Radar and Electronic Warfare cooperation: How to improve the system efficiency?

Kemkemian, Stephane; Nouvel-Fiani, Myriam; Chamouard, Eric

doi:10.1109/radar.2010.5494518

Cited by 4 publications

(4 citation statements)

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“…Variable space is solved by intelligent optimization algorithms such as heuristic or hyperheuristic algorithm, and then the optimal heading of UAV swarms combination is obtained by searching for the optimal heading. Finally, the optimal heading instructions of each moment are continuously distributed to other UAVs through robust airborne data link [28]. According to the engineering experience, the time delay of more than 2 seconds caused by the real-time optimization calculation of the combined heading angles and the distribution of heading instructions for each UAV is easy to reduce the accuracy and tracking stability.…”

Section: Traditional Centralized Real-time Optimization (Crto) Mementioning

confidence: 99%

Optimal Configuration Analysis of AOA Localization and Optimal Heading Angles Generation Method for UAV Swarms

et al. 2019

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In this paper, the angle-of-arrival (AOA) measurements are adapted to locate a target using the UAV swarms equipped with passive receivers. The measurement noise is considered to be target-to-receiver distance dependent. The Cramer-Rao low bound (CRLB) of the AOA localization is calculated, and the optimal deployments are explored through changing angular separations and distances. Then, a distributed collaborative autonomous generation (DCAG) method is proposed based on the deep neural network (NN). The off-line training and on-line application rules are applied to generate the optimal heading angles for the UAV swarms in the AOA localization. The simulation results show that through the DCAG method, the generated heading angles for UAV swarms enhance the localization accuracy and stability. INDEX TERMS AOA localization, distributed collaborative autonomous generation (DCAG), Cramer-Rao low bound (CRLB), deep neural network (NN).

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Section: Traditional Centralized Real-time Optimization (Crto) Mementioning

confidence: 99%

Optimal Configuration Analysis of AOA Localization and Optimal Heading Angles Generation Method for UAV Swarms

et al. 2019

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“…In modern radar countermeasure technologies, our air combat platforms will be threatened by various hostile reconnaissance radars and weapon systems. The purpose of our radar countermeasure system is to intercept, measure and identify hostile radar radiation signals and then get the information including radar technique parameters, positions, types and so on [10]. Especially judging the location information of hostile reconnaissance radars can help us analyse the composition and distribution of the enemy's weapon defence system.…”

Section: Introductionmentioning

confidence: 99%

“…Especially judging the location information of hostile reconnaissance radars can help us analyse the composition and distribution of the enemy's weapon defence system. Consequently, passive radar source localisation has numerous important military significances such as the implementation of radar warning, guiding the radar jamming, guiding the weapon fire attack and so on [10].…”

Section: Introductionmentioning

confidence: 99%

Passive radar source localisation based on PSAAA using single small size aircraft

Hao

et al. 2016

IET Radar, Sonar & Navigation

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“…Therefore, it is of great importance to identify the measured MFR emitters correctly. 7,8 At present, there are only a few public studies about MFR EID owing to the agility characteristics of MFR parameters. 9,10 In the aspect of MFR modeling, Visnevski 6 considered multifunction radars (MFRs) as stochastic discrete event systems and put forward a novel model-centric approach for MFR modeling, by utilizing the theory of formal language and syntactic pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Multi-function radar emitter identification based on stochastic syntax-directed translation schema

Liu

Sun

et al. 2014

Chinese Journal of Aeronautics

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To cope with the problem of emitter identification caused by the radar words' uncertainty of measured multi-function radar emitters, this paper proposes a new identification method based on stochastic syntax-directed translation schema (SSDTS). This method, which is deduced from the syntactic modeling of multi-function radars, considers the probabilities of radar phrases appearance in different radar modes as well as the probabilities of radar word errors occurrence in different radar phrases. It concludes that the proposed method can not only correct the defective radar words by using the stochastic translation schema, but also identify the real radar phrases and working modes of measured emitters concurrently. Furthermore, a number of simulations are presented to demonstrate the identification capability and adaptability of the SSDTS algorithm. The results show that even under the condition of the defective radar words distorted by noise, the proposed algorithm can infer the phrases, work modes and types of measured emitters correctly.

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Radar and Electronic Warfare cooperation: How to improve the system efficiency?

Cited by 4 publications

References 0 publications

Optimal Configuration Analysis of AOA Localization and Optimal Heading Angles Generation Method for UAV Swarms

Optimal Configuration Analysis of AOA Localization and Optimal Heading Angles Generation Method for UAV Swarms

Passive radar source localisation based on PSAAA using single small size aircraft

Multi-function radar emitter identification based on stochastic syntax-directed translation schema

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