Sang-Hong Park

New Discrimination Features for SAR Automatic Target Recognition

We propose new features and a redundancy-free feature selection scheme for discriminating targets from clutter in high-resolution synthetic aperture radar imagery. These were found to be very useful for discriminating targets from clutter and well combined with various classical discriminative features. If a feature set with a larger dimension is used, its redundancy may increase. In order to reduce this redundancy, a rank-based feature selection scheme is devised. Our discriminating features and feature selection algorithm are evaluated using the moving and stationary target acquisition and recognition (MSTAR) data set.

Cross-Range Scaling Algorithm for ISAR Images Using 2-D Fourier Transform and Polar Mapping

This paper proposes a new method that solves the problem of inverse synthetic aperture radar image cross-range scaling by estimating the rotational velocity (RV) using the expansion-rotation-scale relationship between two range-Doppler (RD) images. This method is composed of three steps. The first step is preprocessing to construct 2-D Fourier transform images and initial polar-mapped images. In this step, two RD images are 2-D Fourier transformed to avoid the necessity of finding the rotation center; then, the transformed images are polar mapped with identical polar grids to convert rotation into translation in the θ-direction only. The second step is a coarse search that finds the angular shift that provides the maximum correlation between two polar images. The angular shift found is used as the initial relative rotation angle (RA), and the initial relative scaling factor (RSF) is calculated using the RV which is equal to the angular shift divided by the time delay between the images. The third step is the optimization of the relative RA using the Nelder-Mead approach, with the RSF updated using the relative RA derived during each iteration. In simulations using a Mig-25 aircraft, composed of ideal point scatterers, and the measured data from a Boeing 747-400, the targets were properly rescaled in the range-cross-range domain due to the accurate estimation of the RV.

MOTION COMPENSATION FOR SQUINT MODE SPOTLIGHT SAR IMAGING USING EFFICIENT 2D INTERPOLATION

In the squint mode airborne spotlight synthetic aperture radar system using the range migration algorithm (RMA), autofocus (AF) technique yields poor results due to the squint spreading of the point spread function (PSF) of a scatterer. Thus, two-dimensional (2D) interpolation is required to direct PSF blurring in cross-range direction, to improve the cross-range resolution ¢y and to remove the spatially-varying sidelobe. Because conventional 2D interpolation requires huge computation time and yields large computation errors, we propose an e-cient 2D interpolation technique for squint-mode RMA composed of two 1D interpolations. Simulation results using the measured turbulence data show ¢y was improved considerably and PSF was successfully focused by the proposed method with a reduced computation time.

MICRO-DOPPLER ANALYSIS OF KOREAN OFFSHORE WIND TURBINE ON THE L-BAND RADAR

To cope with the energy shortage and the rising cost of the fossil fuel, many wind farms are being constructed under the supervision of Korean government along the coasts of Korean peninsula to generate clean and renewable energy. However, construction of these wind farms may cause negative efiect on various L-band radars in operation. This paper presents the result of the micro-Doppler (MD) analysis of the in∞uence of the wind turbine on the L-band radar using the point scatterer model and the radar cross section of the real turbine predicted by the method of physical optics. The simulation results obtained at three observation angles show that the range of MD occupies a considerable portion of the helicopter MD range, and thus, the operations using helicopters need to be avoided in the wind farm region, and additional radars are required for the recognition of helicopter-like objects.

Efficient classification of ISAR images using 2d fourier transform and polar mapping

This paper proposes an efficient method to classify inverse synthetic aperture radar (ISAR) images. The proposed method achieves invariance to translation and rotation of ISAR images by using two-dimensional (2D) Fourier transform (FT) of ISAR images, polar mapping of the 2D FT image, and a simple nearest-neighbor classifier. In simulations using ISAR images measured in a compact range, the proposed method yielded high classification ratios with small-sized data regardless of the location of the rotation center, whereas the existing method was very sensitive to the location of it.

Efficient Classification of Low-Resolution Range Profiles of Automobiles Using a Combination of Useful Features

The range proflle (RP) of an automobile is derived by compressing the wideband radar signal, and it can be utilized for the classiflcation and thus contribute to lane change and collision avoidance. However, the limited radar bandwidth due to the cost and the system complexity impedes the successful classiflcation. This paper proposes an e-cient method to construct an e-cient feature vector of the automobile RP through combined use of the central moment, the information on the maximum-minimum and the peak information. Simulation results using the flve automobile models composed of point scatterers and a simple nearest neighbor classifler prove that the proposed method improves the classiflcation result, especially in the multi-aspect classiflcation.

Improved Classification Performance Using ISAR Images and Trace Transform

This paper proposes a new scheme for improving the classification performance of inverse synthetic aperture radar (ISAR) images. The proposed scheme utilizes the trace transform to gather abundant information from an ISAR image, regardless of the spatial distribution of the target response in the ISAR image. In simulations using ISAR images with various spatial distributions, the proposed scheme substantially improved the classification performance compared with existing methods, which are highly vulnerable to spatial variation.

Estimation of the micro-motion parameters of a missile warhead using a micro-Doppler profile

This paper deals with the problem of estimating micro-motion parameters. The proposed algorithm uses independent-component analysis to decompose the received signals into individual scatterer signals, and uses particle-swarm optimization to estimate the micro-motion parameters in a micro-Doppler profile. Simulation results show that the proposed method can successfully estimate micro-motion parameters of a warhead model consisting of many point scatterers.