Abstract-Nyquist folding receiver (NYFR) is a new kind of interception architecture, which can simultaneously intercept wideband signals in multi-Nyquist zones with one or two analog-to-digital converters (ADCs). A parameter estimation algorithm of the linear frequency modulated (LFM) signal intercepted by an improved NYFR is presented. Firstly, the NYFR is improved by introducing a synchronous mechanism, and we denote this structure as a synchronous NYFR (SNYFR). Secondly, taking LFM as an example, the input and output noise distributions of an SNYFR are discussed. Then, a fast parameter estimation algorithm is derived from the frequency spectrum of the output signal, and an advice for the design of local oscillator signal is given. Simulations show that the parameter estimation accuracy is close to the maximum likelihood when the signal to noise ratio (SNR) is above −3 dB.
In many applications, some covariates could be missing for various
reasons. Regression quantiles could be either biased or under-powered when
ignoring the missing data. Multiple imputation and EM-based augment approach
have been proposed to fully utilize the data with missing covariates for
quantile regression. Both methods however are computationally expensive. We
propose a fast imputation algorithm (FI) to handle the missing covariates in
quantile regression, which is an extension of the fractional imputation in
likelihood based regressions. FI and modified imputation algorithms (FIIPW and
MIIPW) are compared to existing MI and IPW approaches in the simulation studies,
and applied to part of of the National Collaborative Perinatal Project
study.
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