A Novel Phase-Difference Fingerprinting Technique for Localization of Unknown Emitters

Haniz, Azril; Tran, Gia Khanh; Saito, Kentaro; Sakaguchi, Kei; Takada, Jun‐ichi; Hayashi, Daisuke; Yamaguchi, Toshihiro; Arata, Shintaro

doi:10.1109/tvt.2017.2696049

Cited by 13 publications

(13 citation statements)

References 19 publications

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“…Localization accuracy was examined with illegal radios placed at a total of 1232 location on a grid with 20m spacing with 5MHz bandwidth and 30dBm Tx power. Detailed simulation parameters can be found in [48]. Figure 20 shows a comparison of the median distance error between various localization algorithms over a wide range of illegal radio frequencies.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Thirdly, to improve the pattern matching results, we require regression and interpolation of the training fingerprints in multiple domains to match the parameters of the illegal radio. In this section, we explain techniques which can overcome the problems above, which were previously introduced [47] [48], including a hybrid algorithm to combine the strengths of multiple fingerprints [35]. Simulation results in a dense urban environment are presented, followed by preliminary measurements in an open field conducted using the developed hardware prototype.…”

Section: Localization Of Radio Terminals Emitting Illegal Radio Signalsmentioning

confidence: 99%

“…These steps cannot be performed beforehand in the offline phase because the signal parameters of the illegal radio can only be estimated or known after it has appeared in the area of interest. It is difficult to give a simple estimate of the total complexity, but details regarding each step can be found in [48] [49]. Nevertheless, the complexity can be greatly reduced by employing particle filters instead of the brute-force search which requires interpolation of the whole database.…”

Section: Regression On Fingerprintsmentioning

confidence: 99%

“…Only the online phase complexity is considered here because computations in the offline phase does not affect real-time performance of the algorithms 3. Computational cost of interpolation in multiple domains is high, and more details can be found in[48] [49].…”

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confidence: 99%

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A Guide of Fingerprint Based Radio Emitter Localization Using Multiple Sensors

Tao

Haniz

Sano

et al. 2018

IEICE Trans. Commun.

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Location information is essential to varieties of applications. It is one of the most important context to be detected by wireless distributed sensors, which is a key technology in Internet-of-Things. Fingerprint-based methods, which compare location unique fingerprints collected beforehand with the fingerprint measured from the target, have attracted much attention recently in both of academia and industry. They have been successfully used for many location-based applications. From the viewpoint of practical applications, in this paper, four different typical approaches of fingerprint-based radio emitter localization system are introduced with four different representative applications: localization of LTE smart phone used for anti-cheating in exams, indoor localization of Wi-Fi terminals, localized light control in BEMS using location information of occupants, and illegal radio localization in outdoor environments. Based on the different practical application scenarios, different solutions, which are designed to enhance the localization performance, are discussed in detail. To the best of the authors' knowledge, this is the first paper to give a guideline for readers about fingerprint-based localization system in terms of fingerprint selection, hardware architecture design and algorithm enhancement. key words: Internet-of-Things, wireless distributed sensors, radio emitter localization, fingerprint, different applications, guideline, prototyping 2013, and the M.E. degree in electrical, electronic and in-formation engineering

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Localization Of Radio Terminals Emitting Illegal Radio Signalsmentioning

confidence: 99%

Section: Regression On Fingerprintsmentioning

confidence: 99%

mentioning

confidence: 99%

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A Guide of Fingerprint Based Radio Emitter Localization Using Multiple Sensors

Tao

Haniz

Sano

et al. 2018

IEICE Trans. Commun.

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“…Currently, complex and various kinds of radio signals emitted by radio transmitters have been widely used and existed in our daily life. Most communication systems used in wireless applications, such as mobile searching, emergency communications, location-based interference management, and public safety, rely on radio signals and are susceptible to *Correspondence: thdu@hebut.edu.cn School of Artificial Intelligence, Hebei University of Technology, Hongqiao, Tianjin 300130, China interference by illegal signals [2,3]. Therefore, accurate detection of the radio transmitter, as the focus of considerable research efforts, has received significant recent interest, and it is crucial to strengthen management of radio signals and realize the reasonable use of radio spectrum resources.…”

Section: Introductionmentioning

confidence: 99%

Detection of an unknown radio transmitter using an enhanced K-nearest neighbor algorithm based on virtual reference point and RSSD information

Zhang

Jiang

2019

J Wireless Com Network

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Accurate detection of the unknown radio transmitter (URT) is crucial to combat illegal occupation of radio signal resources and protect communication system from harmful signal interference. The fingerprint positioning technique based on received signal strength (RSS) is famous for requiring no extra equipment, antenna arrays, and time synchronization. However, conventional RSS-based fingerprint positioning techniques that utilize K-nearest neighbor (KNN) method are confronted with problems when the positioning target is radio transmitter with unknown emission strength and frequency. Moreover, they not only cannot realize the precise localization of the URT but also only rely on pre-set reference points in the fingerprint database. In this paper, a new KNN-based geo-location approach using received signal strength difference (RSSD) information and virtual reference point is proposed to estimate an URT location. To obtain more accurate RSSD measurements, a RSSD-based filtering method by calculating the Euclidean distance between each sampling RSSD and the average value is devised to establish the fingerprint database. To achieve higher positioning accuracy, we combine KNN technique with the virtual reference (VR) point to propose RSSD-VRKNN algorithm. The simulation results show that the proposed scheme can obtain the best positioning performance compared with the conventional KNN and weighted K-nearest neighbor (WKNN) techniques. The performance and feasibility of our proposed algorithm are verified through extensive experiments.

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Multimodal system for recording individual-level behaviors in songbird groups

Rüttimann

Rychen

Tomka

et al. 2022

Preprint

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In longitudinal observations of animal groups, the goal is to identify individuals and to reliably detect their interactive behaviors including their vocalizations. However, to reliably extract individual vocalizations from their mixtures and other environmental sounds remains a serious challenge. Promising approaches are multi-modal systems that make use of animal-borne wireless sensors and that exploit the inherent signal redundancy. In this vein, we designed a modular recording system (BirdPark) that yields synchronized data streams and contains a custom software-defined radio receiver. We record pairs of songbirds with multiple cameras and microphones and record their body vibrations with custom low-power frequency-modulated (FM) radio transmitters. Our custom multi-antenna radio demodulation technique increases the signal-to-noise ratio of the received radio signals by 6 dB and reduces the signal loss rate by a factor of 87 to only 0.03% of the recording time compared to standard single-antenna demodulation techniques. Nevertheless, neither a single vibration channel nor a single sound channel is sufficient by itself to signal the complete vocal output of an individual, with each sensor modality missing on average about 3.7% of vocalizations. Our work emphasizes the need for high-quality recording systems and for multi-modal analysis of social behavior.

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A Novel Phase-Difference Fingerprinting Technique for Localization of Unknown Emitters

Cited by 13 publications

References 19 publications

A Guide of Fingerprint Based Radio Emitter Localization Using Multiple Sensors

A Guide of Fingerprint Based Radio Emitter Localization Using Multiple Sensors

Detection of an unknown radio transmitter using an enhanced K-nearest neighbor algorithm based on virtual reference point and RSSD information

Multimodal system for recording individual-level behaviors in songbird groups

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