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2 edition of Estimation of bias errors in angle-of-arrival measurements using platform motion found in the catalog.

Estimation of bias errors in angle-of-arrival measurements using platform motion

Alex Grindlay

Estimation of bias errors in angle-of-arrival measurements using platform motion

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  • 13 Currently reading

Published by Naval Research Laboratory in Washington, D.C .
Written in English

    Subjects:
  • Radar

  • Edition Notes

    StatementA. Grindlay
    SeriesNRL report -- 8512
    ContributionsNaval Research Laboratory (U.S.). Radar Analysis Branch
    The Physical Object
    Paginationiii, 14 p. :
    Number of Pages14
    ID Numbers
    Open LibraryOL14859592M


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Estimation of bias errors in angle-of-arrival measurements using platform motion by Alex Grindlay Download PDF EPUB FB2

An algorithm has been developed to estimate the bias errors in angle-of-arrival measurements made by electromagnetic detection devices on-board a pitching and rolling platform. The algorithm assumes that continuous exact measurements of the platform's roll and pitch conditions are available.

When the roll and pitch conditions are used to transform deck-plane angular measurements Author: A. Grindlay. If the allowable bias is a constant or proportional value across the measuring interval, select Across measuring interval, and then in the Absolute edit box, type the bias in measurement units, and/or in the Relative edit box, type the bias as a percentage (suffix with % symbol).

CLSI EPA3 uses a familywise significance level so the overall significance level is a maximum of 5% regardless of the number of comparisons (for example, for 1 level the significance level is 5%, for 2 levels the significance level is 5%/2 = % for each level, for 3. Interestingly, as θ0 →∞, the unbiased and biased estimators coincide.

Using (3) and (9) the resulting minimum MSE can be shown to be MSE(θˆ b) = V θ4 0 +θ 2V (θ2 0 + V)2.(11)For |θ|≤θ0 the term in brackets is less than or equal to θ2 0 /(θ 2. Bias. The bias of an estimator H is the expected value of the estimator less the value θ being estimated: [].

Example Bias Mitigation Using Multiple Sources Evaluate All Sources of Software Size Estimate Independently then show table to minimize anchoring and other bias s t y t t y e s e s e We consider both bias and precision with respect to how well an estimator performs over many, many samples of the same size.

The average of these multiple samples is called the expected value of the estimator. Bias is a measure of how far the expected value of the estimate is from the true value of the parameter being estimated.

Precision is a measure of how similar the multiple estimates. measurement situations, precision arises from the vari-ance produced by the measurement device or procedure. The total variance then arises from the variability generated by measurement error, sample variation and estimation variance.

For example, the precision of measurements of a continuous variable depends on the resolution of the measuring.

The effects of random errors in data have been widely studied in the context of statistical inference. Errors-in-variable or ‘measurement error’ models are regression models that correct for random errors in the dependent variables [20, 21].

These errors, if uncorrected, lead to bias in the parameter. here show how you can use the formula to determine the sign of bias using basic knowledge about cor(x1,y) and cor(x1,x2). once you have the sign of the bias, you can determine if your biased slope is an upper or lower limit for the true slope.

MEASUREMENT ERROR MODELS XIAOHONG CHEN and HAN HONG and DENIS NEKIPELOV1 Key words: Linear or nonlinear errors-in-variables models, classical or nonclassical measurement errors, attenuation bias, instrumental variables, double measurements, deconvolution, auxiliary sample JEL Classification: C1, C3 1 Introduction.

Errors that contribute to bias can be present even where all equipment and standards are properly calibrated and under control. Temperature probably has the most potential for introducing this type of bias into the measurements.

For example, a constant heat source will introduce serious errors in dimensional measurements of metal objects. Testing the bias of an estimation in Matlab: To test the bias of the above mentioned estimators in Matlab, the signal model: x[n]=A+w[n] is taken as a starting point.

Here ‘A’ is a constant DC value (say for example it takes a value of ) and w[n] is a vector of random noise that follows standard normal distribution with mean=0 and. reports a novel methodology for estimating the sensor bias of three-axis field sensors (e.g.

magnetometers and accelerometers). Our approach employs three-axis angular velocity measurements from an angular-rate gyroscope to estimate the three-axis field sensor measurement bias that, when properly calibrated, can sig. Kutluyil Dogancay's research works with 2, citations and 9, reads, including: AOA Pseudolinear Target Motion Analysis in the Presence of Sensor Location Errors.

If your specimen is a random conglomerate of homogeneous grains, one can estimate the sample bias by first estimating the "containment probability". This is the probability that points located a distance L from a randomly chosen point will be in the same "grain".

In navigation applications, the presence of an unknown bias in the measurement of rate gyros is a key performance-limiting factor. In order to estimate the gyro bias and improve the accuracy of attitude measurement, we proposed a new method which uses the rotation of an inertial measurement unit, which is independent from rigid body motion.

In this tutorial, we discuss the topic of position and orientation estimation using inertial sensors. We consider two separate problem formulations. The rst is estimation of orientation only, while the other is the combined estimation of both position and orientation.

The latter is sometimes called pose estimation. Bias, Mean-Square Error, Relative Eciency Consider a population parameter for which estimation is desired. For ex-ample, could be the population mean (traditionally called µ) or the popu-lation variance (traditionally called 2).

Or it might be some other parame-ter of interest such as the population median, population mode, population. Therefore, the existing problem is to estimate the target state and the measurement bias (or and), using the measurement.

The proposed estimation procedure in the paper is summarized in Figure 2. Suppose that we focus on a time period, e.g., which is exactly the fixed interval of extended Rauch-Tung-Striebel (RTS) smoother.

In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called statistics, "bias" is an objective property of an estimator. Bias can also be measured with respect to the median, rather than the mean (expected value), in.

CLSI EPA3 Measurement Procedure Comparison and Bias Estimation Using Patient Samples; Approved Guideline - Third Edition.

This document addresses the design of measurement procedure comparison experiments using patient samples and subsequent data analysis techniques used to determine the bias between two in vitro diagnostic measurement procedures.

This paper presents a new hybrid pseudolinear estimator (PLE) for target motion analysis of a constant-velocity target in the two-dimensional plane using angle-of-arrival, time-difference-of-arrival and frequency-difference-of-arrival measurements obtained from spatially distributed stationary passive receivers.

M.N. Victorino, C. Menon, in Wearable Technology in Medicine and Health Care, Inertial Measurement Units.

Inertial Measurement Units (IMU), sensors which comprise accelerometers and gyroscopes that use microcontrollers to process its collected measurements and Bluetooth modules for system communication, are another technology growing in use for human motion tracking and analysis. One of the goals of inferential statistics is to estimate unknown population estimation is performed by constructing confidence intervals from statistical samples.

One question becomes, “How good of an estimator do we have?” In other words, “How accurate is our statistical process, in the long run, of estimating our population parameter. Biased Estimate of the variance of a set of N measurements: Unbiased Estimates of the variance of a set of N measurements:: 1 N (Xn n=1 N ∑−µˆ)2 1 N−1 (Xn n=1 N ∑−µˆ)2 and 1 N (Xn n=1 N ∑−µ)2 First estimate the mean, and use that estimate in this calculate (have lost 1 degree of freedom) Special case where the mean is.

Instead of using γ that is expressed radians, we can define the Bias Coefficient: The bias coefficient is a unit-free metric. A forecast that is always over the observed values will have a bias coefficient equal to -1, always over-forecasting, while the bias.

You can measure the biases when the MTi is laying still. Remember that the biases change, so there is no fixed value you can use to correct the bias for a longer period of time. Outputting orientation.

If not corrected and you would use only the gyroscopes to calculate the orientation, the orientation would drift because of the sensor bias. and the platform’s initial configuration (Ri 0, v i 0, p 0), the following orientation R i n, velocity vi n and position p n could be calculated by using the angular velocity and acceleration.

Kalman Filter is one of the most widely used methods in motion estimation because it is both extremely simple and. Simulations, Econometrics, Stata, R,intelligent mulit-agent systems, Psychometrics, latent modelling, maximization, statistics, quantitative methods. Previous work has created a non-linear optimization (NLO) method for calculating the most likely estimate from AOA measurements.

Two new modifications to the NLO algorithm are created and shown to correct AOA measurement errors by estimating the inherent bias and time-drift in the Inertial Measurement Unit (IMU) of the AOA sensing platform. Appendix A: Determining Accuracy and Precision of Measurements: Using Calibration to Estimate Bias and Precision Errors The write-up below is a guide for determining bias and precision errors in your measurements.

In has two parts: Part A deals with calibration of measurements relative to a. () Adaptive Compensation of Gyro Bias in Rigid-Body Attitude Estimation Using a Single Vector Measurement.

IEEE Transactions on Automatic Control() Covariance Analysis of Maximum Likelihood Attitude Estimation. Then the bias of this estimator is defined to be where E[ ] denotes expected value over the distribution, i.e.

averaging over all possible observations. An estimator is said to be unbiased if its bias is equal to zero for all values of parameter θ. There are more general notions of bias and unbiasedness. • Earthquake detection networks are starting to use inertial measurements to obtain the high-frequency content of the quake along with GNSS measurements for the lower frequencies» Tsunami prediction networks are starting to use accelerometers along with GNSS receivers Inertial Navigation Systems p To calculate the Bias one simply adds up all of the forecasts and all of the observations seperately.

We can see from the above table that the sum of all forecasts isas is the observations. Hence the average is /12 or The 3rd column sums up the errors and because the two values average the same there is no overall bias.

The Dependence of the performance on bias of emitter location and sensor trajectory is illustrated using the numerical results. Get your next full-text faster Help increase your chance of getting.

(ARMA) modeling to the residual errors. Discussion about ARMA modeling can be also found in [5]. ESTIMATION OF SLANT IONOSPHERIC DELAY RATES AND MULTIPATH IN CODE PHASE MEASUREMENTS USING LINEAR REGRESSION A.

Linear Model for Code minus Carrier Phase Measurements The basic measurements in a GPS receiver are code and carrier phase measurements. Its measurement is represented as where is the measured turn rate, is actual turn rate, is bias, and is Gaussian noise. is the signal of interest for navigation and is used to derive heading changes.

Heading is defined as the angle of the vehicle direction of motion from North. The bias is estimated and removed from to attain an accurate. Estimation bias, or simply bias, is a concept in statistical inference that relates to the accuracy of parameter estimation.

The term bias was first introduced in the statistical context by English statistician Sir Arthur L. Bowley in This entry provides the formal definition of estimation bias along with the concept of error, its. Bias, Bootstrap Estimates and dSDare respectively the boot-strap estimate of the bias, the bootstrap bias corrected estimate and the bootstrap estimation of the standard deviation; figures in parentheses are the asymptotic standard deviation () based .Reliably determining the bias of the procedure is not easy for the following reasons: If the random effects are strong then this needs a very large number of measurements.

When a limited number of measurements are made then the bias estimate will always contain a contribution from random effects, which will make the bias estimate artificially.2 m(x) =x +ε(x).(1) The symbol m(x) can be interpreted as a measurement operator of the variable order to obtain an additional independent equation for ε(x), a quantity αx+β that is a linear transformation of x is measured by the same device, where α is a scaling constant and β is a.