2007Unpublished venueRequires access

Binary Chirp Signals in - Mixture Noise: Coherent and Noncoherent Detection

Abdullah Kadri, Raveendra K. Rao

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Abstract

The detection of weak binary chirp signals in isin-mixture noise environment is addressed. Both coherent and noncoherent reception cases are treated. Optimum weak signal receiver structures are derived and analyzed. Closed-form expressions for bit error probabilities of these receivers are derived, and the theoretical bit error rates are illustrated as a function of the modulation parameters, signal-to-noise ratio, detection sample size, and parameters of the first-order probability density of the noise. A comparison of the performances of coherent and noncoherent receivers is also presented. The noise environment is modeled as Gaussian-Gaussian mixture model.

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What this paper is about

The detection of weak binary chirp signals in isin-mixture noise environment is addressed. Both coherent and noncoherent reception cases are treated. Optimum weak signal receiver structures are derived and analyzed. Closed-form expressions for bit error probabilities of these receivers are derived, and the theoretical bit error rates are illustrated as a function of the modulation parameters, signal-to-noise ratio, detection sample size, and parameters of the first-order probability density of the noise. A comparison of the performances of coherent and noncoherent receivers is also presented. The noise environment is modeled as Gaussian-Gaussian mixture model.

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Available abstract

The detection of weak binary chirp signals in isin-mixture noise environment is addressed. Both coherent and noncoherent reception cases are treated. Optimum weak signal receiver structures are derived and analyzed. Closed-form expressions for bit error probabilities of these receivers are derived, and the theoretical bit error rates are illustrated as a function of the modulation parameters, signal-to-noise ratio, detection sample size, and parameters of the first-order probability density of the noise. A comparison of the performances of coherent and noncoherent receivers is also presented. The noise environment is modeled as Gaussian-Gaussian mixture model.

Key concepts: Gaussian noise, Chirp, Noise (video), Binary number, Additive white Gaussian noise, Signal-to-noise ratio (imaging), Probability density function, Algorithm

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