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A technique to reduce the scan length in near field antenna measurement is presented. In the technique, the original scan length is selected for a critical angle of 30° 35°. The measured near field probe data is then extrapolated beyond the available probed region. The extended near field probe data is next used to predict the far field pattern of the AUT. The extrapolation is carried out by estimating the aperture distribution from the measured probe data. The aperture size, the separation between the AUT and the probed plane and the orientation of the probed plane with respect to the AUT are selected such that the aperture distribution leads to the minimum error between the measured near field probe data and the near field due to the aperture distribution.
A technique to reduce the scan length in near field antenna measurement is presented. In the technique, the original scan length is selected for a critical angle of 30° 35°. The measured near field probe data is then extrapolated beyond the available probed region. The extended near field probe data is next used to predict the far field pattern of the AUT. The extrapolation is carried out by estimating the aperture distribution from the measured probe data. The aperture size, the separation between the AUT and the probed plane and the orientation of the probed plane with respect to the AUT are selected such that the aperture distribution leads to the minimum error between the measured near field probe data and the near field due to the aperture distribution.
Andrew Corporation, founded in 1937 and headquartered in Orland Park, Illinois, has evolved into a worldwide supplier of communication products and systems. To develop a superior, high performance line of base station products for a very competitive marketplace, several new antenna measurement systems and upgrades to existing facilities were implemented. This engineering project developed an indoor test range facility incorporating design tool advantages from among Andrew Corporation's other antenna test facilities. This paper presents a 22-foot vertical by 5-foot diameter cylindrical near-field measurement system designed by Nearfield Systems Incorporated of Carson, California. This system is capable of measuring frequencies ranging from 800 MHz to 4 GHz, omnidirectional and panel type base station antennas up to twelve feet tall having horizontal, vertical or slant (+/- 45 degree) polarizations. Far-field patterns, near-field data and even individual element amplitude and phases are graphically displayed.
A sophisticated software package FARANA (FAR-field ANAiysis) is presented for transforming planar near-field test data to far-field antenna patterns, including enhanced analysis of far-field results. FARANA is coded in MATLAB version 5.0. MATLAB (MATrix LABoratory) is an interactive mathematical modelling tool based on matrix solutions without dimensioning. Using MATLAB, numerical engineering problems can be solved in a fraction of time of time required by programs coded in FORTRAN or C. FARANA operates with a state-of-the-art graphical user's-interface, is intuitive to use and features high speed and accuracy. This paper addresses an assessment of the program, discusses its use and enhanced far-field analysis capabilities.
We describe a technique which uses field intensity patterns formed by the interference of an unknown test antenna and a known referenceantenna - holograms in the classical optical sense - for determining the far-field pattern of the unknown antenna. The field intensity is measured by acquiring an infrared picture of the tem perature distribution on a resistive screen heated by incident microwave energy. The output of the camera is processed to yield the electric field intensity on the surface of the resistive screen. Required measurements are the field patterns of the unknown antenna and two holograms taken with relative phase differences between the reference and unknown antennas of 0° and 90°. In addition, the amplitude and phase of the reference field at the measurement plane are needed. These can be obtained from a separate measurement of the reference using standard near-field techniques. The algorithm gives the complex near field of the antenna under test which can then be processed to obtain the far-field pattern of the antenna under test. We present results showing far-field patterns which acceptably reproduce the main beam and near sidelobes. Such techniques will allow rapid testing of certain antenna types.
We describe a technique which uses field intensity patterns formed by the interference of an unknown test antenna and a known referenceantenna - holograms in the classical optical sense - for determining the far-field pattern of the unknown antenna. The field intensity is measured by acquiring an infrared picture of the tem perature distribution on a resistive screen heated by incident microwave energy. The output of the camera is processed to yield the electric field intensity on the surface of the resistive screen. Required measurements are the field patterns of the unknown antenna and two holograms taken with relative phase differences between the reference and unknown antennas of 0° and 90°. In addition, the amplitude and phase of the reference field at the measurement plane are needed. These can be obtained from a separate measurement of the reference using standard near-field techniques. The algorithm gives the complex near field of the antenna under test which can then be processed to obtain the far-field pattern of the antenna under test. We present results showing far-field patterns which acceptably reproduce the main beam and near sidelobes. Such techniques will allow rapid testing of certain antenna types.
The mechanical rotator must be correctly aligned and the probe placed in the proper location when performing spherical near-field measurements. This alignment is usually accomplished using optical instruments such as theodolites and autocollimators and ideally should be done with the antenna under test mounted on the rotator. In some cases it may be impractical to place the alignment mirrors on the AUT or optical instruments may not be available. In these and other cases, it is desirable to check alignment with electrical measurements on the actual AUT and probe. Such tests have recently been developed and verified. Appropriate comparison and analysis of two near-field measurements that should be identical or have a known difference yields precise measures of some rotator and probe alignment errors. While these tests are independent of the AUT pattern, judicious choice or placement of the antenna can increase the sensitivity of the test. Typical measurements will be presented using analysis recently included in NSI software.
When a planar near-field measurement is done, errors are introduced due to imperfections in the mechanical and electrical parts of the measurement equipment. In order to identify the characteristics of different types of errors, a MatLab program that simulates the near-field from an antenna has been developed. The near-field is transformed to far-field and the errors are evaluated. This paper looks into four different error types: 1) Truncation errors (if the measurement surface is to small the near-field will be truncated before it reaches adequately low levels), 2) Probe-AUT distance errors (fluctuations in the probe AUT distance over the measurement surface), 3) Zigzag errors (due to data being acquired during both travel directions of the probe), 4) I,Q amplification errors (different amplification for the I and Q channels in the receiver). The results are presented in plots which illustrate where in space the largest antenna pattern errors occur.
A series of measurements to validate the performance of a Vertical Planar Near-field Antenna Test Range located at the Hughes Space and Communications Company (HSC) was performed. These measurements were made as part of a task to provide validation of this particular range for detailed Production Antenna Testing. This validation was required in preparation for measuring a particular flight antenna. The range validation consisted of a series of self comparison tests and far-field range pattern comparison tests using an offset reflector antenna as the validation antenna. This antenna had been previously measured on a far-field antenna range which is in constant use to test flight antennas. This paper describes the range validation tests and presents some of the results. Comparisons of some far-field patterns measured on the validation antenna at both the far-field and near-field ranges is presented.
NSI recently delivered a Turnkey Near-field Antenna Measurement System (TNAMS) to the Naval Surface Warfare Center - Crane Division (NSWC-CD) in Crane Indiana. The system supports characterization and calibration of the Navy's active array antennas. TNAMS includes a precision 12' x 9' vertical planar near-field robotic scanner with laser optical position measurement system, dual source microwave instrumentation for multiple frequency acquisition, and a wide PRF range pulse mode capability. TNAMS is part of the Active Array Measurement Test Bed (AAMTB) which supports testing of high power active arrays including synchronization with the Navy's Active Array Measurement Test Vehicle (AAMTV), now under development. The paper summarizes the hardware configuration and unique features of the pulse mode capability for high power phased array testing and the TNAMS interface to the AAMTV and AAMTB computers. In addition, range test data comparing antenna patterns with various pulse characteristics is presented.
This paper presents a new technique for performing range compensation of full sphere antenna patterns measured on fixed line-of-sight antenna ranges where pattern measurements are made over a spherical surface. Such ranges include far-field, compact, and spherical near-field ranges. A plane wave model of the range field illuminating the antenna under test (AUT) is determined as described in another paper. This plane wave model consists of a small, selectable number of plane waves. Equations are given describing the transformation of range coordinates to AUT coordinates. This allows the response of an AUT to a plane wave from an arbitrary direction to be defined using only the far-field pattern of the AUT. The error pattern added to the pattern measurement by the extraneous plane waves is then estimated using the plane wave model and the measured pattern. This error pattern is subtracted from the antenna pattern measurement to obtain a compensated pattern. The compensated pattern and error pattern are improved iteratively. This paper demonstrates the technique using simulated data. The rotation of the spherical AUT grid with respect to the range grid during the measurement requires an interpolation of the measured fields to estimate the error pattern. Investigations of interpolation error are presented. The computational complexity of the compensation algorithm, excluding the plane wave model, is on the order of the number of measurement points on the spherical measurement grid. K
Cellular and PCS basestation antennas are basically arrays with highly directive elevation patterns and broad azimuth patterns. This causes measurement problems because they are large but not directive in both principal planes. As a result, the pattern measurements of these antennas that have been performed outside have been unreliable in many cases because they are very receptive to interference and range clutter. Thus, one wants to move inside but the antenna size can significantly impact the overall range cost. This paper describes a very practical solution to this problem. Since basestation antennas are long and narrow, one can use a near field scanner approach to deal with the length. In fact by using a sectorial horn probe, the narrow dimension of the antenna-under-test is illuminated by a cylindrical wave. Thus, the scanner need only probe the field along the antenna length. This linear scan data can then be transformed to generate the desired far field elevation pattern. The details of this novel design will be described as well as the results, to illustrate the system capability and accuracy.
A technique was developed to recover the near-field function on a larger data set than the one that is measured. It requires the preliminary determination of functions containing the information relating the two data sets. The simplest way of obtaining such a function is to measure the near-field function on the larger and the smaller data set. This seems to be a drawback to the technique. However. after making one such pair of measurement it is therefore non necessary to do so again and the field of the antenna can be obtained, from the smaller data set measurement, with comparable accuracy. The technique is somewhat different when compensating for a sampling rate reduction. However, in both cases an analytical extension is required to fill the desired domain of definition, followed by a division. In the case of the sampling area the division of the spectral functions f2 by f1 is made in the spectral domain while in the case of the sampling rate the division of the near-field functions E' by E is made in the near-field domain. An experiment was performed to demonstrate the applicability of the above technique. A full near-field measurement of a linear array antenna was performed and processed, then after displacement of the antenna, measurements were done, in one case, on a truncated smaller scan area, and in another case with a larger sampling interval. The technique was applied to recover the complete far-field characteristics of the antenna from the smaller data set. The far-field characteristics of the antenna obtained by this technique were shown to be very similar to the results obtained from a more complete near field measurement.
A method is presented for computing far field antenna patterns from spherical near field antenna measurement data. The new method utilizes a novel Uniform Geometrical Theory of Diffraction (UTD) based transformation of spherically scanned antenna tangential electric (or magnetic) near field measured values to more efficiently obtain the antenna far field. Examples illustrating the accuracy and speed of UTD based spherical near to far field transformations for large to moderately large antennas are presented.
Phase space representation (PSR) is introduced as a diagnostic tool for near-field antenna measurements. The PSR of a linear scan is defined as a two dimensional function of position and wavenumber. This combined spatial-wavenumber distribution can reveal features which are not directly visible in either spatial- or wavenumber-domains. It is shown that PSR is useful for both error diagnostic and compensation of certain errors. In particular, the benefits of PSR in identifying aliasing, spatial errors, multiple AUT-probe reflections, and random errors in amplitude and phase will be demonstrated. The capability of the PSR in compensation of contaminated measurements is demonstrated by examples.
The far-field parameters of an antenna are obtained from near-Field measurement with an accuracy that is limited by the sampling area and the sampling rate used to collect the measurement data. It is therefore important to know the relation between the far-field parameters and the sampling parameters. A parametric study of the far field parameters accuracy versus the sampling parameters was made. In order to determine the optimal choice of the sampling parameters to achieve the desired far-field accuracy, planar near-field measurements of a linear array were performed in an anechoid chamber at the Canadian Space Agency. A program performing Fast-Fourier Transform was used to process the data and to obtain spectral domain and reconstruct the far field patterns. A methodology developed in [1] was used to compare different spectral and far field patterns obtained from different sampling conditions. Parametric curves were developed for the far-field parameters such as gain, beam pointing, beam width, sidelobes, etc.
We review the development and recent performance results of a stand-alone fiber optic based EM field sensor system. The sensor heads are miniature (lcm), electrically passive, and are directly coupled to optical fibers at the remote sensing site. Sensor conversion of EM fields to optical intensity is carried out by mounting small antenna structures directly onto high speed lithium niobate electro-optic modulator chips. Optical power to the sensor head is derived from a stabilized laser which is located within a system chassis at a control room location. Sensor and fiber temperature drift effects are compensated by specialized remote bias control electronics. Recent broad spectrum tests have demonstrated a system bandwidth of about 20 GHz, and a minimum detectable field in the lO's of mV/m. Ultra wideband pulse measurements have demonstrated real time pulse signals of about 2 Vpp for 3 KV/m fields. The sensor system is slated for application in EMI effects such as EM compatibility, and for pin-point near-field and far-field mapping of radiation patterns. The technology is readily scaleable to frequencies exceeding 20 GHz.
Ell has been extensively involved in the development of advanced processing techniques (APT) to improve the quality and utility of both indoor and outdoor RCS/ISAR measurements. These include algorithms for removal of clutter, RFI, and targetsupport contamination (including interactions), prediction of far field RCS from near field measurements, suppression of multipath contamination, and extraction of scattering features/components. These techniques have been implemented in a framework based on ERIM International's IMGMANIP signal/ image processing toolbox and stream input-output (SIO) data flow paradigm. This paper describes a recently-developed Graphical User Interface (GUI) which incorporates the most mature and frequently-used APT algorithms.
Based on the method of moments (MoM), a network model algorithm perturbs the linearized electromagnetic interaction model (the admittance matrix) of a simulated target to match an actual measured data set in the least squares sense, resulting in a more accurate interaction model for the physical target. Since the admittance matrix is independent of source location this technique is amenable for use as a near field to far field transform algorithm. In this ansatz, a MoM admittance matrix is perturbed to match a set of near field measurements, then the far zone field is predicted using the perturbed admittance matrix multiplied by the appropriate far field measurement vector. This paper describes the application of the MoM network model technique to measured and numerical data for ten and twenty wavelength conespheres. Initially, a discussion is given of the code modifications necessary to adapt JRMBOR for network model use. A validation is then provided using "perfect" numerically-generated near field data to perturb an admittance matrix rendered inaccurate through a deliberate undersampling of the conesphere geometry. Finally, results are given for the MoM network model algorithm with measured near field data, with the resulting predictions compared to measured far field truth. Algorithm performance is examined as a function of frequency for monostatic near field input data.
The image-based near-field to far-field transformation is based on a reflectivity approximation that is commonly used in ISAR imaging. It is a limited but computationally efficient transform whose accuracy, for appropriate targets, rivals that of computationally more intense transforms. Previous results include applications of the transform to lOA. long wire and lOA. long conesphere numerical data. Here, 1-D and 2-D versions of the transform are applied to conesphere near-field measurements data and the results are compared to corresponding far-field measurements data. Transform errors obtained for these data are compared to corresponding results obtained using newly generated near-field and far-field numerical data. The image-based transform is believed to be especially applicable to the far-field correction of near-field measurements of complicated targets like aircraft or vehicles that are too large or too poorly defined to be simulated numerically.