Corey Cooke

This report provides an initial discussion of the potential impact to safety-related/important-to-safety (SR/ITS) systems by the possible expanded use of wireless technologies. One aspect of this discussion is focused on a review of Regulatory Guide (RG) 1.180 Rev. 2 to determine whether any revisions are necessary to reflect the current state of practice in evaluating and protecting instrumentation and control (I&C) systems and components from electromagnetic interference (EMI) or radiofrequency interference (RFI) on safety and nonsafety I&C. Electromagnetic compatibility (EMC)3 is the ability of a device or system to function satisfactorily in its electromagnetic (EM) environment, which is achieved by limiting the unintentional generation, propagation, and reception of EM energy that may cause unwanted effects such as EMI. The goal of EMC is to ensure the correct operation of different types of equipment in a common EM environment.

The U.S. Department of Energy's Office of Scientific and Technical Information

The U.S. Department of Energy's Office of Scientific and Technical Information

Bello’s stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense stationary uncorrelated scattering (WSSUS) models. WSSUS models separate the channel from the effect of the waveform and receive filter, making it an ideal formulation for waveform design problems. Of particular interest in the radar waveform design community is the ability to suppress unwanted backscatter from the earth’s surface, known as clutter. Various methods for estimating WSSUS system functions have been studied in the literature, but to date no analytic expressions for radar surface clutter range-Doppler scattering functions exist. In this work we derive a frequency-selective generalization of the Jakes Doppler spectrum model, which is widely used in the wireless communications literature, adapt it for use in radar problems, and show how the maximum entropy method can be used to extend this model to account for internal clutter motion. Validation of the spectral and stationarity properties of the proposed model against a subset of the Australian Ingara sea clutter database is performed, and good agreement is shown.

Bello's stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense stationary uncorrelated scattering (WSSUS) models. WSSUS models separate the channel from the effect of the waveform and receive filter, making it an ideal formulation for waveform design problems. Of particular interest in the radar waveform design community is the ability to suppress unwanted backscatter from the earth's surface, known as clutter. Various methods for estimating WSSUS system functions have been studied in the literature, but to date, no analytic expressions for radar surface clutter range-Doppler scattering functions exist. In this work we derive a wideband generalization of the Jakes Doppler spectrum model, which is widely used in the wireless communications literature, adapt it for use in radar problems, and show how the maximum entropy method can be used to extend this model to account for internal clutter motion. Validation of the spectral and stationarity properties of the proposed model against a subset of the Australian Ingara sea clutter database is performed, and good agreement is shown.

Historically radar returns from the sea surface have generally been modeled in one of two ways-either a) a quasi-deterministic fashion using electromagnetic scattering theory combined with a random realization of physical sea surface based on insights from hydrodynamics or b) using a statistical model of the amplitude envelope based on assumptions about the distribution of scatterers within a resolution cell visible from a given observation angle. However, these models usually suffer from either excessive computational and/or conceptual complexity or have highly restrictive regimes of applicability. In this work we present an alternative characterization of sea clutter returns utilizing statistical linear time-varying system theory in an attempt to provide a more general model that works in a wider variety of circumstances while retaining a competitively low computational burden. It is hoped that the compactness of this mathematical representation will facilitate more rapid development of effective clutter mitigation techniques in the future.

Due to the complex nature and scale of radar system simulations, especially when many entities are involved, many components of subsystems are simplified to make the analysis tractable. However, this loss in fidelity can have adverse effects that are unexpected, especially when it comes to detection probabilities and target state estimation. In the RF environment in which future radar systems will coexist, the scarcity of available RF spectrum will force the radars to increasingly coexist with existing systems. In this work we survey the issues relevant to RF amplifier modeling and show that the frequently-applied simplifying assumption of amplifier linearity can have drastic effects in many realistic scenarios. Amplifier modeling approaches are discussed and a practical measurement scheme is presented.

Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projections minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. A linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.

Signal source separation is an important aspect of both measurement and signature intelligence (MASINT) as well as modern commercial smart grid applications. In some MASINT scenarios, real-time signal processing is required in a small physical form factor, which can become prohibitive with the high sample rates required of modern signal identification algorithms. We develop a new Fast Fourier Transform (FFT) algorithm, the Aliased FFT (AFFT), that exploits spectral redundancy and sparsity in the signal to drastically reduce the number of computations required from O(N logN), for the normal FFT, to one that approaches the idealized O(K logK) for a K-sparse signal. We also develop a signal source separation algorithm using harmonically aligned signal projections (HASP) that is useful for transforming a one-dimensional signal of interest (SOI) into a two-dimensional image in such a way to facilitate automated feature extraction. An automated feature extractor is described which can accurately achieve super-resolution far beyond that of the traditional FFT by again taking advantage of the harmonic structure present in the SOI. We demonstrate HASP and this feature extractor for the application of load disaggregation-identifying the presence of certain types of devices connected to a power system.