A comparison of frequency estimation techniques for high-dynamic trajectories



Publisher: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, Publisher: National Technical Information Service, distributor in Pasadena, Calif, [Springfield, Va

Written in English
Published: Downloads: 714
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Subjects:

  • Computerized simulation.,
  • Dynamic response.,
  • Global positioning system.,
  • Orbital position estimation.,
  • Signal processing.,
  • Spacecraft trajectories.

Edition Notes

StatementV.A. Vilnrotter, S. Hinedi, R. Kumar.
SeriesNASA contractor report -- NASA CR-184865.
ContributionsHinedi, Sami M., Kumar, R., Jet Propulsion Laboratory (U.S.)
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL15408193M

Three classes of estimation methods are implemented and compared in this paper. The first is the exact method, which uses the continuous-time Markov chain representation of stochastic chemical kinetics and is tractable only for a very restricted class of problems. The next class of methods is based on Markov chain Monte Carlo (MCMC) by: existing frequency estimation techniques and proposes a new technique for estimating the instantaneous frequency of signals using short sinusoid-like basis functions. Furthermore, it also shows that the proposed algorithm can be implemented in a popular embedded DSP . Eq.1) In most cases, including the examples below, all coefficients a k ≥ 0. These windows have only 2 K + 1 non-zero N-point DFT coefficients, and they are all real-valued. [F] Hann and Hamming windows Edit Main article: Hann function Hann window Hamming window, a 0 = and a 1 = The original Hamming window would have a 0 = and a 1 = The customary cosine-sum. Fast and Accurate Frequency Estimation Using Sliding DFT Anit Kumar Sahu1, Mrityunjoy Chakraborty2 Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, Kharagpur, INDIA : 1 @, 2 [email protected] Abstract—Frequency Estimation of a complex exponential isCited by: 1.

  GitHub is where people build software. More than 40 million people use GitHub to discover, fork, and contribute to over million projects.   Culled from Google Books, it contains more than five million books published between and —at a rough estimate, 4 percent of all books ever Author: Geoffrey Nunberg. This article uses simulation to compare two test equating methods under the common-item nonequivalent groups design: the frequency estimation method and the chained equipercentile method. An item response theory model is used to define the true equating criterion, simulate group differences, and generate response data. Three linear equating methods are also included for by: Estimation Theory for Engineers Roberto ogneriT 30th August 1 Applications Modern estimation theory can be found at the heart of many electronic signal processing systems designed to extract information. These systems include: Radar where the delay of the received pulse echo has to be estimated in the presence of noiseFile Size: KB.

Google Scholar Citations Book [1] S. Zhou, and Z.-H. Wang, "OFDM for Underwater Acoustic Communications," John Wiley & Sons, June (Chinese translation published in May )Theses [1] Z.-H. Wang, "Communications and Networking in Underwater Acoustic Networked Systems," Ph.D. Thesis, University of Connecticut, Aug. [2] Z.-H. Wang, "Array Signal Processing for Interference. 4. STRATEGIES FOR VARIANCE ESTIMATION The estimation of the variance of a survey statistic is complicated not only by the complexity of the sample design, as seen in the previous chapters, but also by the form of the statistic. Even with an SRS design, the variance esti-mation of some statistics requires nonstandard estimating techniques. For. Spectrum analysis, also referred to as frequency domain analysis or spectral density estimation, is the technical process of decomposing a complex signal into simpler parts. As described above, many physical processes are best described as a sum of many individual frequency components. Any process that quantifies the various amounts (e.g. amplitudes, powers, intensities) versus frequency (or. A COMPARISON OF FREQUENCY, INTERVAL, AND TIME‐SAMPLING METHODS OF DATA COLLECTION. Alan C. Repp. and frequency recording would be represented similarly by each method. Results indicated: (1) that time‐sampling provided an extremely inaccurate estimate of responding, and (2) that interval recording accurately represented responding of Cited by:

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A comparison of frequency estimation techniques for high-dynamic trajectories (SuDoc NAS ) [Vilnrotter, V.

A.] on *FREE* shipping on qualifying offers. A comparison of frequency estimation techniques for high-dynamic trajectories (SuDoc NAS )Author: V. Vilnrotter. velocity trajectory is converted to an equivalent doppler frequency trajectory as which shows the one-to-one correspondence between the velocity v (t) of the physical trajectory and the instantaneous frequency of the received signal.

Here v (t) is the doppler velocity, f denotes the carrier frequency, and c is the speed of light. Examination of Fig. l(c) reveals that the dopplerCited by: Frequency estimation techniques for high dynamic trajectories.

Abstract: A comparison is presented of four different estimation techniques applied to the problem of continuously estimating the rapidly varying parameters of a sinusoidal signal, observed in the presence of additive noise. Frequency estimates are emphasized, although phase and/or frequency rate are also estimated by some of the Cited by: A comparison is presented for four different estimation techniques applied to the problem of continuously estimating the parameters of a sinusoidal Global Positioning System (GPS) signal, observed in the presence of additive noise, under extremely high-dynamic conditions.

Frequency estimates are emphasized, although phase and/or frequency rate are also estimated by some of the algorithms. Get this from a library. A comparison of frequency estimation techniques for high-dynamic trajectories.

[V A Vilnrotter; Sami M Hinedi; R Kumar; Jet Propulsion Laboratory (U.S.)]. A comparison is presented of four different estimation techniques applied to the problem of continuously estimating the rapidly varying parameters of a sinusoidal signal, observed in the presence of additive noise.

Frequency estimates are emphasized, although phase and/or frequency rate are also estimated by some of the algorithms.

Several methods have been compared to estimate the dominant frequency of a flexible manipulator. Fast algebraic estimation techniques have proven to be faster than time-domain or FFT methods and. The paper concentrates on three frequency estimation techniques and their performances and a comparison is made between them in their relative performances.

Discover the world's research The estimation of the frequency and phase of a complex exponential in additive white Gaussian noise (AWGN) is a fundamental and well-studied problem in signal processing and communications.

A variety of approaches to this problem, distinguished primarily by estimation accuracy, computational complexity, and processing latency, have been Size: KB.

This report presents the results of comparing four different frequency estimation schemes in the presence of high dynamics at low carrier-to-noise ratios. The comparison is based on measured data from a hardware Size: 2MB. than a well known frequency estimator by [8]. It is demonstrated that by using a heuristical adjustment [2] the performance can be greatly improved.

Furthermore, references to two modern techniques are given, which both nearly attain the Cramér-Rao bound for this estimation problem. 1 Introduction The problem of achieving a precise estimation. It is shown that the high frequency dynamics is the high frequency limit of vibration theory and the low frequency limit of thermodynamics.

Two approaches to the high frequency dynamics of complex engineering structures are proposed. The first one is high frequency structural : Alexander K. Belyaev. A Review of the Frequency Estimation and Tracking Problems P.J. Kootsookos CRC for Robust and Adaptive Systems DSTO, Salisbury Site Frequency Estimation and Tracking Project Febru Abstract This report presents a concise review of some frequency estimation and frequency.

This frequency estimate results from first applying NDA coarse frequency estimation () with l 0 = 1 and N s = 4, followed by PA frequency estimation () with D = 20; both the NDA and PA frequency estimation algorithms operate on N fr = 30 consecutive frames.

Cite this entry as: () Frequency Estimation. In: Stolerman I.P. (eds) Encyclopedia of Psychopharmacology. Springer, Berlin, Heidelberg. NumOfRuns = 10; % Number of frequency estimation functions to be calculated for every different data record. % Open a new figure: h1 = figure('NumberTitle', 'off', 'Name', 'Figure Frequency estimation for a process consisting of four complex exponentials in WGN'.

*The most comprehensive text and reference book published on the subject, all the most up to date research on this subject in one place *Key computer procedures and code are provided to assist the reader with practical implementations and applications *This book brings together the main knowledge of time-frequency signal analysis and processing, (TFSAP), from theory and applications, in a user.

Adaptive Instantaneous Frequency Estimation: Techniques and Algorithms Under the requirements of PhD regulationthe above candidate presented a Final Seminar that was open to the public.

A Faculty Panel of three academics attended and reported File Size: 6MB. THE ECONOMETRICS OF HIGH FREQUENCY DATA It follows that E(^˙2 n) = ˙ 2 and Var(^˙2 n) = 2˙4 n 1; since E˜ 2 m = mand Var(˜ m) = 2m.

Hence ˙^2n is consistent for ˙2: ^˙2 n!˙2 in probability as n!1. Similarly, since ˜2 n 1 is the sum of n 1iid ˜21 random variables, by the central limit theorem we have the following convergence in. Many signals can be modelled as sums of sinusoids and noise.

However, the frequencies of the sinusoids are often unknown and must be estimated to identify the source. This book presents and analyses several practical techniques used for such estimation and for tracking slow frequency Cited by: digitalized samples of current or voltage signal are used for available frequency estimation techniques.

Generally, the voltage signal is used for frequency estimation because it is less contort than the line current. Considering the purely sinusoidal power system voltage signal, the frequency. A comparison of the frequency estimation and chained equipercentile methods under the common-item nonequivalent groups design.

Paper presented at the annual meeting of the National Council of Measurement on Education, San Francisco, CA. Google Scholar. Wright, N.K., & Dorans, N.J. Using the selection variable for matching or equating Cited by: NASA Technical Reports Server (NTRS) A comparison of frequency estimation techniques for high-dynamic trajectories.

A comparison is presented for four different estimation techniques applied to the problem of continuously estimating the parameters of a sinusoidal Global Positioning System (GPS) signal, observed in the presence of additive noise, under extremely. to rapid frequency estimation that are progressively being enabled.

Several well studied techniques for frequency estimation include the Discrete Fourier Transform (DFT), classically implemented as the Fast Fourier Transform (FFT), some variation of Least Squares (LS), and more recently, the class of Modern Spectral Analysis (MSA).

Comparison of di erent frequency estimation algorithms J.A. E-mail: contact AT June 5, Foreword This small draft aims at presenting and comparing di erent frequency esti-mation techniques.

More formally, given N successive observations of a pure sinusoidal or exponential tone, sampled at a xed frequency f s = 1=T s, and. Some important classical (non-parametric) and modern (parametric) statistical spectrum and frequency estimation algorithms are demonstrated, reproducing the examples from chapter 8 of M.

Hayes book. Namely, the following Methods are exposed:Reviews: 7. ORF Methods of Estimation – 48 and mb j = Z xjdFb(x) = 1 n Xn i=1 Xj i — emprirical moment By the law of average, the empirical moments are close to theoretical ones.

The method of moments is to solve the following estimating equations. What is Estimation. “Estimation is the process of finding an estimate, or approximation, which is a value that is usable for some purpose even if input data may be incomplete, uncertain, or unstable.”[Wiki Definition]Software Estimation Techniques.

The Estimate is prediction or a rough idea to determine how much effort would take to complete a defined task.

Estimation of Frequency, Amplitude, and Phase from the DFT of a Time Series Barry G. Quinn Abstract—In a previous paper, a frequency estimator using only three Fourier coefficients was introduced, which has asymptotic variance of order.

In this correspondence, a File Size: KB. The switching frequency of medium-voltage ac drives is limited to low values to restrain the dynamic losses of the power devices. This favors the use of synchronous optimal pulsewidth-modulation schemes that minimize the harmonic current.

It is a drawback, though, that optimal algorithms do not have a means to extract the fundamental component of the load current. High-performance torque.

The grid resolution defaults to degree. Other resolutions can be set through the menu. The trajectory frequency (F) is just the sum of the number of trajectories (T) that passed through each (i,j) grid cell divided by the total number (N) of trajectories analyzed: F i,j = Σ T i,j / N.techniques [10,11], newton-type algorithm [12], prony estimation [13], discrete Fourier transform [14–16], maxi - mum likelihood method [17], and complex prony analysis [18].

In this paper, a novel frequency estimation algorithm based on DFT and second derivative is proposed. The rest 2 presents the proposed frequency estimation by: 2.On frequency estimation and this suggests that instead of using the conjugate of Dn, Dcn = An + iBn as the appropri- ate estimate one ought to use -Dc r, if &„ -estimate of thus see that, if wn is not within o(n~') of (o0, the.