Ji-hong Liu

Bio: Ji-hong Liu is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Inverse synthetic aperture radar & Radar imaging. The author has an hindex of 1, co-authored 2 publications receiving 5 citations.

The ISAR imaging of ballistic midcourse targets based on Sparse Bayesian Learning

Abstract: The ISAR (inverse synthetic aperture radar) imaging technology is an important tool for the ballistic missile midcourse target recognitions. Considering the rotationally symmetric targets, the sparse representation model of the ballistic midcourse targets with micro-motion is established. The sparse recovery algorithm named SBL (Sparse Bayesian Learning) is analyzed, which can provide a much sparser solution than the general sparse recovery algorithms. Based on the newly developed CS (Compress sensing) theory, the ISAR imaging of the ballistic missile is reconstructed by using only a few echoes. Simulation results verify the validity and superiority of the proposed method.

Sinusoidal Frequency Modulation Sparse Recovery for Precession Rate Estimation Using Low-Frequency Long-Range Radar

Abstract: Low-frequency long-range radars (LFLRRs) are assumed to lack the ability of extracting targets micro motion signature, due to their low and nonuniform track update rate, as well as the weak micro Doppler (m-D) owing to their large wave length. The recently proposed sinusoidal frequency modulated (SFM) Fourier transform can achieve a longer integral period gain, and consequently provides a new perspective for extracting weak m-D signature. However, its direct application is unavailable for LFLRRs, since their track update rate is very low and may not even be constant. This paper derives a new approach, named sinusoidal frequency modulation sparse recovery (SFMSR) for m-D analysis with LFLRR, by exploiting the micro motion spectrum sparsity in SFM signal space. SFMSR employs the Fourier modulation dictionary, which is determined only by the frequency in SFM signal space. Unlike other sparse representation-based methods whose dictionary is discretization of a 3-D space parameterized by the micro motion amplitude, frequency, and initial phase, the SFMSR reduces the m-D analysis to 1-D parameter optimization, and therefore can enhance the precision, stability, and computational efficiency simultaneously. The temporally correlated sparse Bayesian learning in SFM signal space is employed to decompose the signal and produce highly sparse solutions. The simulation results indicate that the proposed method outperforms the existing methods in accuracy and robustness, which can provide satisfactory performance even when the carrier frequency is 430 MHz and the average data rate is 0.5 Hz.

Imaging of moving target for distributed MIMO radar using improved SBL technique

Abstract: The distributed multiple input multiple output (MIMO) radar has the potential to achieve high resolution. But when the target is moving, the imaging result will be blurred if we don't consider the effect of the motion. To solve this problem, the velocity of the target will be estimated along with target recovery in a loop iteration process. Furthermore, by utilizing the sparsity of the scatterers, we use compressive sensing (CS) method to obtain better performance. The improved Sparse Bayesian Learning (SBL) technique is used in this paper for target recovery and velocity estimation. The effectiveness of the proposed sparse recovery approach based on SBL (SRA-SBL) is confirmed by several experimental results.

Feature Parameters Estimation of the Rotor Target Based on ISAR Image Sequences

Abstract: The paper proposed a method to estimate 5 parameters for the question of rotor target recognition, which included the rotation rate, blade number, rotor disk attitude angle, rotor disk radius and the distance of blade root from the rotor disk center. The method generated the electromagnetic scattering data obtained from different radar based on physical optics and edge diffraction, the period of the echo response of the rotor target was obtained by the autocorrelation, the ISAR image sequence was gained by the range-instantaneous doppler algorithm, the blade number could be obtained from the image and the rotation rate was obtained, the paper also deduced the nonlinear three variables equation combined of rotor disk attitude angle and rotor disk radius, so the cross range scaling of the rotor ISAR image was realized, the projection relationship of the rotor target on the ISAR image was analyzed, the way to get the distance of blade root from the rotor disk center was proposed. Finally, the method was proved to be effective by the simulation.

The unified theoretical frame of logarithmic alternative model for sparse information recovery and its application in multiple source location problem by TDOA

Abstract: In sparse information recovery, the core problem is to solve the $l_0$-minimization which is NP-hard. On one hand, in order to recover the original sparse solution, there are a lot of papers designing alternative model for $l_0$-minimization. As one of the most popular choice, the logarithmic alternative model is widely used in many applications. In this paper, we present an unified theoretical analysis of this alternative model designed for $l_0$-minimization. By the theoretical analysis, we prove the equivalence relationship between the alternative model and the original $l_0$-minimization. Furthermore, the main contribution of this paper is to give an unified recovery condition and stable result of this model. By presenting the local optimal condition, this paper also designs an unified algorithm and presents the corresponding convergence result. Finally, we use this new algorithm to solve the multiple source location problem.

Sinusoidal frequency modulation sparse recovery for radar micro-Doppler analysis

Abstract: This paper derives a new approach, named sinusoidal frequency modulation sparse recovery (SFMSR) for micro-Doppler (m-D) analysis, by exploiting the micro motion spectrum sparsity in SFM signal space. SFMSR employ the Fourier modulation dictionary, which is determined only by the “frequency in SFM signal space”. Unlike other SR-based methods whose dictionary is discretization of a 3D space parameterized by the micro motion amplitude, frequency and initial phase, the SFMSR reduce the m-D analysis to 1D parameter optimization, and therefore can enhance the precision, stability and computational efficiency simultaneously. The temporally correlated sparse Bayesian learning (TSBL) in SFM signal space is employed to decompose the signal and produce highly sparse solutions. Simulation results indicate that the proposed method outperforms the existing methods in accuracy and robustness.