A sub-Nyquist co-prime sampling music spectral approach for natural frequency identification of white-noise excited structures

Motivated by practical needs to reduce data transmission payloads in wireless sensors for vibration-based monitoring of civil engineering structures, this paper proposes a novel approach for identifying resonant frequencies of white-noise excited structures using acceleration measurements acquired at rates significantly below the Nyquist rate. The approach adopts the deterministic co-prime sub-Nyquist sampling scheme, originally developed to facilitate telecommunication applications, to estimate the autocorrelation function of response acceleration time-histories of low-amplitude white-noise excited structures treated as realizations of a stationary stochastic process. This is achieved without posing any sparsity conditions to the signals. Next, the standard MUSIC algorithm is applied to the estimated autocorrelation function to derive a denoised super-resolution pseudo-spectrum in which natural frequencies are marked by prominent spikes. The accuracy and applicability of the proposed approach is numerically assessed using computer-generated noise-corrupted acceleration time-history data obtained by a simulation-based framework pertaining to a white-noise excited structural system with two closely-spaced modes of vibration carrying the same amount of energy, and a third isolated weakly excited vibrating mode. All three natural frequencies are accurately identified by sampling at as low as 78% below Nyquist rate for signal to noise ratio as low as 0dB (i.e., energy of additive white noise equal to the signal energy), suggesting that the proposed approach is robust and noise-immune while it can reduce data transmission requirements in acceleration wireless sensors for natural frequency identification of engineering structures

Development and Demonstration of a TDOA-Based GNSS Interference Signal Localization System

Background theory, a reference design, and demonstration results are given for a Global Navigation Satellite System (GNSS) interference localization system comprising a distributed radio-frequency sensor network that simultaneously locates multiple interference sources by measuring their signals’ time difference of arrival (TDOA) between pairs of nodes in the network. The end-to-end solution offered here draws from previous work in single-emitter group delay estimation, very long baseline interferometry, subspace-based estimation, radar, and passive geolocation. Synchronization and automatic localization of sensor nodes is achieved through a tightly-coupled receiver architecture that enables phase-coherent and synchronous sampling of the interference signals and so-called reference signals which carry timing and positioning information. Signal and crosscorrelation models are developed and implemented in a simulator. Multiple-emitter subspace-based TDOA estimation techniques are developed as well as emitter identification and localization algorithms. Simulator performance is compared to the CramérRao lower bound for single-emitter TDOA precision. Results are given for a test exercise in which the system accurately locates emitters broadcasting in the amateur radio band in Austin, TX.Aerospace Engineering and Engineering Mechanic

Noise Measurements of the VAIIPR Fan

This final report has been prepared by Honeywell Aerospace, Phoenix, Arizona, a unit of Honeywell International, Inc., documenting work performed during the period September 2004 through November 2005 for the National Aeronautics and Space Administration (NASA) Glenn Research Center, Cleveland, Ohio, under the Revolutionary Aero-Space Engine Research (RASER) Program, Contract No. NAS3- 01136, Task Order 6, Noise Measurements of the VAIIPR Fan. The NASA Task Manager was Dr. Joe Grady, NASA Glenn Research Center, Mail Code 60-6, Cleveland, Ohio 44135. The NASA Contract Officer was Mr. Albert Spence, NASA Glenn Research Center, Mail Code 60-6, Cleveland, Ohio 44135. This report focuses on the evaluation of internal fan noise as generated from various inflow disturbances based on measurements made from a circumferential array of sensors located near the fan and sensors upstream of a serpentine inlet

Estimation of Sparse MIMO Channels with Common Support

We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are individually sparse and at the same time share a common support set. Since the underlying physical channels are inherently continuous-time, we propose a parametric sparse estimation technique based on finite rate of innovation (FRI) principles. Parametric estimation is especially relevant to MIMO communications as it allows for a robust estimation and concise description of the channels. The core of the algorithm is a generalization of conventional spectral estimation methods to multiple input signals with common support. We show the application of our technique for channel estimation in OFDM (uniformly/contiguous DFT pilots) and CDMA downlink (Walsh-Hadamard coded schemes). In the presence of additive white Gaussian noise, theoretical lower bounds on the estimation of SCS channel parameters in Rayleigh fading conditions are derived. Finally, an analytical spatial channel model is derived, and simulations on this model in the OFDM setting show the symbol error rate (SER) is reduced by a factor 2 (0 dB of SNR) to 5 (high SNR) compared to standard non-parametric methods - e.g. lowpass interpolation.Comment: 12 pages / 7 figures. Submitted to IEEE Transactions on Communicatio

Xampling: Signal Acquisition and Processing in Union of Subspaces

We introduce Xampling, a unified framework for signal acquisition and processing of signals in a union of subspaces. The main functions of this framework are two. Analog compression that narrows down the input bandwidth prior to sampling with commercial devices. A nonlinear algorithm then detects the input subspace prior to conventional signal processing. A representative union model of spectrally-sparse signals serves as a test-case to study these Xampling functions. We adopt three metrics for the choice of analog compression: robustness to model mismatch, required hardware accuracy and software complexities. We conduct a comprehensive comparison between two sub-Nyquist acquisition strategies for spectrally-sparse signals, the random demodulator and the modulated wideband converter (MWC), in terms of these metrics and draw operative conclusions regarding the choice of analog compression. We then address lowrate signal processing and develop an algorithm for that purpose that enables convenient signal processing at sub-Nyquist rates from samples obtained by the MWC. We conclude by showing that a variety of other sampling approaches for different union classes fit nicely into our framework.Comment: 16 pages, 9 figures, submitted to IEEE for possible publicatio

A probabilistic approach to analyse Blade Tip Timing data of non-synchronous vibrations under constant rotor speed

Blades are among the most critical components of turbomachines, their monitoring and characterization undergoing working conditions are fundamental for the insiders, both for preventing eventual breakage and for optimising future development. Two approaches are possible for monitoring rotor blade vibrations: a traditional one based on the use of strain gauges and another one called Blade Tip Timing (BTT). BTT is an indirect, non-intrusive simple and robust measurement method, but the processing of such data is not easy because they are often subsampled with respect to the Nyquist limit and the ordering of the samples is not unique. In this work the focus is on multi component non-synchronous vibrations, typical for example of flutter, measured at constant rotor speed by a BTT system. These data are organized into batches of fixed length called snapshots and they are interpreted as members of a random vector. When the signal contains only one harmonic component the frequency can be determined using a method here described and called Harmonic Matching (HM). While for the analyses of multi harmonic component vibrations a probabilistic approach capable of separating and identify the components using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed. For the development of data processing methods, the possibility of having controllable and repeatable data is fundamental, for this reason two test rigs of increasing complexity have been developed and are here described

An improved multi-variate empirical mode decomposition method towards system identification of structures

Structural health monitoring (SHM) plays a key role towards condition assessment of large-scale civil structures using modern sensing technology. Once the rich vibration data is collected, important system information is extracted from the data and sub- sequently such information is used for necessary decision making including adopting maintenance, retro tting or control strategies. System identi cation is one of the key steps in SHM where unknown system information of the structures is estimated based on the response measurements. However, depending on excitation characteristics or system behavior, vibration measurements become complicated where traditional methods are unable to accurately analyze the data. In this thesis, Multivariate Empirical Mode Decomposition (MEMD) method is ex- plored to undertake ambient system identi cation of structures using the multi-sensor vibration data. Due to inherent sifting operation of EMD, the traditional MEMD re- sults into mode-mixing that causes signi cant inaccuracy in structural modal identi - cation. In this research, Independent Component Analysis (ICA) method is integrated with the MEMD to alleviate mode mixing in the resulting modal responses. The pro- posed hybrid MEMD method is veri ed using a suite of numerical, experimental and full-scale studies (e.g., a high-rise tower in China and a long-span bridge in Canada) considering several practical applications including low energy modes, closely spaced frequencies and measurement noise in real-life buildings and bridges. The results show signi cantly improved performance of the proposed method compared to the standard EMD method and therefore, the proposed method can be considered as a robust ambient modal identi cation method for exible structures