Ivan J. LaHaie

Overview of an image-based technique for predicting far-field radar cross section from near-field measurements

For the last 18 years, our group has been developing a variety of near-field-to-far-field transformations (NFFFTs) for predicting the far-field (FF) RCS of targets from monostatic near-field (NF) measurements. The most practical and mature of these is based on the reflectivity approximation, commonly used in ISAR imaging to model the target scattering. This image-based NFFFT is also the most computationally efficient because - despite its theoretical underpinnings - it does not explicitly require image formation as part of its implementation. This paper presents a formulation and implementation of the image-based NFFFT that is applicable to two-dimensional (2D) spherical and one-dimensional (1D) circular near-field measurement geometries, along with numerical and experimental examples of its performance. We show that the algorithms far-field RCS pattern-prediction performance is quite good for a variety of frequencies, near-field measurement distances, and target geometries. In addition, we show that the predicted RCS statistics remain quite accurate under conditions where the predicted far-field patterns have significantly degraded due to multiple interactions and other effect.

Three-dimensional position and orientation measurements using magneto-quasistatic fields and complex image theory [measurements corner]

Traditional wireless position-location systems, operating using propagating waves, suffer reduced performance in non-line-of-sight (NLoS) applications. Traditional systems that use quasistatic fields have instead been limited to short ranges, progressive direction-finding applications, require RF fingerprinting, or do not provide complete immunity to dielectric obstacles (use of electric fields). These limitations impose severe restrictions in applications such as tracking an American football during game play, where position and orientation tracking may be required over long ranges, and when the line-of-sight (LoS) is blocked by groups of people. A technique using magneto-quasistatic fields and complex image theory was recently shown to circumvent these problems, and to enable accurate long-range one-dimensional and two-dimensional measurements. In this work, we present three-dimensional position and orientation measurements using the magneto-quasistatic system and complex image theory over an area of 27.43 m × 27.43 m. Inverting the theoretical expression for the voltage measured at the terminals of the receiving loops to determine three-dimensional position and orientation resulted in mean and median geometric position errors of 0.77 m and 0.71 m, respectively; inclination orientation mean and median errors of 9.67° and 8.24°, respectively; and azimuthal orientation mean and median errors of 2.84° and 2.25°, respectively.

A Partial Rotation Formulation of the Circular Near-Field-to-Far-Field Transformation (CNFFFT)

For many years now, General Dynamics has described the development, characterization, and performance of an image- based circular near-field-to-far-field transformation (CNFFFT) for predicting far-field radar cross sections (RCS) from near- field measurements collected on a circular path around the target. In this paper, we consider the CNFFFT algorithm as an azimuthal-filtering process, and develop a formulation capable of transforming data that is not measured over a full 360deg. Such a formulation has applications in measurement scenarios where collection of a complete rotation is not practical. As part of the development, we provide guidelines for the near-field data support required to achieve a desired accuracy in the sub-360deg CNFFFT result. Numerical simulations are provided to demonstrate that the results of this partial-rotation formulation are consistent with the full-circle CNFFFT results presented in past papers.

antenna-pattern correction for near-field-to-far field RCS transformation of 1D linear SAR measurements

In a previous AMTA paper (B. E. Fischer, et al.), we presented a first-principles algorithm, called wavenumber migration (WM), for estimating a targets far-field RCS and/or far-field images from extreme near-field linear (one-dimensional) or planar (two-dimensional) SAR measurements, such as those collected for flight-line diagnostics of aircraft signatures. However, the algorithm assumes the radar antenna has a uniform, isotropic pattern for both transmitting and receiving. In this paper, we describe a modification to the (one-dimensional) linear SAR wavenumber migration algorithm that compensates for nonuniform antenna-pattern effects. We also introduce two variants to the algorithm that eliminate certain computational steps and lead to more efficient implementations. The effectiveness of the pattern compensation is demonstrated for all three versions of the algorithm in both the RCS and the image domains using simulated data from arrays of simple point scatterers.

Measurements corner: Causes of discrepancies between measurements and EM simulations of millimeter-wave antennas

This article describes the possible factors that can introduce discrepancies between measurements and three-dimensional EM simulations of millimeter-wave (mm-wave) antennas. The effects of the choice of the simulation solver, the relative permittivity and the homogeneity of the substrate material, the manufacturing tolerance, and the measurement errors are discussed. It is concluded that a fair comparison between measurements and simulations of mm-wave antennas can only be made if all these possible causes of discrepancies are taken into account.This article describes the possible factors that can introduce discrepancies between measurements and three-dimensional EM simulations of millimeter-wave (mm-wave) antennas. The effects of the choice of the simulation solver, the relative permittivity and the homogeneity of the substrate material, the manufacturing tolerance, and the measurement errors are discussed. It is concluded that a fair comparison between measurements and simulations of mm-wave antennas can only be made if all these possible causes of discrepancies are taken into account.

T13 — Transformations for Radar Cross-Section (RCS) and imaging from monostatic near-field measurements

Summary form only given, as follows. True far-field (FF) radar cross-section (RCS) measurements of full-scale targets are often impractical to perform because of the large distances and/or large compact range reflector required to produce a plane wave illumination of the target. This fact has led to a requirement for techniques that can infer FF RCS from limited (specifically, monostatic-only) measurements in the near-field (NF) of the target. In this tutorial, we will present an in-depth derivation of a family of mature, self-consistent, and accurate near field RCS transformations that are based on models that are used in synthetic aperture, tomographic, and other forms of radar imaging. These image-based techniques have been successfully applied in practice to a wide range of targets and measurement configurations. The complete presentation was not made available for publication as part of the conference proceedings.

In memoriam: Ray King

Ray King, a retired Lawrence Livermore National Laboratory (LLNL) engineer, died February 21, 2010. He was 77. Born in Montrose, Colorado, on January 1, 1933, King grew up on a large ranch in the mountains outside of Montrose. He took great pride and enjoyment in his high school years as a member of FFA (Future Farmers of America), and became state President his senior year in high school.

Recent Microwave Absorber Wall-Reflectivity Measurement Methods [Measurements Corner]

The classical and widely used termination - VSWR method, used to measure the reflectivity of an absorber wall in an anechoic chamber, has been recently upgraded. This measurement (slotted-line measurement type) was in the past realized frequency by frequency, with an amplitude transmitter/receiver and one antenna on a moving cart. It is now possible to cover a complete frequency bandwidth without any movement with the advanced VSWR method (AVSWR), and also with the RCS method. These two complementary methods do not require antenna movement, and allow measuring complete bandwidths at the same time, where only a few frequencies were measured before. It is not an exaggeration to say that the progress is as high as the transition between the slotted-line measurement technique and automated vector network analyzers at the end of the 1970s.

Model-Based optimization using ℓ 1 minimization for reducing the uncertainty in radar cross-section (RCS) measurements and predictions

Our team at Integrity Applications Inc. (IAI) has worked extensively on radar cross-section (RCS) measurements and predictions and problems related to removal of background contamination, defect detection and localization, image editing and reconstruction (IER), sub-Nyquist interpolation, and near field-to-far field transformation (among others). In all cases, we have found that model-based optimization techniques using an ℓ1 minimization solver provide significantly improved performance with forward models as simple as isotropic point scatterers and as complex as rigorous method of moments (MoM) codes. In this overview paper, we present two simulated examples of model-based optimization using ℓ1 minimization from each end of that spectrum. The first involves the use of a sparsely-sampled set of RCS measurements to reduce the uncertainty in a MoM model due to unknown defects on the target. The model is then used to interpolate the sparsely-sampled RCS pattern data. The second involves the use of a dictionary of point scatterers and other linear basis functions to reduce the uncertainty in a set of RCS measurements due to additive contamination from clutter and noise.

Introduction [Measurements Corner]

The problem of detecting, locating, and tracking dismounts (moving people), using a distributed collection of simple and cheap narrowband (and, hence, low-resolution) radar devices, is an extremely challenging problem. It has been a topic of considerable research for the past several years. In particular, system performance is dependent upon both the positioning of the devices and the detection/tracking algorithms applied to the raw radar data. This months Measurements Comer paper describes a novel Bayes-optimal nonlinear filtering technique for the latter. It demonstrates that reasonable dismount tracking can be accomplished with 5–10m bistatic range-resolution systems.