Browsing by Subject "Cognitive radar"

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Open Access
Cognitive-radio systems for spectrum, location, and environmental awareness
(IEEE, 2010) Celebi, H.; Güvenç, I.; Gezici, Sinan; Arslan, H.
In order to perform reliable communications, a system needs to have sufficient information about its operational environment, such as spectral resources and propagation characteristics. Cognitive-radio technology has capabilities for acquiring accurate spectrum, location, and environmental information, due to its unique features such as spectrum, location, and environmental awareness. The goal of this paper is to give a comprehensive review of the implementation of these concepts. In addition, the dynamic nature of cognitive-radio systems - including dynamic spectrum utilization, transmission, the propagation channel, and reception - is discussed, along with performance limits, challenges, mitigation techniques, and open issues. The capabilities of cognitive-radio systems for accurate characterization of operational environments are emphasized. These are crucial for efficient communications, localization, and radar systems.
Open Access
Radar antenna selection for direction-of-arrival estimations
(IEEE, 2021-06-18) Atalik, Arda; Yılmaz, Mustafa; Arıkan, Orhan
Multi-antenna radars exhibit positively correlated detection performance with the number of elements utilized. The feasibility of refining antenna arrays to reduce cost of operation with only marginal loss of performance has attracted significant attention as utilizing a large number of elements may be prohibitively costly in terms of computation and power. Under cognitive radar paradigm, the goal is to choose an optimal or near optimal subset of elements from an antenna array of pre-specified geometry while meeting certain performance and cost criteria. In this work, we present optimization based selection methods for certain array geometries to select the best K element sub-array in terms of Cramér-Rao lower bound (CRB) on direction-of- arrival (DoA) estimations. Our results indicate that it is possible to reduce K up to a certain point without significant reduction in DoA estimation performance. The maximum possible reduction in K depends on the operating signal-to-noise ratio (SNR) and how much performance loss is tolerated. Thus, once the operating SNR is known, it is possible to utilize fewer array elements with slight decrease in performance.