Target Rotation Rate Research Articles

Cross-correlation registration algorithm enhancements for multipixel inverse synthetic aperture LiDAR.

The accurate registration and realignment of complex signal volumes is critical for cross-range aperture gain in 3D LiDAR aperture synthesis. For targets at long range, only a limited number of diffraction-limited pixels will be projected on the target, resulting in low cross-range support. In addition, the signal-to-noise ratio (SNR) is typically low. This research describes an enhanced cross-correlation registration algorithm for 3D inverse synthetic aperture LiDAR data volumes that improves performance for low cross-range support, low SNRs, and relatively large aperture shifts. The registration performance is improved through statistical removal of the cross-correlation noise pedestal and compensation for the reduced signal overlap caused by larger shifts. The registration performance is characterized as a function of SNR, signal shift (target rotation rate), and target pixel support. The algorithm's improvements allow for registration convergence at 1-5 dB lower SNR than the baseline cross-correlation algorithm. In addition, the algorithm enhancements allow for registration convergence at 10%-20% greater shifts.

Inverse synthetic aperture radar phase adjustment and cross-range scaling based on sparsity

Due to inherent sparsity of ISAR images, compressive sensing theory has been used to obtain a high resolution image. However, before applying sparse recovery methods, the phase error due to the translational motion of target is compensated by autofocusing algorithms and the target rotation rate is estimated by cross-range scaling methods. In this paper, a comprehensive matrix model for a uniformly rotating target that includes the phase error and chirp-rate of the target is derived. Then by using sparsity and minimum entropy criterion, the estimation of residual phase error and the rotation rate is refined. In order to reduce the computational load, we simplify the model and by an iterative method based on adaptive dictionary, the phase error and chirp-rate are estimated separately. Actually, by exploiting a two-dimensional (2D) optimization method and the Nelder–Mead algorithm the phase adjustment is performed and the chirp-rate is estimated by solving a 1D optimization method for dominant range cells of the target. Finally, both simulation and practical data have been used to verify the validity of the proposed approach.

Target rotation parameter estimation for ISAR imaging via frame processing

Frame processing method offers a model-based approach to Inverse Synthetic Aperture Radar (ISAR) imaging. It also provides a way to estimate the rotation rate of a non-cooperative target from radar returns via the frame operator properties. In this paper, the relationship between the best achievable ISAR image and the reconstructed image from radar returns was derived in the framework of Finite Frame Processing theory. We show that image defocusing caused by the use of an incorrect target rotation rate is interpreted under the FP method as a frame operator mismatch problem which causes energy dispersion. The unknown target rotation rate may be computed by optimizing the frame operator via a prominent point. Consequently, a prominent intensity maximization method in FP framework was proposed to estimate the underlying target rotation rate from radar returns. In addition, an image filtering technique was implemented to assist searching for a prominent point in practice. The proposed method is justified via a simulation analysis on the performance of FP imaging versus target rotation rate error. Effectiveness of the proposed method is also confirmed from real ISAR data experiments.

Joint ISAR Imaging and Cross-Range Scaling Method Based on Compressive Sensing With Adaptive Dictionary

Compressive sensing (CS) is successfully applied in inverse synthetic aperture radar (ISAR) imaging. But, as target rotation rate is not concerned in the CS-based imaging methods, the obtained image cannot be scaled in the cross-range dimension. Consequently, difficulties arise in extracting the target geometrical information from the CS ISAR image. But, target geometrical size is an important parameter in automatic radar target recognition. To remedy this problem, a joint ISAR imaging and cross-range scaling method is proposed. In the proposed method, an adaptive parametric dictionary, comprising chirp rate parameter, is used to represent the observed data. By minimizing the reconstruction error, sparsity-constrained optimization, combined with the chirp-rate parameter and target reflective coefficient, is established. To find a solution to the nonlinear and nonconvex optimization problem, an iterative procedure is developed. Finally, with the help of the chirp-rate, target rotation rate can be estimated by the least square method, and the ISAR image can be scaled in cross-range. Experimental results show that the proposed method can fit the observed data better than the method using a fixed Fourier dictionary. Besides, cross-range scaled ISAR images can be obtained with limited pulses.

Comparison of parametric sparse recovery methods for ISAR image formation

A parametric sparse representation model of the inverse synthetic aperture radar (ISAR) signal has been proposed recently, and the ISAR signal is decomposed as a summation of many basis-signals determined by the target rotation rate. Based on the parametric sparse representation model, several sparsity-driven algorithms are proposed to retrieve both the target rotation rate and the ISAR image. In this paper, four parametric sparse recovery algorithms are compared mainly in three aspects: the accuracy of the rotation rate estimation, the ISAR image quality and the computational load. Numerical examples are presented to show the advantages and disadvantages for each method.

Target rotation rate estimation via ISAR frame processing

A method is proposed for estimating the target rotation rate for ISAR imaging based on the frame processing technique. The method utilises the intrinsic structures of the frame which span the Hilbert space of radar return from which the rotational information of the target is extracted. For a prominent point, the intensity of the projection is maximum when the frame component corresponding to the prominent point is built with the target rotation rate.

Inverse Synthetic Aperture Radar Imaging Using Frame Theory

Inverse synthetic aperture radar (ISAR) imaging is conventionally addressed by means of fast Fourier transform (FFT) method. In this paper, a frame processing method derived based on frame theory for ISAR imaging is proposed as an alternative to FFT based approaches. In this method, a finite set of K consecutive target returns after a front-end processing by radar receiver are treated as a vector in a Hilbert space where a frame structure is defined. A frame and its dual are constructed as the functions of waveform and processing parameters and the target rotation rate. The latter can be estimated online. For certain classes of high resolution radar (HRR) waveforms, we show that it is possible to use standard methods of frame analysis and synthesis to reconstruct the ISAR image from radar returns. This method is analogous to those of reflective tomography, and does not suffer from the “range walk” problem exhibited by the FFT based approaches which poses constraints on the feasible aperture size for observing a target. Both simulated and experimental results are presented to demonstrate the effectiveness of the proposed method. Practical implementation and issues of the proposed frame processing technique are also discussed.

Growth-induced magnetic anisotropy behavior in thin garnet films fabricated by pulsed laser deposition

The effect of target rotation rate on the magneto-optical properties and induced magnetic anisotropy of pulse laser deposition grown Bi:YIG garnets has been investigated. The rotation rate dependence of magnetic properties of thin films has been studied using magneto-optical Faraday rotation combined with magneto-optical Kerr measurement setup. Our results show that decreasing the target rotation rate can also increase the polar coercive field and hence magnetic anisotropy. Also the spectral answer of samples show the capability of use of them in one off optical ranges such as visible, near infrared and far infrared regions.

ISAR 2-D Imaging of Uniformly Rotating Targets via Matching Pursuit

An algorithm based on matching pursuit (MP) is proposed for inverse synthetic aperture radar (ISAR) two-dimensional (2-D) imaging of uniformly rotating targets. The ISAR echo is decomposed into many subsignals that are generated by discretizing spatial domain and synthesizing the ISAR data for every discretized spatial position. The subsignals that indeed contribute to the ISAR echo are selected by the MP, and their coefficients represent the superresolution image. The target rotation rate is estimated by combining MP with maximum contrast search.

The effect of target rotation rate on structural and morphological properties of thin garnet films fabricated by pulsed laser deposition

The effect of target rotation rate on the structural and morphological properties of pulsed laser deposition grown Bi:YIG garnets is investigated. The rotation rate dependence of the surface morphology and magnetic properties of the thin films were studied using atomic force microscopy combined with a magneto-optical measurement setup. The results show that decrease in the target rotation rate can also increase the roughness, the index of refraction, and the surface skewness and can decrease Faraday rotation by an order of magnitude.

An Analysis of ISAR Image Distortion Based on the Phase Modulation Effect

Distortion in the ISAR image of a target is a result of nonuniform rotational motion of the target during the imaging period. In many of the measured ISAR images from moving targets, such as those from in-flight aircraft, the distortion can be quite severe. Often, the image integration time is only a few seconds in duration and the target's rotational displacement is only a few degrees. The conventional quadratic phase distortion effect is not adequate in explaining the severe blurring in many of these observations. A numerical model based on a time-varying target rotation rate has been developed to quantify the distortion in the ISAR image. It has successfully modelled the severe distortion observed; the model's simulated results are validated by experimental data. Results from the analysis indicate that the severe distortion is attributed to the phase modulation effect where a time-varying Doppler frequency provides the smearing mechanism. For target identification applications, an efficient method on refocusing distorted ISAR images based on time-frequency analysis has also been developed based on the insights obtained from the results of the numerical modelling and experimental investigation conducted in this study.

Discrete 2-D system identification for imaging rotating radar targets

Abstract A new approach to high-resolution radar imaging is presented. The starting is the notion that there exists an image of the target which needs to be identified. This image consists of a finite array of pixel elements, each with a magnitude and phase representing the complex reflectivity of target scatterers. The radar observation process is then modelled as a sequence of matrix operations applied to the image. We first treat this problem in a general way, then impose practical constraints to finally deal with the problem of a rotating target. One solution for recovering the image from the measurements in this case is obtained by inverting the matrix operations, although the rotation rate of the target is required. A second solution based on maximum likelihood techniques is derived and the connection with the pseudoinverse is shown. When the rotation rate of the target is unknown, the maximum likelihood solution can still be found through minimization of a non-convex matrix trace cost function. It is also observed that for unknown target rotation rate the maximum likelihood solution gives the same image estimate as does the conventional Fourier transform solution. Some results of applying this identification approach to simulated and experimental data are presented. Various extensions are proposed, including a recursive solution.