2015 IEEE Radar Conference (RadarCon) 2015
DOI: 10.1109/radar.2015.7131010
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Passive sensing for adaptable radar bandwidth

Abstract: A spectrum sharing technique is introduced that passively monitors the RF spectrum for sub-bands of high signal to interference plus noise ratios (SINR) within a constrained bandwidth of interest. The goal of the proposed technique is to allow the radar to maintain high levels of SINR within selected frequency sub-bands in a highly congested RF environment. A sub-band is selected for radar that maximizes SINR and minimizes the range resolution cell size, two conflicting objectives. In this paper, a spectrum se… Show more

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Cited by 28 publications
(18 citation statements)
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“…The computational complexity of the weighted average approach, as reported in [8], results in N 2 summations and (3N 2 +2N) multiplications/divisions, and is of order O(N 2 ).…”
Section: Spectrum Sensing For Radarmentioning
confidence: 99%
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“…The computational complexity of the weighted average approach, as reported in [8], results in N 2 summations and (3N 2 +2N) multiplications/divisions, and is of order O(N 2 ).…”
Section: Spectrum Sensing For Radarmentioning
confidence: 99%
“…A potential spectrum sharing solution that addresses the above radar challenges is the spectrum sensing, multi-objective optimization (SS-MO) technique originally introduced by the authors in [8]. This technique passively monitors the operating band of the radar using spectrum sensing to identify a frequency sub-band with minimal RFI.…”
Section: Introductionmentioning
confidence: 99%
“…Full details of this technique are discussed in [4,5] where it is shown that the SINR and peak-to-average-sidelobe ratio are significantly improved. A block diagram of the SS-MO technique for radar is illustrated in Fig.…”
Section: Spectrum Sensing For Radarmentioning
confidence: 99%
“…(4) is solved using a linear weighting function [4]. The boundary conditions used for the SS-MO technique are Z1,min = 0.1 and min , 2 Z = 10 MHz.…”
Section: Experiments and Analysismentioning
confidence: 99%
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