Optimal waveforms and processing for sparse frequency UWB operation

Salzman, J.; Akamine, D.; Lefevre, R.

doi:10.1109/nrc.2001.922960

Cited by 28 publications

(14 citation statements)

References 2 publications

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“…The AR processes have been applied to model a wide category of signals, e.g., fading channels [26][27], non-cooperative narrowband interferences [28], non-cooperative radar signals [30][31], and digitally modulated signals [33]. In the cognitive radio, the primary signals are similar to the non-cooperative signals described by AR process in [26][27][28][30-31] [33].…”

Section: Formulation Of Incomplete Signal Modelmentioning

confidence: 99%

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Sequential Likelihood Ratio Test under Incomplete Signal Model for Spectrum Sensing

Chung

2013

IEEE Trans. Wireless Commun.

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Abstract-Detecting the existence of the transmitter emitting signals is an important mechanism in many applications, e.g., the spectrum sensing in the cognitive radio. In conventional detection schemes, the predefined number of samples is taken for detection and the statistics of the signals are assumed to be available in the signal model. However, under the ubiquitous fading effects and the non-cooperation of the targets, the signal statistics are not accurately obtainable at the detector. In this paper, we propose a sequential detector operating on the signal model described by the autoregressive moving average (ARMA) process without assuming known coefficients. The sequential detector for the ARMA model is derived by using the likelihood ratio test framework and the predictive distributions of the ARMA process. The novelties the proposed sequential detector include: 1) performing detection without requiring complete knowledge of the signal; 2) using smaller number of samples to reach the decision on average; and 3) allowing user-specified probabilities of detection and false alarm. We derive the approximate average number of samples required to reach the decision. The energy detector and sequential energy detector are compared with the proposed sequential detector by simulations. The results show the sequential detector uses the smaller average number of samples than the energy detector and sequential energy detector to termination.

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Section: Formulation Of Incomplete Signal Modelmentioning

confidence: 99%

“…In [29], the AR modeling of channel occupations is verified through experimental data. In [30][31], the AR model is used to describe the data collected by the synthetic aperture radar. In [32], the AR is used to model the signals emitted by the non-cooperative target, where the AR modeling is verified through experimental data.…”

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confidence: 99%

Sequential Likelihood Ratio Test under Incomplete Signal Model for Spectrum Sensing

Chung

2013

IEEE Trans. Wireless Commun.

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“…Specifically, the transmitted signal should not use certain frequency bands that have already been reserved, such as the bands for navigation and military communications; or there could exist strong emitters whose operating frequencies should be avoided. Therefore it is required that the spectral power of transmitted waveforms be small for certain frequency bands [2] [3][4] [5].…”

Section: Introductionmentioning

confidence: 99%

Waveform design with stopband and correlation constraints for cognitive radar

Stoica

2010

2010 2nd International Workshop on Cognitive Information Processing

130

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One of the main objectives of cognitive radar is to adapt the spectrum of transmit waveforms to certain needs, such as avoiding reserved frequency bands or narrowband interferences. Besides spectral requirements, good correlation properties of the transmit waveforms are also desired in specific applications, such as range compression. Moreover, practical hardware constraints usually require the transmit waveforms be unimodular (i.e. only phase-modulated). In this paper, we propose a new algorithm named SCAN (stopband cyclic algorithm new) to design unimodular sequences with spectral power suppressed in arbitrary bands and with low correlation sidelobes as well. The SCAN algorithm, which starts from random initializations, can generate many sequences possessing similarly good properties. Furthermore, the SCAN algorithm is based on FFT (fast Fourier transform) operations and thus is computationally efficient, which facilitates long-sequence design and real-time waveform update.

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“…Many bands of the frequency spectrum are protected and SAR operation is prohibited in those bands [1].…”

Section: Introductionmentioning

confidence: 99%

Coherent multi-frequency-band resolution enhancement for synthetic aperture radar

Zaugg

Long

Edwards

et al. 2009

2009 IEEE International Geoscience and Remote Sensing Symposium

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Optimal waveforms and processing for sparse frequency UWB operation

Cited by 28 publications

References 2 publications

Sequential Likelihood Ratio Test under Incomplete Signal Model for Spectrum Sensing

Sequential Likelihood Ratio Test under Incomplete Signal Model for Spectrum Sensing

Waveform design with stopband and correlation constraints for cognitive radar

Coherent multi-frequency-band resolution enhancement for synthetic aperture radar

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