Topic
White noise
About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.
Papers published on a yearly basis
Papers
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TL;DR: A general and flexible algorithm is proposed based on the majorization-minimization method with guaranteed monotonicity, lower computational complexity per iteration and/or convergence to a B-stationary point and many waveform constraints can be flexibly incorporated into the algorithm with only a few modifications.
Abstract: In this paper, we consider the joint design of both transmit waveforms and receive filters for a colocated multiple-input-multiple-output (MIMO) radar with the existence of signal-dependent interference and white noise. The design problem is formulated into a maximization of the signal-to-interference-plus-noise ratio (SINR), including various constraints on the transmit waveforms. Compared with the traditional alternating semidefinite relaxation approach, a general and flexible algorithm is proposed based on the majorization-minimization method with guaranteed monotonicity, lower computational complexity per iteration and/or convergence to a B-stationary point. Many waveform constraints can be flexibly incorporated into the algorithm with only a few modifications. Furthermore, the connection between the proposed algorithm and the alternating optimization approach is revealed. Finally, the proposed algorithm is evaluated via numerical experiments in terms of SINR performance, ambiguity function, computational time, and properties of the designed waveforms. The experiment results show that the proposed algorithms are faster in terms of running time and meanwhile achieve as good SINR performance as the the existing methods.
166 citations
TL;DR: It is found that the orientation (or direction) of the cepstral vector is less susceptible to noise perturbation than the vector norm, and a family of distortion measures based on the projection between two cEPstral vectors is proposed, which have the same computational efficiency as the band-pass cepStral distortion measure.
Abstract: Consideration is given to the formulation of speech similarity measures, a fundamental component in recognizer designs, that are robust to the change of ambient conditions. The authors focus on the speech cepstrum derived from linear prediction coefficients (the LPC cepstrum). By using some common models for noisy speech, they show analytically that additive white noise reduces the norm (length) of the LPC cepstral vectors. Empirical observations on the parameter histograms not only confirm the analytical results through the use of noise models but further reveal that at a given (global) signal-to-noise ratio (SNR), the norm reduction on cepstral vectors with larger norms is generally less than on vectors with smaller norms, and that lower order coefficients are more affected than higher order terms. In addition, it is found that the orientation (or direction) of the cepstral vector is less susceptible to noise perturbation than the vector norm. As a consequence of the above results, a family of distortion measures based on the projection between two cepstral vectors is proposed. The new measures have the same computational efficiency as the band-pass cepstral distortion measure. >
166 citations
TL;DR: In this paper, the authors discuss some properties of order patterns both in deterministic and random orbit generation and show that forbidden patterns are robust against noise and disintegrate with a rate that depends on the noise level.
Abstract: In this letter we discuss some properties of order patterns both in deterministic and random orbit generation. As it turns out, the orbits of one-dimensional maps have always forbidden patterns, i.e., order patterns that cannot occur, in contrast with random time series, in which any order pattern appears with probability one. However, finite random sequences may exhibit "false" forbidden patterns with non-vanishing probability. In this case, forbidden patterns decay with the sequence length, thus unveiling the random nature of the sequence. Last but not least, true forbidden patterns are robust against noise and disintegrate with a rate that depends on the noise level. These properties can be embodied in a simple method to distinguish deterministic, finite time series with very high levels of observational noise, from random ones. We present numerical evidence for white noise.
166 citations
TL;DR: In this article, a version of the Wong-Zakai theorem for one-dimensional parabolic nonlinear stochastic PDEs driven by space-time white noise is proved.
Abstract: We prove a version of the Wong-Zakai theorem for one-dimensional parabolic nonlinear stochastic PDEs driven by space-time white noise. As a corollary, we obtain a detailed local description of solutions.
166 citations
TL;DR: In this article, asymptotic expansions for the exponential growth rate (Lyapunov exponent) and rotation number of the random oscillator were constructed when the noise is large, small, rapidly varying or slowly varying.
Abstract: We construct asymptotic expansions for the exponential growth rate (Lyapunov exponent) and rotation number of the random oscillator when the noise is large, small, rapidly varying or slowly varying. We then apply our results to problems in the stability of the random oscillator, the spectrum of the one-dimensional random Schr6dinger operator and wave propagation in a one-dimensional random medium.
165 citations