Estimation Of Signal Parameters By Rotational Invariance Techniques Research Articles

Identification of Joint Discrepancy in Steel Truss Bridge Using Hilbert Transform with root-MUSIC and ESPRIT Techniques

In the present paper, the first three modal frequencies obtained through recorded vibration signal of a steel truss bridge is investigated at three different vehicular speeds. The denoising and extraction of modal frequencies from vibration response signals of steel truss bridge is performed using Hilbert transform (HT) in combination with high-frequency resolution enhancing techniques. The modal frequency extracting techniques applied includes Fast Fourier transform (FFT), multiple signal classification (MUSIC), and estimation of signal parameters by rotational invariance technique (ESPRIT). The comparisons of the outcomes of HT-FFT, HT-root-MUSIC, and HT- ESPRIT are computed. It is observed that HT-FFT computed results with the low resolution which hindered in obtaining distinct modal frequencies while HT-MUSIC and HT-ESPRIT achieved the most denoised, robust and reliable outcomes. The HT-ESPRIT outperforms HT-MUSIC in the elimination of unwanted noise. Further, the application of the sliding window HT-ESPRIT method clearly shows the variation of modal frequencies with the change in vehicular speeds. The first three analytical modal frequencies of 4.56 Hz, 10.44 Hz and 16.66 Hz frequencies are compared with the experimental frequencies obtained through vibrations at 10 km/h, 20 km/h and 30 km/h vehicular speeds, respectively. The joints (also denoted as nodes) 6, 7, and 8 exhibited irregular behaviour with no or minimum frequency peaks due to increased flexibility of member at these locations.

Millimeter Wave Channel Estimation and Beam forming Techniques

Millimeter-wave(mm-wave) frequencies offer higher bandwidth communication channels. Most of the wireless communication equipment operates with 6GHZ carrier frequencies, where the millimeter-wave communication systems are operated in the frequency range of 30GHZ to 300GHZ. Channel estimation will become a difficult task when the number of antennas is more than the number of radiofrequency chains. Millimeter-wave communication has received large attention from research fraternity and industry. In this paper, we listed the evolution and advancements in antenna beam forming, at the same time we outline some of the channel estimation techniques such as Auxiliary Beam pair (ABP), Adaptive algorithm based Codebook design(AABCD), Estimation of Signal Parameters by Rotational Invariance Technique (ESPRIT).

RF-Based Noncontact Respiratory Rate Monitoring With Parametric Spectral Estimation

Respiratory rate (RR) monitoring of an adult or an infant during sleep or in steady position can be lifesaving, especially in home care systems. In this paper, we examine the potentials of wireless radio frequency (RF) signals to monitor the RR. A new noncontact RR monitoring system is proposed. The system includes a simple motion detection algorithm based on adaptive thresholding to eliminate the effects of the large-scale body movements on the RR estimation. In the proposed system, the high resolution subspace-based parametric spectral estimation approaches, estimation of signal parameters by rotational invariance technique (ESPRIT) and multiple signal classification (MUSIC), are presented as the RR estimation algorithms. According to our knowledge, the ESPRIT algorithm, which estimates the spectrum without searching, is used for the first time in this paper for RR estimation. It is shown that ESPRIT is computationally efficient and works approximately 49 times faster than the MUSIC algorithm. It is also shown with various experiments conducted with ten volunteers that the proposed noncontact RR monitoring system attains 0.13 breath per minute (bpm) error rate with the limited number of data and outperforms the periodogram method commonly used as the benchmark.

Experimental study of bubble sources localization in a water tank for oil spill detection

Since the Deepwater Horizon oil spill in 2010, there have been increasing demand for a real-time monitoring system that can detect, locate, and assess oil leakages. To this goal, we launched a three-year research project to develop a passive acoustic technique in 2017. The leaked crude oil creates underwater sound through bubble oscillations in an oil spill event. This paper focuses on studying the localization of the source of bubble-induced sounds in an experimental environment. Hydrophone arrays were deployed randomly and used to receive the acoustic signals in a 2 m × 2 m × 2 m water tank. We used spatial spectrum analysis algorithms such as Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT) to determine the direction of arrival (DOA) by a hydrophone Uniform Linear Array (ULA), and utilized the multiple 2-D angles to localize the bubbles in 3-D space. We will also investigate the sensitivity of the localization algorithm with respect to the variations of physical features such as the cluster of frequency components and noise level. Experiment results will be presented and discussed.Since the Deepwater Horizon oil spill in 2010, there have been increasing demand for a real-time monitoring system that can detect, locate, and assess oil leakages. To this goal, we launched a three-year research project to develop a passive acoustic technique in 2017. The leaked crude oil creates underwater sound through bubble oscillations in an oil spill event. This paper focuses on studying the localization of the source of bubble-induced sounds in an experimental environment. Hydrophone arrays were deployed randomly and used to receive the acoustic signals in a 2 m × 2 m × 2 m water tank. We used spatial spectrum analysis algorithms such as Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT) to determine the direction of arrival (DOA) by a hydrophone Uniform Linear Array (ULA), and utilized the multiple 2-D angles to localize the bubbles in 3-D space. We will also investigate the sensitivity of the localization algorithm with respect...

LTE receiver design and multipath analysis for navigation in urban environments

Mitigating multipath of cellular long-term evolution (LTE) signals for robust positioning in urban environments is considered. A computationally efficient receiver, which uses a phase-locked loop (PLL)–aided delay-locked loop (DLL) to track the received LTE signals, is presented. The PLL-aided DLL uses orthogonal frequency division multiplexing (OFDM)–based discriminator functions to estimate and track the time-of-arrival. The code phase and carrier phase performances in an additive white Gaussian noise (AWGN) channel are evaluated numerically. The effects of multipath on the code phase and carrier phase are analyzed, demonstrating robust multipath mitigation for high transmission LTE bandwidths. The average of the DLL discriminator functions over multiple LTE symbols is presented to reduce the pseudorange error. The proposed receiver is evaluated on a ground vehicle in an urban environment. Experimental results show a root mean square error (RMSE) of 3.17 m, a standard deviation of 1.06 m, and a maximum error of 6.58 m between the proposed LTE receiver and the GPS navigation solution over a 1.44 km trajectory. The accuracy of the obtained pseudoranges with the proposed receiver is compared against two algorithms: estimation of signal parameters by rotational invariance techniques (ESPRIT) and EKAT (ESPRIT and Kalman filter).

Low-complexity ESPRIT-based DOA estimation for MIMO radar with L-shaped array

ABSTRACTAiming at the problem of two-dimensional (2D) angles estimation for monostatic multiple-input–multiple-output radar with L-shaped array, a novel reduced-dimensional (RD) direction of arrival estimation (DOA) method based on estimation of signal parameters by rotational invariance techniques (ESPRIT) algorithm is proposed. First, through the RD matrix design and RD transformation, the higher dimensional received data can be transformed into a lower dimensional signal space. Then the signal space can be achieved via the eigen-value decomposition. Last, conjoint with the ESPRIT algorithm, 2D spatial angle parameters with automatic pairing can be jointly estimated. The proposed method can remove data redundancy of high dimensional received data at the greatest degree and enhance sensors efficiency without costing the aperture of array, with a result of better performance than 2D-ESPRIT algorithm. Moreover, compared with the multiple signal classification (MUSIC)-like algorithms, the proposed method has the better overall performance and is more available for a lower computation complexity and higher instantaneity of application. Simulation results verify that the correctness of theoretical analysis and the effectiveness of proposed algorithm.

A Wavelet-Modified ESPRIT Hybrid Method for Assessment of Spectral Components from 0 to 150 kHz

Waveform distortions are an important issue in distribution systems. In particular, the assessment of very wide spectra, that include also components in the 2–150 kHz range, has recently become an issue of great interest. This is due to the increasing presence of high-spectral emission devices like end-user devices and distributed generation systems. This study proposed a new sliding-window wavelet-modified estimation of signal parameters by rotational invariance technique (ESPRIT) method, particularly suitable for the spectral analysis of waveforms that have very wide spectra. The method is very accurate and requires reduced computational effort. It can be applied successfully to detect spectral components in the range of 0–150 kHz introduced both by distributed power plants, such as wind and photovoltaic generation systems, and by end-user equipment connected to grids through static converters, such as fluorescent lamps.

Power system low frequency oscillation mode estimation using wide area measurement systems

Oscillations in power systems are triggered by a wide variety of events. The system damps most of the oscillations, but a few undamped oscillations may remain which may lead to system collapse. Therefore low frequency oscillations inspection is necessary in the context of recent power system operation and control. Ringdown portion of the signal provides rich information of the low frequency oscillatory modes which has been taken into analysis. This paper provides a practical case study in which seven signal processing based techniques i.e. Prony Analysis (PA), Fast Fourier Transform (FFT), S-Transform (ST), Wigner-Ville Distribution (WVD), Estimation of Signal Parameters by Rotational Invariance Technique (ESPRIT), Hilbert-Huang Transform (HHT) and Matrix Pencil Method (MPM) were presented for estimating the low frequency modes in a given ringdown signal. Preprocessing of the signal is done by detrending. The application of the signal processing techniques is illustrated using actual wide area measurement systems (WAMS) data collected from four different Phasor Measurement Unit (PMU) i.e. Dadri, Vindyachal, Kanpur and Moga which are located near the recent disturbance event at the Northern Grid of India. Simulation results show that the seven signal processing technique (FFT, PA, ST, WVD, ESPRIT, HHT and MPM) estimates two common oscillatory frequency modes (0.2, 0.5) from the raw signal. Thus, these seven techniques provide satisfactory performance in determining small frequency modes of the signal without losing its valuable property. Also a comparative study of the seven signal processing techniques has been carried out in order to find the best one. It was found that FFT and ESPRIT gives exact frequency modes as compared to other techniques, so they are recommended for estimation of low frequency modes. Further investigations were also carried out to estimate low frequency oscillatory mode with another case study of Eastern Interconnect Phasor Project (EIPP) data with the seven signal processing techniques (FFT, PA, ST, WVD, ESPRIT, HHT and MPM). It was found from the simulation results of EIPP data that two low frequency oscillatory mode (0.51Hz, 0.2Hz) exist.

A comparative study of signal processing methods for structural health monitoring

In this paper four non-parametric and five parametric signal processing techniques are reviewed and their performances are compared through application to a sample exponentially damped synthetic signal with closely-spaced frequencies representing the ambient response of structures. The non-parametric methods are Fourier transform, periodogram estimate of power spectral density, wavelet transform, and empirical mode decomposition with Hilbert spectral analysis (Hilbert-Huang transform). The parametric methods are pseudospectrum estimate using the multiple signal categorization (MUSIC), empirical wavelet transform, approximate Prony method, matrix pencil method, and the estimation of signal parameters by rotational invariance technique (ESPRIT) method. The performances of different methods are studied statistically using the Monte Carlo simulation and the results are presented in terms of average errors of multiple sample analyses.

Covariance differencing‐based matrix decomposition for coherent sources localisation in bi‐static multiple‐input–multiple‐output radar

In this study, a covariance differencing-based matrix decomposition algorithm is proposed for locating coherent sources under spatially coloured noise in bi-static multiple-input–multiple-output (MIMO) radar. The method contains three steps. First, the covariance differencing technique is employed to eliminate sensor noise, especially the spatially coloured noise. Second, a block Toeplitz or block Hankel matrix is constructed for decorrelation with the covariance differenced matrix. The forward-only, backward-only and combined forward-backward block Toeplitz/Hankel matrix constructions are defined, respectively. Third, unitary estimation of signal parameters by rotational invariance techniques (ESPRIT) algorithm is applied to estimate directions-of-departure (DODs) and directions-of-arrival (DOAs) of sources. The proposed algorithm offers several advantages. First, it is more robust and provides better estimation performance than other methods. Then, the coloured noise problem is overcome in a simple and effective way. Further, the computational load is comparatively low. Simulation results demonstrate the validity of the proposed algorithm.

Some advanced parametric methods for assessing waveform distortion in a smart grid with renewable generation

Power quality (PQ) disturbances are becoming an important issue in smart grids (SGs) due to the significant economic consequences that they can generate on sensible loads. However, SGs include several distributed energy resources (DERs) that can be interconnected to the grid with static converters, which lead to a reduction of the PQ levels. Among DERs, wind turbines and photovoltaic systems are expected to be used extensively due to the forecasted reduction in investment costs and other economic incentives. These systems can introduce significant time-varying voltage and current waveform distortions that require advanced spectral analysis methods to be used. This paper provides an application of advanced parametric methods for assessing waveform distortions in SGs with dispersed generation. In particular, the Standard International Electrotechnical Committee (IEC) method, some parametric methods (such as Prony and Estimation of Signal Parameters by Rotational Invariance Technique (ESPRIT)), and some hybrid methods are critically compared on the basis of their accuracy and the computational effort required.

New ESPRIT-based method for an efficient assessment of waveform distortions in power systems

Power quality (PQ) is an important issue due to the presence of an increasing number of perturbing and susceptible loads in modern power systems. In this context PQ evaluation requires advanced methods of signal processing that should be accurate and fast. In this paper, a new, advanced, parametric method is proposed that guarantees an accurate estimation of waveform distortions with reduced computational efforts. The method consists of a modified version of the well-known, parametric, high-resolution, sliding window estimation of signal parameters by rotational invariance technique (ESPRIT) method. The main benefit of the proposed scheme is that it significantly reduces the computational effort required to analyze waveforms by evaluating the frequencies and the damping factors of the spectral components one time or, at most, only a few times. Moreover, the waveform was appropriately sampled again to increase the accuracy of the results; the sampling frequency was chosen heuristically using information about the characteristics of the waveform of the power system. Experiments were conducted and the results were compared also in synthesis data. The results showed that the proposed scheme was effective and efficient.

밀리미터파 탐색기에서 ESPRIT 기법을 이용한 탄도 미사일 탄두 식별 신호처리 기법 개발

This paper introduces a signal processing technique for discrimination of ballistic missile`s warhead. An interceptor missile to destroy the enemy`s ballistic missile requires an information on the location of missile`s warhead. In order to detect and locate the missile`s warhead, a seeker radar in the interceptor missile makes use of chirp waveform to generate high resolution range profiles(HRRPs). We applied one of the well known spectral estimation technique called ESPRIT (Estimation of Signal Parameters by Rotational Invariance Technique) to these HRRPs to estimate scattering centers on the target. Using the information on the one-dimensional(1-D) scattering centers, we can find the location of the warhead by estimating the length of the missile, Simulation results show that the proposed signal processing technique is efficient in discriminating the warhead of an ballistic missile.

Investigation to realize a computationally efficient implementation of the high-order instantaneous-moments-based fringe analysis method

Recently, a high-order instantaneous moments (HIM)-operator-based method was proposed for accurate phase estimation in digital holographic interferometry. The method relies on piece-wise polynomial approximation of phase and subsequent evaluation of the polynomial coefficients from the HIM operator using single-tone frequency estimation. The work presents a comparative analysis of the performance of different single-tone frequency estimation techniques, like Fourier transform followed by optimization, estimation of signal parameters by rotational invariance technique (ESPRIT), multiple signal classification (MUSIC), and iterative frequency estimation by interpolation on Fourier coefficients (IFEIF) in HIM-operator-based methods for phase estimation. Simulation and experimental results demonstrate the potential of the IFEIF technique with respect to computational efficiency and estimation accuracy.

Opportunity detection for OFDMA-based cognitive radio systems with timing misalignment

Accurate detection of spectrum opportunities within the frequency band of an orthogonal frequency division multiple access (OFDMA) system carries critical importance for OFDMA-based cognitive radios. In this paper, we analyze the opportunity detection performances of energy detection and ESPRIT (estimation of signal parameters by rotational invariance techniques) algorithms in the presence of timing misalignments in uplink (UL) OFDMA. For the energy detector, the statistics of subcarrier power are derived considering timing misalignments, and they are verified through computer simulations. Using these statistics, which take inter-carrier-interference (ICI) effects into account, receiver operating characteristics (ROCs) of the energy detector receiver are obtained. It is shown that energy detection has a considerably better performance than ESPRIT, especially when the subcarrier assignment changes frequently. Moreover, a closed form expression is derived for the UL-OFDMA synchronization point that minimizes the ICI. Finally, it is shown that employing resource allocation blocks with larger sizes in the primary network yields better opportunities for the cognitive radio.

Resolution of signal sources via spectral moment estimation

In different practical situations it is desired to estimate the number of signal sources and their positions in space or in frequency domain. The first problem is known as the detection or the order estimation and the second one as the resolution. For the resolution problem techniques such as nonlinear least squares (NLSM), high-order Yule-Walker method (HOYW), multiple signal classification (MUSIC), Pisarenko harmonic retrieval method, min-norm method, estimation of signal parameters by rotational invariance technique (ESPRIT), were proposed (Marple, 1987 and Stoica and Moses, 1997). All these high-resolution methods are based on the analysis of the signal covariance matrix. But the covariance matrix is not the only choice to represent the signal spectrum. In different applications (weather radars, synthetic aperture radar (SAR) signal processing, ultrasound imaging in medicine, atmospheric turbulence measurements) the signal spectrum can be modeled through its algebraic moments. Recently a number of efficient nonparametric methods have been proposed to estimate the algebraic spectral moments (Monakov, 1999). The presented paper is an attempt to solve the direction of arrival (DOA) problem via estimation of the algebraic spectral moments. A method proposed in the article is comparable in its accuracy with the MUSIC method. At the same time its computational burden is much lower. The method permits to estimate the signal power of sources easily to complete the full spectral line analysis. Additionally the method shows good robustness in situations when signal sources have noticeable spatial extend

A New Method for Estimating the Number of Harmonic Components in Noise with Application in High Resolution Radar

In order to operate properly, the superresolution methods based on orthogonal subspace decomposition, such as multiple signal classification (MUSIC) or estimation of signal parameters by rotational invariance techniques (ESPRIT), need accurate estimation of the signal subspace dimension, that is, of the number of harmonic components that are superimposed and corrupted by noise. This estimation is particularly difficult when the S/N ratio is low and the statistical properties of the noise are unknown. Moreover, in some applications such as radar imagery, it is very important to avoid underestimation of the number of harmonic components which are associated to the target scattering centers. In this paper, we propose an effective method for the estimation of the signal subspace dimension which is able to operate against colored noise with performances superior to those exhibited by the classical information theoretic criteria of Akaike and Rissanen. The capabilities of the new method are demonstrated through computer simulations and it is proved that compared to three other methods it carries out the best trade-off from four points of view, S/N ratio in white noise, frequency band of colored noise, dynamic range of the harmonic component amplitudes, and computing time.

Some Radar Imagery Results Using Superresolution Techniques

The key problem in any decision-making system is to gather as much information as possible about the object or the phenomenon under study. In the case of the radar targets, frequency and angular information is integrated to form a radar image, which has very high information content. Salient features can then be extracted in order to characterize or to classify radar targets. The quality of the reconstructed image is mainly related to the resolution performed by the radar system both in slant range and in cross range. The Fourier-based reconstruction methods are fast and robust, but they are limited in resolution and dynamic range. Subspace eigenanalysis based methods, such as multiple signal classification (MUSIC) or estimation of signal parameters by rotational invariance techniques (ESPRIT), are able to provide superresolution and to accurately recover the scattering center locations even for a small number of correlated samples. The aim of the paper is to present some results of our ongoing research on the application of these techniques for radar imagery.

Channel estimation for OFDM transmission in multipath fading channels based on parametric channel modeling

We present an improved channel estimation algorithm for orthogonal frequency-division multiplexing mobile communication systems using pilot subcarriers. This algorithm is based on a parametric channel model where the channel frequency response is estimated using an L-path channel model. In the algorithm, we employ the ESPRIT (estimation of signal parameters by rotational invariance techniques) method to do the initial multipath time delays acquisition and propose an interpath interference cancellation delay locked loop to track the channel multipath time delays. With the multipath time delays information, a minimum mean square error estimator is derived to estimate the channel frequency response. It is demonstrated that the use of the parametric channel model can effectively reduce the signal subspace dimension of the channel correlation matrix for the sparse multipath fading channels and, consequently, improve the channel estimation performance.

A subspace rotation approach to signal parameter estimation

A new approach to Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT) is described in the context of direction-of-arrival estimation, but is also applicable to other problems. The method relies on finding the underlying rotation between the common subspaces associated with an array of pairwise-matched and codirectional sensor doublets. ESPRIT has several remarkable advantages over earlier techniques such as MUSIC, and also provides asymptotically unbiased and efficient estimates.