization which are sporadically used terms in the literature. Thus, to generalize the concept and avoid any confusion, we use the term "sensing" in the acronym JSAC as a consistent and general term associated with the other aforementioned sub-functionalities. This paper elaborates on defining sensing and communication operations and then, provides an overview with preliminaries, key latest findings, and state-of-the-art of JSAC. It also explores both existing and emerging Internet of Things (IoT) applications of JSAC. Next, it provides a new classification of JSAC technologies by taking into account not only the existing JSAC technologies but also a diverse range of technologies that allow JSAC to be used for various types of IoT applications. Eventually, this study projects future research directions and challenges of enabling JSAC in IoT.

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“…Localization of the dominant signal in rs i [n] is conducted by applying the hyperbolic time difference of arrivals (TDOA) method [30][31][32]. The method utilizes the derived arrival delay time and the geological location of pairs of microphones for localization.…”

Section: Noise Source Localizationmentioning

confidence: 99%

An in-Process Inspection System to Detect Noise Originating from within the Interior Trim Panels of Car Doors

Baek

Kim

2020

Sensors

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Car body parts are sometimes responsible for irritating noise caused by assembly defects. Typically, various types of noise are known to originate from within the interior trim panels of car doors. This noise is considered to be an important factor that degrades the emotional satisfaction of the driver of the car. This research suggests an in-process inspection system consisting of an inspection workstation and a noise detection method. The inspection workstation presses down the car door trim panel by using a pneumatic pusher while microphones record the acoustic signals directly above the door trim panel and on the four sides of the workstation. The collected signals are analyzed by the proposed noise detection method after applying noise reduction. The noise detection method determines the presence of irritating noise by using noise source localization in combination with the time difference of arrival method and the relative signal strengths. The performance of the in-process noise detection system was evaluated by conducting experiments on faulty and healthy car door trim panels.

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Mobile Phone Passive Positioning through the Detection of Uplink Signals for Search and Rescue

Cited by 6 publications

References 15 publications

DOA Estimation Based on Weighted l1-norm Sparse Representation for Low SNR Scenarios

DOA Estimation Based on Weighted l1-norm Sparse Representation for Low SNR Scenarios

Toward Joint Radar, Communication, Computation, Localization, and Sensing in IoT

An in-Process Inspection System to Detect Noise Originating from within the Interior Trim Panels of Car Doors

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