Development of a tree structured hierarchical wavelet representation of synthetic database to NCTR

Brousseau, Christian

doi:10.1109/radar.2010.5494593

Cited by 3 publications

(8 citation statements)

References 9 publications

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“…4 shows the range resolution of the LFM pulse in the case where r a = 15 km and L = 30 km for different values of θ. It is well apparent that the range resolution of the monostatic system is always better than the resolution of the bistatic one and how the bistatic range resolution is degraded because of the geometry, especially when the target approaches the baseline between the transmitter and the receiver [19]. Fig.…”

Section: Monostatic and Bistatic Range Profilesmentioning

confidence: 99%

“…Diffractive returns can occur from the antennae, the control surfaces, the actuators and the wing or tail tips and edges. Other surface discontinuities in the aircraft structure can also cause diffractive returns [10,13,19,20]. Fig.…”

Section: Range Profiles Of Real Targetsmentioning

confidence: 99%

“…Therefore the correct way to choose the weights should be to highlight those channels that exhibit the best performance in terms of range resolution. The proposed SELX rule uses the following weighting rule [22][23][24] (see (19)) obtained as in [17] but considering that the ACF is the complex ambiguity function when there is no mismatch in the velocity domain. In (19), r k , L k and θ k are the ranges from receiver to target, the baseline and the DOA for the kth channel.…”

Section: Selection Of Tx and Rx (Selx) Fusion Algorithmmentioning

confidence: 99%

“…The proposed SELX rule uses the following weighting rule [22][23][24] (see (19)) obtained as in [17] but considering that the ACF is the complex ambiguity function when there is no mismatch in the velocity domain. In (19), r k , L k and θ k are the ranges from receiver to target, the baseline and the DOA for the kth channel. The weights p k in (19) depend on the bistatic geometry and on the energy path loss through the SNR.…”

Section: Selection Of Tx and Rx (Selx) Fusion Algorithmmentioning

confidence: 99%

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Non‐cooperative target recognition in multistatic radar systems

Stinco

Greco

Gini

et al. 2014

IET Radar, Sonar & Navigation

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The ability to recognise quickly and reliably non-cooperative targets is a primary issue in modern radar systems. The use of multistatic radars has already been demonstrated to improve sensibly the recognition capacity of a radar system, through the exploitation of its inherent spatial diversity, given by the presence of multiple observation channels. This calls for the development of suitable fusion strategies for efficiently combining the signals arising from multiple channels of the network. In this study, the authors define a multistatic classification algorithm where the information on the target class is provided by the sensors of the system and the final decision is made using a fusion rule that combines the decisions coming from each channel of the radar network. A suitable selection is made to favour the channels with better performance. The numerical analysis demonstrates the superior performance of the proposed algorithm over the classical fusion rule, that takes into account only the effect of the energy path loss, in terms of probability of correct classification

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Section: Monostatic and Bistatic Range Profilesmentioning

confidence: 99%

Section: Range Profiles Of Real Targetsmentioning

confidence: 99%

Section: Selection Of Tx and Rx (Selx) Fusion Algorithmmentioning

confidence: 99%

Section: Selection Of Tx and Rx (Selx) Fusion Algorithmmentioning

confidence: 99%

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Non‐cooperative target recognition in multistatic radar systems

Stinco

Greco

Gini

et al. 2014

IET Radar, Sonar & Navigation

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“…In the case of a clustering algorithm applied to NCTR, best efficiencies are obtained for an optimum number of clusters [19]. For multiresolution hierarchical tree, problem is quite different.…”

Section: Tree Structure Designmentioning

confidence: 99%

Development of multiresolution hierarchical trees to non-cooperative target recognition

Brousseau

2012

2012 9th International Conference on Communications (COMM)

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Algorithmes de reconnaissance NCTR et parallélisation sur GPU

Boulay¹,

Gac²,

Mohammad-Djafari³

et al. 2013

Traitement du signal

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Dans cet article, nous nous sommes intéressés aux problèmes de reconnaissance noncoopérative de cibles (NCTR) en tant que problème de classification supervisée. Après une présentation du système d'acquisition des profils distance radar et du problème de reconnaissance, suivie d'une étude statistique des données, nous proposons d'utiliser un algorithme des K plus proches voisins (KPPV) dont les performances sont détaillées en fonction du nombre de voisins K, du type de distance utilisée et de la nature des données utilisées (débruitées ou non). Dans un second temps, cet algorithme a été parallélisé sur un processeur many-coeurs (GPU : Graphics Processing Unit). Les opérations arithmétiques et le modèle d'accès mémoire ont été étudiés pour obtenir la meilleure parallélisation des calculs. Enfin, nous terminons par une discussion autour des perspectives envisageables pour la méthode proposée, notamment en s'intéressant à d'autres espaces de représentation ou à d'autres méthodes de classification. ABSTRACT. In this paper, first, we present the problem of Non Cooperative Target Recognition (NCTR) as a supervised classification problem. After a presentation on the radar acquisition system of range profiles and the problem of recognition, followed by a statistical study of data, we use a classical classification method of K Nearest Neighbors (KNN) to do this classification. We explore and compare the performances of this algorithm based on the choice of the distances, the choice of K and the nature of used data (denoised or not). KNN algorithm has been executed initially on CPU with Matlab and then on GPU. Arithmetic operations and memory access pattern has been studied to get the best parallelization. Finally, we conclude with a discussion about possible perspectives for the proposed method especially by focusing on other representation spaces or other classification methods.

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Development of a tree structured hierarchical wavelet representation of synthetic database to NCTR

Cited by 3 publications

References 9 publications

Non‐cooperative target recognition in multistatic radar systems

Non‐cooperative target recognition in multistatic radar systems

Development of multiresolution hierarchical trees to non-cooperative target recognition

Algorithmes de reconnaissance NCTR et parallélisation sur GPU

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