A fuzzy approach to signal integration

Leung, S.W.; Minett, James W.; Siu, Y.M.; Lee, M.K.

doi:10.1109/7.993258

Cited by 32 publications

(16 citation statements)

References 24 publications

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“…The third implies that scattering cells of very small neighbourhood mean power are thrown away while scattering cells of very high neighbourhood average power are fully preserved. If There are many nonlinear maps satisfying the above three properties, for instance, the soft thresholding function and hard thresholding function in wavelet denoising [7] and the fuzzy map in [8]. Here, we construct a family of sigmoidshaped nonlinear shrinkage functions specified by noise power and shape parameter μ .…”

Section: The Denoising Methods Based On the Nonlinear Shrinkage Map (Nsm)mentioning

confidence: 98%

A novel double-pulse detection method for aircraft target in ISAR

Shui

Yan

2009

2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar

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Double-pulse or multi-pulse detectors have better performance than single pulse detectors in inverse synthetic aperture radar (ISAR) system. The more echoes we use the better performance we have if the optimum detectors are utilized. The conventional inter-period correlation processing (IPCP) utilizes the received echoes directly. In this paper, a novel triple stage double-pulse detector is derived. We propose the first stage (nonlinear shrinkage map) to realized noise removal, and the second stage to estimate range migration number. Finally, we utilize the modified IPCP receiver to improve accuracy without the increase of the compute load compared with the conventional IPCP system. The proposed receiver is compared with other conventional detectors based on measured aircraft radar data and achieves satisfied performance.

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Section: The Denoising Methods Based On the Nonlinear Shrinkage Map (Nsm)mentioning

confidence: 98%

A novel double-pulse detection method for aircraft target in ISAR

Shui

Yan

2009

2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar

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“…In the fuzzy detector proposed in [7], the membership function w is defined so that it maps the observation space to a value between 0 and 1 indicating the degree to which the test is indicative to the hypothesis 'no signal' and 'signal'. The membership function corresponding to the false alarm space was defined as 2 ( ) Pr( (0, )) w y…”

Section: Fuzzy Ca-cfarmentioning

confidence: 99%

“…In fuzzy logic detection problem, the decision is not restricted to the presence or absence of a signal. Some work dealing with fuzzy constant false alarm rate (CFAR) detection have been reported in the literature [6,7,8] ：Leung and Minett [6] replace the fixed threshold with a soft continuous threshold implemented as a membership function. This function is chosen so that it maps the observation set to a false alarm space corresponding to the false alarm rate.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of Distributed Fuzzy CA-CFAR Detector in Pearson Distributed Clutter

Cai

Chen

et al. 2009

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

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In order to improve the radar's detection performance under Pearson distributed background clutter, the problem of designing CA-CFAR detector based on fuzzy logic is studied in this paper. A kind of distributed fuzzy CA-CFAR detection scheme including two detectors is proposed here. Two detectors compute the values of the membership function to the false alarm space from the samples of the reference cells, in the fusion centre two values are combined according to four kinds of fuzzy fusion rule to produce a global member function to estimate the background clutter power for decision. The simulation results indicate that the fuzzy CA-CFAR detector based on algebraic sum fusion rule provides more robust detection performance than conventional distributed CA CFAR based on AND or OR logic.

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“…Therefore, it would C. Alioua · F. Soltani (B) Laboratoire Signaux et Systèmes de Communication, Département d'électronique, Faculté des Sciences de l'ingénieur, Université de Constantine, Constantine 25000, Algeria e-mail: f.soltan@yahoo.fr be desirable to identify these observations in order to do some additional testing. Some works dealing with the use of fuzzy logic in signal detection have been reported in literature [1][2][3][4][5][6][7][8][9][10][11][12][13]. In [1], Leung replaced the fixed threshold with a soft continuous threshold implemented as a membership function.…”

Section: Introductionmentioning

confidence: 99%

On the modelling of uncertainty in radar CFAR detection

Alioua

Soltani

2009

SIViP

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In this paper, we propose a model for a constant false alarm detection that uses fuzzy logic to describe the uncertainty on the decision about the presence or the absence of a target. The received signal is processed in a sequential manner so that the test is performed after each observation. Each time an observation is taken, the membership function to three regions, namely; "signal present", "signal absent" and "Uncertainty" is evaluated. Then, the received signals from different observations are combined according to some fuzzy fusion rules to obtain the global decision. The simulation results obtained showed that the bounded product rule gives the best detection performance among all the others.

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A fuzzy approach to signal integration

Cited by 32 publications

References 24 publications

A novel double-pulse detection method for aircraft target in ISAR

A novel double-pulse detection method for aircraft target in ISAR

Performance Analysis of Distributed Fuzzy CA-CFAR Detector in Pearson Distributed Clutter

On the modelling of uncertainty in radar CFAR detection

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