Pulse Repetition Interval Detection using Statistical Modeling

Zade, Amin Amiri Tehrani; Pezeshk, Amir Mansour

doi:10.1145/3015166.3015170

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…[4], The probabilities of different states for 2 stagger waveforms having PRI, 𝑧 1 𝑎𝑛𝑑 𝑧 2 is found shown in fig. [5]. The gate time is considered to be less than both PRI and 𝑧 1 < 𝑧 2 Similar results can be found for 3 stagger waveform and above and are discussed in [3].…”

Section: Emitters With Variable Prisupporting

confidence: 65%

See 1 more Smart Citation

Radar Recognition Using Pulse Repetition Interval Technology

Ahmad¹,

Abd‐Alhameed²

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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Recognition of pulse repetition interval modulation (PRI) of a radar signal is important. It helps in identifying the radar's functional purpose and assists in emitter identification. This paper discusses two methods, the gate time pulse method and feature extraction, used for PRI modulation recognition. These features are based on both sequential and statistical information of the pulse intervals. The extracted features can be used for emitter recognition by feeding the information in a neural network.

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Section: Emitters With Variable Prisupporting

confidence: 65%

“…Eq. 1 is differentiated resulting in second difference time of arrival (TOA) [5] 𝑧 𝑛 = 𝑥 𝑛+1 − 𝑥 𝑛 , 𝑛 = 1,2, … . 𝑁 − 2…”

Section: A Features Based On Sequential Informationmentioning

confidence: 99%

Radar Recognition Using Pulse Repetition Interval Technology

Ahmad¹,

Abd‐Alhameed²

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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“…By combining cluster and modulo detection, the latest algorithms allow the association of more than 80% of the SIGINT data [22]. To associate the 20% radar signals remaining from processing, we can rely on human perception because humans have the ability to correlate despite noise or missing values and have the mind plasticity to overcome novelty [20].…”

Section: Related Workmentioning

confidence: 99%

Expert Evaluation of the Usability of HeloVis: A 3D Immersive Helical Visualization for SIGINT Analysis

Cantú

Duval

Grisvard

et al. 2019

Virtual Reality and Augmented Reality

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This paper presents an evaluation of HeloVis: a 3D interactive visualization that relies on immersive properties to improve user performance during SIGnal INTelligence (SIGINT) analysis. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained in the data. In this paper, we briefly recall what is SIGINT, the challenges that it brings to visual analytics, and the limitations of state of the art SIGINT tools. Then, we present HeloVis, and we evaluate its efficiency through the results of an evaluation that we have made with civil and military operators who are the expert end-users of SIGINT analysis.

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“…2-b). Concerning the data volume, as the capacities of current radars are confidential, we cannot detail this matter but if we refer to studies made a few years ago [21] and if we consider the increase of data storage capacities, we can state that the current data volume is bigger than the number of perceptible objects that is commonly defined by the number of pixels provided by the visualization device [10]. …”

Section: Datamentioning

confidence: 99%

“…By combining clustering and modulo detection, the latest algorithms allow the association of more than 80% of the SIGINT data [21]. The remaining 20%, which are still in a huge amount, are unprocessed because of two issues.…”

Section: Related Workmentioning

confidence: 99%

HeloVis: A Helical Visualization for SIGINT Analysis Using 3D Immersion

Cantú

Duval

Grisvard

et al. 2018

2018 IEEE Pacific Visualization Symposium (PacificVis)

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In this paper we present HeloVis: a 3D interactive visualization that relies on immersive properties to improve the user performance during SIGINT analysis. SIGINT, which stands for SIGnal INTelligence, is a field facing many challenges like huge amounts of data, complex data and novice users. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained into the data and to abstract from interferences such as noise or missing data. In this paper, we first present SIGINT and the challenges that it brings to visual analytics. Then, we present the existing work that is currently used in or that fits the SIGINT context. Finally, we present HeloVis, an innovative application on an immersive context that allows performing SIG-INT analysis and we present its evaluation performed with military operators who are the end-users of SIGINT analysis.

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Pulse Repetition Interval Detection using Statistical Modeling

Cited by 3 publications

References 7 publications

Radar Recognition Using Pulse Repetition Interval Technology

Radar Recognition Using Pulse Repetition Interval Technology

Expert Evaluation of the Usability of HeloVis: A 3D Immersive Helical Visualization for SIGINT Analysis

HeloVis: A Helical Visualization for SIGINT Analysis Using 3D Immersion

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