Changzheng Ma

Three-Dimensional ISAR Imaging Based on Antenna Array

In this paper, a 3D inverse synthetic aperture radar (ISAR) imaging method based on an antenna array configuration is proposed. The performance of conventional interferometric ISAR imaging system using three antennas is poor, as the positions of scatterers, which have the same range-Doppler value and projected onto the ISAR plane as a synthesis scatterer, cannot be correctly estimated. However, by using two antenna arrays perpendicular to each other, the systems ability to separate these scatterers can be improved. The criterion for the selection of a range unit that contains an isolated scatterer in the 2-D array domain for doing motion compensation is discussed. If there is no range unit which contains only an isolated scatterer, then radial and cross-range motion compensation has to be carried out by motion parameter estimation. Two cross-range motion parameters measurement algorithms, one based on array processing of the range profile and another based on correlation of ISAR images of different antennas, are proposed. The coordinates registration problem for the scatterers of a synthesis scatterer is also discussed. Simulation results have shown the effectiveness of the proposed methods.

Three-Dimensional Imaging of Targets Using Colocated MIMO Radar

Conventional inverse synthetic aperture radar image is a 2-D range-Doppler projection of a target and does not provide 3-D information. Its formation also requires complex motion compensation when the target is uncooperative and maneuvering. On the other hand, multiple-input and multiple-output (MIMO) radar, in addition to having a wide virtual aperture and high cross-range resolution, could also obtain a targets 3-D image in one snapshot and thus have avoided the complex motion compensation needed. In this paper, we propose a 3-D imaging algorithm using three MIMO configurations. The signal model is derived based on a modified zero correlation zone code. A strong scatterer selection criterion is also proposed for the construction of the target profile.

Sparse Array 3-D ISAR Imaging Based on Maximum Likelihood Estimation and CLEAN Technique

Large 2-D sparse array provides high angular resolution microwave images but artifacts are also induced by the high sidelobes of the beam pattern, thus, limiting its dynamic range. CLEAN technique has been used in the literature to extract strong scatterers for use in subsequent signal cancellation (artifacts removal). However, the performance of DFT parameters estimation based CLEAN algorithm for the estimation of the signal amplitudes is known to be poor, and this affects the signal cancellation. In this paper, DFT is used only to provide the initial estimates, and the maximum likelihood parameters estimation method with steepest descent implementation is then used to improve the precision of the calculated scatterers positions and amplitudes. Time domain information is also used to reduce the sidelobe levels. As a result, clear, artifact-free images could be obtained. The effects of multiple reflections and rotation speed estimation error are also discussed. The proposed method has been verified using numerical simulations and it has been shown to be effective.

Bistatic ISAR Imaging Incorporating Interferometric 3-D Imaging Technique

Bistatic inverse synthetic aperture radar (BiISAR) imaging provides complementary information to monostatic ISAR imaging. A suggestion that the conventional bistatic ISAR can be replaced by an equivalent monostatic radar is shown to be inadequate in this paper. We prove that the BiISAR image is a sheared version of the projection of the target on the range-Doppler plane. A bistatic interferometric radar configuration is proposed to correct this distortion and to form 3-D image. Simulation results have validated our analysis and shown the viability of the proposed 3-D imaging algorithm.

Interferometric ISAR Imaging on Squint Model

Conventional interferometric inverse synthesis aperture radar three dimensional imaging only consider broadside imaging condition. In this paper, squint model imaging con- flguration is discussed and the coordinate transform equation is given. The ISAR range proflle envelope alignment problem among difierent antennas are also discussed. Simulation results show the efiectiveness of our proposed method. In this paper, a vector is denoted by a small bold letter or a letter with an overhead arrow, while scatterers position and coordinate system are denoted by capital letters. The geometry of the three dimensional ISAR imaging based on three antennas is shown in Fig. 1. The origin of the three dimensional coordinate system (X 0 ; Y 0 ; Z 0 ) is denoted as O 0 , the transmitting antenna also acts as a receiving antenna and is located at the origin. The other two receiving antennas A, B are located in the X and Y axis and denoted as antenna 1 and antenna 2 respectively, and the distance from both A and B to O 0 is d. A local coordinate system (X; Y; Z) on the target parallel to the radar coordinate system is taken with reference, where the local origin is denoted as O. Point P located in (x; y; z) is expressed as p = ii! OP. Let the transmitted signal from the antenna O 0 be ~(t) = exp(j2…ft). The back scattered signals from scatterer P received at antennas O 0 and A are

MIMO Radar 3D Imaging Based on Combined Amplitude and Total Variation Cost Function With Sequential Order One Negative Exponential Form

In inverse synthetic aperture radar (ISAR) imaging, a target is usually regarded as consist of a few strong (specular) scatterers and the distribution of these strong scatterers is sparse in the imaging volume. In this paper, we propose to incorporate the sparse signal recovery method in 3D multiple-input multiple-output radar imaging algorithm. Sequential order one negative exponential (SOONE) function, which forms homotopy between ℓ1 and ℓ0 norms, is proposed to measure the sparsity. Gradient projection is used to solve a constrained nonconvex SOONE function minimization problem and recover the sparse signal. However, while the gradient projection method is computationally simple, it is not robust when a matrix in the algorithm is ill conditioned. We thus further propose using diagonal loading and singular value decomposition methods to improve the robustness of the algorithm. In order to handle targets with large flat surfaces, a combined amplitude and total-variation objective function is also proposed to regularize the shapes of the flat surfaces. Simulation results show that the proposed gradient projection of SOONE function method is better than orthogonal matching pursuit, CoSaMp, ℓ1-magic, Bayesian method with Laplace prior, smoothed ℓ0 method, and ℓ1-ℓs in high SNR cases for recovery of ±1 random spikes sparse signal. The quality of the simulated 3D images and real data ISAR images obtained using the new method is better than that of the conventional correlation method and minimum ℓ2 norm method, and competitive to the aforementioned sparse signal recovery algorithms.

Receiver Design for MIMO Radar Range Sidelobes Suppression

Multiple-input multiple-output (MIMO) radar transmits multiple-coded signals. In matched filter receiver, coded signals are required to have very good auto- and cross-correlation properties. These conditions are difficult to fulfill simultaneously when the lengths of the codes are short and there are many transmit antennas. In this correspondence, a receive filter matrix is designed using quadratic optimization method. The integrated correlations between the transmitted codes and the receive filter matrix in the area around zero shift are constrained under a prescribed level and the output noise power is minimized. A closed form solution is obtained. This integrated sidelobe level (ISL) method is computationally more efficient compared with the peak sidelobe level (PSL) method.

Three-Dimensional Imaging Using Colocated MIMO Radar and ISAR Technique

A conventional inverse synthetic aperture radar image is a 2-D range-Doppler projection of a target and does not provide 3-D information. Three-dimensional imaging using an interferometric technique cannot separate scatterers that have been projected onto the same range-Doppler unit. Multiple-input-multiple-output (MIMO) radar, however, in addition to having a wide virtual aperture and a high cross-range resolution, could also obtain a targets 3-D image in one snapshot. We discuss in this paper the use of time-domain information to improve MIMO radars imaging quality. A 3-D image alignment algorithm and a rotation vector estimation method are discussed. Simulation results show that image SNR is improved and cross-range sidelobes are mitigated.

Fast median filtering algorithm based on FPGA

Median filter has good capabilities for reducing a variety kind of random noise, and causes less ambiguity than linear smoothing filters under same processing size. In order to suppress the impulse noise of digital video signal and meet the systems needs of real-time, it is of great significance to do fast filtering of image based on hardware. By analyzing the common 3×3 filtering windows mathematical model, this paper proposes a fast median filtering algorithm based on field programmable gate array (FPGA) and the scheme design. According to the characteristics of parallel structures and its suitable for pipeline design of FPGA. VHDL and schematic design are used in this paper to design the implement circuit. Quartus II is used for timing simulation. The results show that it can filter the impulse noise in real time and improves the quality of image.

Zero Correlation Zone Codes and Extended Zero Correlation Zone Codes for MIMO radar signal separation

Pseudo random codes are easily constructed and can be used in multiple-input multiple-output (MIMO) radar to provide for signal separation. However it has the drawbacks of high aperiodic cross-correlation and auto-correlation sidelobes. Fortunately, in some applications, codes with low correlations are needed only over a narrow window. In this paper, we introduce the Zero Correlation Zone Codes (ZCZC) which have zero correlations in a narrow zone. In other applications, the targets are stationary, which allows for using a long code sequence. In this case we propose an Extended Zero Correlation Zone Codes (EZCZC) where the zero correlation zone is extended to cover the whole sidelobes area. Design examples are given.