A Fast Enhanced Algorithm of PRI Transform

Mahdavi, Ali; Pezeshk, Amir Mansour

doi:10.1109/parelec.2011.20

Cited by 21 publications

(22 citation statements)

References 6 publications

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“…Since probability of mutual interference between two radars can be extremely low, in big radar system with large number of radars mutual interference cannot be neglected. It can be shown, that providing pulse-to-pulse changes in pulse repetition frequency (PRF) (especially pseudo-random jittering) [13], [14] can greatly reduce the probability of mutual interference to a sufficient level. In Fig.3 the probability of mutual interference in closelyspaced multiposition radar system consisting from 40 equal radars with pulse-to-pulse operating frequency jittering is shown.…”

Section: B Frequency Reallocationmentioning

confidence: 99%

Resource management in closely spaced multiposition radar systems

Vovk

Прокопенко

Stavytskyi³

et al. 2016

2016 17th International Radar Symposium (IRS)

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In this paper we consider the resource management task in closely spaced multiposition radar systems. The aim of the work is to develop an investigate approaches of avoiding resource overuse and mutual interference of the radars. We consider two levels of resource management: reallocation of active radars depending on the operational situation in area of responsibility the system to minimize used radar resources, and reallocation of frequency resources between active radars to avoid mutual interference. Fast suboptimal algorithms are proposed for both levels of optimization.

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Section: B Frequency Reallocationmentioning

confidence: 99%

Resource management in closely spaced multiposition radar systems

Vovk

Прокопенко

Stavytskyi³

et al. 2016

2016 17th International Radar Symposium (IRS)

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“…n'est pas constante (Mahdavi, Pezeshk, 2011). A titre d'exemple, si nous divisons l'intervalle des ICI [0.002 , 2] en 1000 segments selon les équations (6) et (7), le pas entre deux segments est 0.002 s. Alors, la résolution du segment k=2 serait de 50 % alors que pour le segment k=1000 elle serait de 0,1 %.…”

Section: Ash Discrétiséeunclassified

Désentrelacement de clics par analyse du rythme

Bot¹,

Gervaise²,

Bonnel³

et al. 2013

Traitement du signal

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Tous les odontocètes émettent de courtes impulsions acoustiques (clics) pour satisfaire une activité de localisation, de chasse ou de communication. Ces impulsions sont le plus souvent émises en trains rythmés. Il est très fréquent d'enregistrer simultanément les clics de plusieurs animaux. Cet article présente une méthode permettant de séparer les trains de clics enregistrés sur un unique hydrophone afin de connaître le nombre d'animaux ayant émis simultanément. La méthode proposée est testée avec succès sur des données simulées et sur des données réelles. ABSTRACT. All odontocetes produce short acoustic pulses called clicks to perform localization, hunt preys or communicate. These clicks are generally emitted in trains having a certain rhythm. Frequently, click trains from several animals are recorded simultaneously. This paper introduces a method for deinterleaving these click trains with a single hydrophone. As final results it gives the number of animals clicking simultaneously and allows the tracking of rhythm variations within each train. The proposed algorithm is successfully tested on simulated data and validated on real data.

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“…Xi et al [11] introduced the overlapping box and dynamic sequence search algorithm based on classic SDIF method, which could deinterleave the PRIjittered signals with jitter quantity less than 10%. Meanwhile, Ken'ichi and Masaaki [12] proposed an approach named PRI transformation, and its improved versions [13], [14] were proposed later. PRI transformation adopted correlation of inter-pulse with a phase factor, which was good at eliminating the interference of sub-harmonics.…”

Section: Introductionmentioning

confidence: 99%

Improved Algorithm of Radar Pulse Repetition Interval Deinterleaving Based on Pulse Correlation

Sun

Ren

et al. 2019

IEEE Access

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In the electromagnetic space, a single channel radar receiver will often intercept several periodic pulse trains radiating from the surrounding emitters simultaneously. The aim of radar pulse deinterleaving is to sort out the pulses coming from different emitters. Most traditional pulse repetition interval (PRI) deinterleaving methods are easy to sort out the pulses with small PRI fluctuations but difficult in dealing with relatively bigger fluctuation or staggered PRIs. In addition, this searching procedure can easily cause pulse omission phenomenon and even generate false pulses. In this paper, we propose an improved histogram method for PRI deinterleaving based on pulse correlation to overcome the above-mentioned shortcomings. After calculating the multi-level time difference histogram, we introduce the mean filter and interquartile range algorithm to optimize the estimated PRI values. Our method extracts the pulse pairs based on pulse correlation directly instead of searching for the pulses and then determines whether the PRI is staggered or not. The experiments on simulation data show that our method can achieve better performance on both the pulse trains of jittered PRIs and the staggered PRIs.

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A Fast Enhanced Algorithm of PRI Transform

Cited by 21 publications

References 6 publications

Resource management in closely spaced multiposition radar systems

Resource management in closely spaced multiposition radar systems

Désentrelacement de clics par analyse du rythme

Improved Algorithm of Radar Pulse Repetition Interval Deinterleaving Based on Pulse Correlation

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