Dazhen Gu

We present a study of power spectral density (PSD) estimation from data sampled in the time domain. This work was motivated by our recent development of digital radiometry, where radiation spectra were obtained by processing the digitally sampled signal. The PSD estimation can be generalized by a quadratic estimator and the minimization of the mean squared error (mse) of the estimator leads to the optimal window choice. The bounds of the variance and the bias are formulated in order to quantify the uncertainty associated with nonideal PSD estimation in digital signal processing (DSP). Windowed estimates of spectrum measurements are presented for comparison in terms of computational efficiency and amplitude measurement precision. A few examples of real and simulated data are shown for comparison.

Novel atomic and molecular systems for radiative thermometry |

In this work, we show that high antenna frequency selectivity can make it more challenging to accurately measure antenna efficiency in a reverberation chamber when using the two-antenna method, since electronic noise can affect the measured stirred-energy component of the S-parameters. Recognizing whether the measured antenna-efficiency estimate is significantly affected by electronic noise is not trivial. The measured S-parameters used for calculation may have high SNRs, but the antenna-efficiency estimate can still show large systematic errors due to noise. We model noise as a component of the measured stirred energy, showing that the measured reflection is much more susceptible to noise than the measured transmission. We do not focus on the origin of the noise, but rather on the effect it has on the measurement.We show the effect of noise on the overestimation of the stirred energy components of the S-parameters, which affects the two-antenna formulation more significantly than the three-antenna method one. We show that this overestimation can cause efficiency estimates calculated with the two-and three-antenna methods to be significantly over-or under-estimated and we show that uncertainties increase as well. Finally, we provide clear steps to recognize and reduce noise effects in antenna efficiency measurements.

A detailed investigation of the quotient of two independent complex random variables is presented. The numerator has a zero mean, and the denominator has a non-zero mean. A normalization step is taken prior to the theoretical developments in order to simplify the formulation. Next, an indirect approach is taken to derive the statistics of the modulus and phase angle of the quotient. That in turn enables a straightforward extension of the statistical results to real and imaginary parts. After the normalization procedure, the probability density function of the quotient is found as a function of only the mean of the random variable that corresponds to the denominator term. Asymptotic analysis shows that the quotient closely resembles a normally-distributed complex random variable as the mean becomes large. In addition, the first and second moments, as well as the approximate of the second moment of the clipped random variable, are derived, which are closely related to practical applications in complex-signal processing such as microwave metrology of scattering-parameters. Tolerance intervals associated with the ratio of complex random variables are presented.

We present a general model of noisy scattering-parameter (S-parameter) measurements performed by a vector network analyzer (VNA). The residual error of the S-parameter due to the noise is examined to appear like a complex Gaussian quotient. The statistical analysis of the residual error is given, and relevant statistical quantities are derived and discussed. Experiments were conducted on a two-port VNA to validate the noise-influenced S-parameter model. We show that the uncertainty due to the noise is often critical in S-parameter measurements, in particular for S-parameters of a small magnitude.

Tightly packaged receivers pose a challenge for noise measurements. Their only outputs are often diagnostic or benchmark information-“user data” that result from unknown processing. These include data rate test results, signal-to-noise ratio estimated by the receiver, and so on. Some of these are important gauges of communication viability that may be enshrined in performance and conformance specifications. Engineers can estimate these parameters based on standards and simplified system models, but there are few means to validate against physical measurements. We propose here a set of measurement techniques to complement and support models of system noise. The approach is founded on a semiparametric model of the noise response of a full-stack receiver. We probe this response experimentally by systematically perturbing signals and excess noise levels at the receiver input. The resulting technique is blind to protocol and implementation details. We introduce the design and implementation of some novel test capabilities required for these tests: a precision programmable excess noise source and a highly directive programmable attenuator. We also introduce a regression procedure to estimate system noise (or NF) from the controlled input conditions and summary statistics of the user data output. We also estimate uncertainty in the measurement by combining traditional methods with a Monte Carlo method that propagates random errors through the regression. Case studies demonstrate the measurement with consumer wireless networking and geolocation equipment. These include verification by repeatability testing and cross-comparison against Y-factor measurements.

Infrared (IR) emissivity is a critical parameter for modeling and predicting heat transfer by radiation. Microwave absorbing materials, having a high emissivity in the microwave spectrum, are crucial in a wide array of applications, such as electromagnetic interference mitigation, stealth technology, and microwave remote sensing and radiometer calibration. Accurate knowledge of the thermal properties of these materials is necessary for efficient design and optimization of these types of systems. Typical microwave absorbing materials consist of a dielectric epoxy material impregnated with a lossy material, such as iron or carbon. We study a novel cryogenically compatible epoxy-based absorber material that has been loaded with varying concentrations of carbonyl iron powder (CIP). We study six materials with CIP concentrations of 0%, 5%, 10%, 20%, 30%, and 50% by tap volume. We use a commercial IR camera with sensitivity in the range 7.5– $13~\mu \text{m}$ to measure the radiance of the samples and a waterbath IR blackbody at ten temperatures between about 19 °C and 45 °C. A linear Deming fitting is performed, considering uncertainties in both the measured parameters, and the slope of the linear fit is shown to be the IR emissivity, averaged over the spectral response of the camera. The emissivity ranges between 0.868 and 0.757, decreasing monotonically as a function of iron carbonyl concentration between 0% and 50%. The uncertainty of the emissivity determination method is derived and presented. The uncertainty of the presented method is shown to be no larger than 3.3% for all measured samples.