A Framework to Optimize Radar Resource Allocation for Multi-Target Tracking in ESA Radars

Sagayaraj, M.J.; Rathi, Aparna; Veeraraghavan, S.; Nigam, Chhabi

doi:10.1109/aero.2008.4526407

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2018

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“…The approach was then successfully applied to the six benchmarking problems for radar allocation and tracking described in 21,23 . Training the model with more complicated maneuvering scenarios will further improve the performance.…”

Section: Resultsmentioning

confidence: 99%

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A Hybrid Approach to Cognition in Radars

et al. 2018

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In many engineering domains, cognition is emerging to play vital role. Cognition will play crucial role in radar engineering as well for the development of next generation radars. In this paper, a cognitive architecture for radars is introduced, based on hybrid cognitive architectures. The paper proposes deep learning applications for integrated target classification based on high-resolution radar range profile measurements and target revisit time calculation as case studies. The proposed architecture is based on the artificial cognitive systems concepts and provides a basis for addressing cognition in radars, which is inadequately explored for radar systems. Initial experimental studies on the applicability of deep learning techniques under this approach provided promising results.

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Section: Resultsmentioning

confidence: 99%

“…This new approach is computationally efficient and it completely eliminates the iterative revisit time computation used in [21][22][23] as it predicts the revisit times in single step for each target. once the neural network is trained it can be used for all targets.…”

Section: Resultsmentioning

confidence: 99%