Seong-Hyeon Lee

Efficient ISAR autofocus via minimization of Tsallis Entropy

Inverse synthetic aperture radar (ISAR) is a coherent imaging system formed by conducting signal processing on the received data consisting of radar cross section reflected from a maneuvering target. Autofocus is an essential step of ISAR imaging, whose performance has a great influence on the quality of the radar image. Minimum Shannon entropy phase adjustment (MSEPA) and minimum Renyi entropy-based algorithm (MREA) have been widely used to compensate for the phase error in ISAR autofocus. However, MSEPA and MREA have some drawbacks in terms of the noise sensitivity and computational efficiency. Tsallis entropy (nonextensive entropy) is a useful measure to describe the thermostatistical properties of physical systems. This paper concentrates on the performance of minimum Tsallis entropy phase adjustment (MTEPA) instead of the Shannon entropy. By minimizing the Tsallis entropy of an ISAR image, the MTEPA can significantly improve the computational efficiency, while retaining the image focal quality of the restored ISAR images, as compared to MSEPA and MREA. The order q of Tsallis entropy can be experimentally found by analyzing both an image quality metric and the computation time. In experimental results, the effectiveness of the MTEPA is illustrated and analyzed with simulated targets consisting of point scatterers and measured Boeing-747 data.

Efficient ISAR Autofocus Technique Using Eigenimages

In this paper, we propose a new and efficient inverse synthetic aperture radar (ISAR) autofocus technique by introducing eigenimages to boost the speed of the traditional autofocus algorithms. First, a preprocessing step is applied to mitigate the noise components in the received data. Then, we perform an eigen-decomposition of the covariance matrix of the range-aligned data, and generate the signal eigenimage obtained by deriving the Fourier transform of a small number of eigenvectors corresponding to the dominant eigenvalues. Finally, traditional autofocus methods are combined with the proposed signal eigenimage rather than the original ISAR image to eliminate image blurring due to phase errors. The proposed method can significantly lower the computational complexity of the traditional autofocus methods because the dimensionality of the signal eigenimage is considerably smaller than that of the ISAR image. Despite the low dimensionality of the signal eigenimages, the proposed scheme provides well-focused ISAR images that are comparable to those of the traditional autofocus methods in terms of image focal quality. Several simulations and experimental results using measured data of an actual flying aircraft are presented to verify the effectiveness of the proposed method.

ISAR Imaging of High-Speed Maneuvering Target Using Gapped Stepped-Frequency Waveform and Compressive Sensing

In the case of a stepped-frequency waveform (SFW) inverse synthetic aperture radar (ISAR) system, the translational motion (TM) of a target can be usually divided into two parts: 1) target motion within a pulse repetition interval, called the inter-pulse translational motion (IPTM) and 2) target motion between bursts, called the inter-burst translational motion (IBTM). The former induces severe blurring in the ISAR images as well as range-compressed data (i.e., range profile), and the latter also causes dramatic degradation of the ISAR image quality. In this paper, a novel framework for high-resolution gapped SFW (GSFW) ISAR imaging of high-speed maneuvering target is proposed. The main novelty of the proposed method is twofold: 1) accurate TM parameter estimation in conjunction with a compressive sensing theory using a newly devised cost function and particle swarm optimization and 2) compensation for both the IPTM and IBTM phase errors simultaneously even with the GSFW data set. Simulation results using ideal point scatterers show that the proposed method is capable of precise reconstruction of ISAR image and accurate TM parameter estimation. Experimental results using real measured data verify the robustness and the effectiveness of the proposed method.In the case of a stepped-frequency waveform (SFW) inverse synthetic aperture radar (ISAR) system, the translational motion (TM) of a target can be usually divided into two parts: 1) target motion within a pulse repetition interval, called the inter-pulse translational motion (IPTM) and 2) target motion between bursts, called the inter-burst translational motion (IBTM). The former induces severe blurring in the ISAR images as well as range-compressed data (i.e., range profile), and the latter also causes dramatic degradation of the ISAR image quality. In this paper, a novel framework for high-resolution gapped SFW (GSFW) ISAR imaging of high-speed maneuvering target is proposed. The main novelty of the proposed method is twofold: 1) accurate TM parameter estimation in conjunction with a compressive sensing theory using a newly devised cost function and particle swarm optimization and 2) compensation for both the IPTM and IBTM phase errors simultaneously even with the GSFW data set. Simulation results using ideal point scatterers show that the proposed method is capable of precise reconstruction of ISAR image and accurate TM parameter estimation. Experimental results using real measured data verify the robustness and the effectiveness of the proposed method.

Bistatic-ISAR Distortion Correction and Range and Cross-Range Scaling

The bistatic configuration used for inverse synthetic aperture radar (ISAR) imaging has attracted much attention because of its potential to overcome the certain limitations of monostatic ISAR imaging. However, range and cross-range scaling and bistatic distortion correction are essential for the efficient use of a bistatic ISAR (Bi-ISAR) image in their applications. In this paper, we introduce a novel range and cross-range scaling technique and bistatic distortion correction method for the restoration of a sheared (Bi-ISAR) image. The proposed method is composed of five stages: 1) extraction of the prominent scatterer using the range-bin selection method; 2) determination of the coefficients of the phase-history data in the selected range bin; 3) estimation of rotation center of the target using the optimization procedures via the particle swarm optimization algorithm; 4) range and cross-range scaling of (Bi-ISAR) image; and 5) restoration of the bistatic distortion that caused the sheared (Bi-ISAR) image. Finally, the proposed method can generate a well-scaled and restored (Bi-ISAR) image. An outstanding advantage of this method is that it requires no prior information about the bistatic configuration. Several examples are presented to validate the effectiveness of the proposed method.

Bistatic ISAR image reconstruction using sparse-recovery interpolation of missing data

When a bistatic inverse synthetic aperture radar (ISAR) system fails to collect complete radar cross section (RCS) datasets, bistatic ISAR (Bi-ISAR) images are usually corrupted using the conventional Fourier transform (FT)-based imaging algorithm. To overcome this problem, this paper proposes a new Bi-ISAR image reconstruction method that includes three steps: suboptimal estimation of parameters regarding the bistatic angle in the Bi-ISAR signal model via an orthogonal matching pursuit-type group-searching scheme, Bi-ISAR signal reconstruction using the estimated parameters, and Bi-ISAR image generation using the FT-based imaging algorithm applied to the reconstructed Bi-ISAR signal. To validate the reconstruction capability of the proposed method, bistatic-scattered field data using the physical optics technique as well as the point-scatterer model are used for Bi-ISAR image reconstruction. The results show that the proposed sparse-recovery-interpolation approach based on the Bi-ISAR signal model reconstruction combined with the classical FT-based algorithm can yield high reconstruction accuracy for incomplete bistatic RCS data compared to conventional numerical interpolation methods and existing direct sparse reconstruction techniques.

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.

ISAR autofocus by minimizing entropy of eigenimages

In this paper, we propose a novel and efficient inverse synthetic aperture radar (ISAR) autofocus technique by applying an eigenimage with a preprocessing step to minimum entropy phase adjustment (MEPA) algorithm. Several experimental results using measured data of an actual flying aircraft demonstrated that the proposed scheme can lead to substantially fast convergence to global minimum of the entropy cost surface and reduce the computational complexity of ISAR autofocus, compared to the traditional MEPA.

Analysis of Target Identification Performances Using Bistatic ISAR Images

Inverse synthetic aperture radar(ISAR) image generated from bistatic radar(Bi-ISAR) represents two-dimensional scattering distribution of a target, and the Bi-ISAR can be used for bistatic target identification. However, Bi-ISAR has large variability in scattering mechanisms depending on bistatic configurations and do not represent exact range-Doppler information of a target due to inherent distortion. Thus, an efficient training DB construction is the most important factor in target identification using Bi-ISARs. Recently, a database construction method based on realistic flight scenarios of a target, which provides a reliable identification performance for the monostatic target identification, was applied to target identification using high resolution range profiles(HRRPs) generated from bistatic radar(BiHRRPs), to construct efficient training DB under bistatic configurations. Consequently, high identification performance was achieved using only small amount of training Bi-HRRPs, when the target is a considerable distance away from the bistatic radar. Thus, flight scenarios based training DB construction is applied to target identification using Bi-ISARs. Then, the capability and efficiency of the method is analyzed.

Inter-Pulse Motion Compensation of an ISAR Image Generated by Stepped Chirp Waveform Using Improved Particle Swarm Optimization

Inverse synthetic aperture radar(ISAR) is coherent imaging system formed by conducting signal processing of received data which consists of radar cross section(RCS) reflected from maneuvering target. A novel algorithm is proposed to compensate inter-pulse motion(IPM) for the purpose of forming an well-focused ISAR image through signals generated by stepped chirp waveform(SCW). The velocity and acceleration of the target related to IPM are estimated based on particle swarm optimization (PSO) which has been widely used in optimization technique. Furthermore, a modified PSO which enables us to improve the performance of PSO is used to compensate IPM in a very short-time. Simulation results using point scatterer model of a Boeing-737 aircraft validate the performance of the proposed algorithm.

A Study on Accurate Angle Estimation of Multiple Targets for Digital Beam Forming Automotive Radar

In order to satisfy several conditions with respect to size, weight, and costs, automotive radars use an antenna consisting of a small number of receiving channels. If RELAX technique is applied to the automotive radars, angles of targets located in antenna beam can be estimated as well as the number of the targets. However, a small number of receiving channels in the antenna leads to inaccurate spectral estimation in angle domain, which in turn degrades performance of RELAX technique. Therefore, in this study, root-MUSIC technique coupled with MDL criterion is introduced to decide accurate angles of targets in antenna beam. In simulations, we show superior performance of proposed scheme using simulation results when three point targets are located in antenna beam.