Quantitative measures of comparison between antenna pattern data sets

McCormick, J.; Gregson, Stuart; Parini, C.G.

doi:10.1049/ip-map:20045005

Cited by 8 publications

(8 citation statements)

References 1 publication

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“…The logarithmic difference is given from as and using [27, (2.2-11)] the density function of can be expressed from the density function of as (23) where denotes the possible values of the logarithmic difference . From this expression for the density function for the logarithmic difference the mean and standard deviation can be found as (24) (25) and the th percentile of the absolute difference, , determined from (26) In Figs. 10 and 11 these statistical measures are illustrated for different noise levels in pattern 1 and 2.…”

Section: Methods For Comparisonsmentioning

confidence: 99%

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Comparison of Antenna Measurement Facilities With the DTU-ESA 12 GHz Validation Standard Antenna Within the EU Antenna Centre of Excellence

Pivnenko

Pallesen

Breinbjerg

et al. 2009

IEEE Trans. Antennas Propagat.

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Abstract-The primary objective of many antenna measurement facilities is to provide a specified high accuracy of the measured data. The validation of an antenna measurement facility is the process of proving that such a specified accuracy can be achieved. Since this constitutes a very challenging task, antenna measurement accuracy has been the subject of much research over many years and a range of useful measures have been introduced.

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Section: Methods For Comparisonsmentioning

confidence: 99%

“…Another measure of merit, specifically designed for comparison of the similarity of radiation patterns, has been proposed in [25] for multiband antennas. Also, recently an extensive discussion, based on simulated data, on quantitative measures for comparison of antenna patterns has been published in [26].…”

Section: Measures Of Meritmentioning

confidence: 99%

Comparison of Antenna Measurement Facilities With the DTU-ESA 12 GHz Validation Standard Antenna Within the EU Antenna Centre of Excellence

Pivnenko

Pallesen

Breinbjerg

et al. 2009

IEEE Trans. Antennas Propagat.

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“…As a result, if elementary statistical calculations are used for the comparison purposes, a parametric expression which utilizes the concept of weights by weighing different comparison metrics would have to be implemented [1]. Even if such an approach was invoked, the process still would not provide an objective metric since the values of the weights were determined at the user's discretion.…”

Section: Introductionmentioning

confidence: 99%

Objective Metric for Antenna Patterns Comparison Using Mahalanobis-Taguchi-Gram-Schmidt Method

Bekaryan

Song

Hsu

et al. 2007

2007 IEEE 66th Vehicular Technology Conference

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The application of the modified MTGS method offers a practical alternative to elementary statistical calculation for generating objective quantitative metric to measure the similarity between two data sets. Since the MTGS method can capture many degrees of variation between two data sets and discriminate between data sets which have dissimilar average magnitudes, correlation coefficients and uniformity, its application is much more efficient than elementary statistical analysis that evaluates each metric individually. The feasibility of modified MTGS method for similarity analysis, which used the difference in MD values calculated with MTGS method for the antenna radiation pattern similarity comparison, was successfully demonstrated through three test cases.

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“…The requirement for obtaining an objective, quantitative and robust method of assessing such data is clear. Previously, a novel hybrid technique has been developed that modifies the basic ordinal technique (1) to take account of different regions of interest by re tabulating the data in such a way as to attribute more samples to regions of greatest interest prior to ranking the data. This approach enables the interval aspects of the data sets to be considered whilst minimising the impact of numerical instabilities that are more often associated with purely interval assessment techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Although there are a great many ways of categorising a given data set, one of the simplest is to divide the interval data set into a number of bins and to count how many elements fall within each bin. Thus, each data, set will provide a single histogram that can be compared using the ordinal measure of correspondence, described in (5), irrespective of the number of dimensions and the number of elements each data set contains.…”

Section: Introductionmentioning

confidence: 99%

Advances in the objective measure of comparison between antenna pattern functions

Gregsons

McCormick

Parini

2003

Twelfth International Conference on Antennas and Propagation (ICAP 2003)

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The utility of a variety of objective, quantitative and robust methods of assessing similarities between antenna measurement data have already been highlighted in a number of recent publications (1, 2, 3). These techniques involved the extraction of interval and ordinal features from the data sets that can then be effectively compared to establish their adjacency. However, frequently such is the volume and complexities of the data involved that a single comparison methodology is inadequate to effectively classify all types of data. Within this paper we intend to compare and contrast several techniques for obtaining a quantitative, holistic, measure of similarity between data sets as well as introducing a new hybrid technique. In addition to more conventional interval techniques, e.g. crosscorrelation coefficient, two newer more sophisticated techniques will be presented. These are an ordinal and an interval-ordinal technique. In addition to these newer statistical image classification techniques, a novel hybrid categorical ordinal technique is developed that retains the advantages of the interval-ordinal technique but removes the requirement for interpolation and facilitates the comparison of two, or higher, dimensional data sets of differing sizes. These techniques will be illustrated with reference to a number of data sets that will be examined assessed and classified to obtain measures of adjacency that relate global features of the data sets. This data is derived from the output of partial scan techniques (4) that attempt to reduce truncation errors in planar near field antenna measurements by the construction of bespoke polyhedral sampling surfaces that aim to enclose all the current sources.

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Quantitative measures of comparison between antenna pattern data sets

Cited by 8 publications

References 1 publication

Comparison of Antenna Measurement Facilities With the DTU-ESA 12 GHz Validation Standard Antenna Within the EU Antenna Centre of Excellence

Comparison of Antenna Measurement Facilities With the DTU-ESA 12 GHz Validation Standard Antenna Within the EU Antenna Centre of Excellence

Objective Metric for Antenna Patterns Comparison Using Mahalanobis-Taguchi-Gram-Schmidt Method

Advances in the objective measure of comparison between antenna pattern functions

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