High-speed multi-target detection is a challenging problem in radar applications. Typically, targets with high speed go through several range cells in the observation period, which makes it more difficult to obtain each target's power coherently accumulated for target detection. In this study, novel multi-target detection with a narrowband radar system is proposed. In order to remove range migration and obtain coherent integration of the target energy, the Keystone transform is applied to the moving targets. However, because of the high target velocity and the low radar pulse repetition frequency phase ambiguity will occur, so the range migration will not be corrected properly. Then the phase ambiguity function of the kth target is compensated, and the envelope of the kth target concentrates in a certain range cell. Following by frequency modulation rate search a quadratic phase term is compensated, and the signal energy is finally coherently accumulated by FT analysis. The target is detected if the ratio of peak value to noise is higher than a predetermined threshold. For target detection in low signal-to-noise ratio (SNR), the Clean technique is applied. The proposed algorithm is verified by simulation and raw radar data results.

High-speed multi-target detection with narrowband radar

Terahertz (THz) waves propagation in a stratified media slab containing dusty plasmas are investigated using the propagation matrix method. The dispersive permittivity of conductors and dusty plasmas at THz frequencies are given. After the validation of the method is performed, the propagation properties of aluminum coated by dusty plasmas with and without vacuum interlayer are compared. The THz reflection and transmission coefficients for a dusty plasma slab with different medium parameters are discussed.

Propagation Matrix Method Study on THz Waves Propagation in a Dusty Plasma Sheath

The inverse synthetic aperture radar (ISAR) imaging technique of a moving target with sparse sampling data has attracted wide attention due to its ability to reduce the data collection burden. However, traditional low-rank or 2D compressive sensing (CS)-based ISAR imaging methods can handle the random sampling or the separable sampling data only. When the specific data collection condition cannot be satisfied, low-rank or 2D CS-based methods cannot provide satisfactory imaging results any more. To remedy this problem, in this paper, we proposed a joint low-rank and sparsity priors’ constrained model for ISAR imaging with various sparse data patterns. This model is inspired by the facts that the received radar data have a low-rank property and the ISAR image is sparse on the specific dictionary. Two reconstruction algorithms to solve the double priors’ constrained optimization problem are developed under the alternative direction method of multipliers (ADMM) framework with the help of augmented Lagrange multipliers (ALM). Results on simulation data and real data show that the proposed methods are quite effective in recovering missing samples and focused image and perform better than the matrix completion-based method and the sparse representation-based method when dealing with the various kinds of sparse sampling data.

Jointly Using Low-Rank and Sparsity Priors for Sparse Inverse Synthetic Aperture Radar Imaging

The complex dielectric permittivity of a dusty plasma, which contains an inhomogeneous electron density, charging response factor, and dust charging frequency caused by dust particles, is given to simulate the time-varying dusty plasma sheath. The recurrence field and current equations for terahertz (THz) wave propagation in the plasma are deduced and discretized using the auxiliary differential equation finite-difference time-domain (ADE-FDTD) method. The reflection, transmission, and absorption coefficients of a time-varying dusty plasma slab are calculated by using the ADE-FDTD method. The numerical results indicate that the reflection coefficients of the slab are easily affected by the rise time of the electron density. Besides, the absorption coefficients increase with the decrease in the rise time and working frequency as well as with the increase in the densities of electrons and dust particles. It is shown that THz waves may have potential applications in radio communications with a spacecraft at its re-entry into the atmosphere.

Propagation Properties of Terahertz Waves in a Time-Varying Dusty Plasma Slab Using FDTD

Since the compressed sensing (CS) theory broke through the limitation of the traditional Nyquist sampling theory, it has attracted extensive attention in the field of microwave imaging. However, in 3-D microwave sparse reconstruction application, conventional CS-based algorithms always suffer from huge computational cost. In this article, a novel 3-D microwave sparse reconstruction method based on a complex-valued sparse reconstruction network (CSR-Net) is proposed, which converts complex number operations into matrix operations for real and imaginary parts. Using the unfolding + network approximate scheme, each iteration process of CS-based iterative threshold optimization is designed as a block of CSR-Net, and a modified shrinkage term is introduced to improve the convergence performance of the approach. In addition, CSR-Net adopts a convolutional neural network module to replace a nonlinear sparse representation process, which dramatically reduces computational complexity and improves reconstruction performance over conventional CS-based iterative threshold optimization algorithms. Then, we divide the 3-D scene into a series of 2-D slices, and a phase correction scheme is adopted to ensure that the whole 3-D scene can be reconstructed with measurement matrix of a slice. Moreover, an efficient position–amplitude–random training method without additional real-measured data is employed for the proposed network, which effectively train the CSR-Net without enough real-measured data. Extensive experiment results demonstrate that CSR-Net outperforms both conventional iterative threshold optimization methods and deep network-based ISTA-NET-plus large margins. Its speed and reconstruction accuracy in 3-D imaging can achieve a state-of-the-art level.

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CSR-Net: A Novel Complex-Valued Network for Fast and Precise 3-D Microwave Sparse Reconstruction

The theory of compressed sensing (CS) states that an unknown sparse signal can be accurately recovered from a limited number of measurements by solving a sparsity-constrained optimization problem In this paper, we present a new framework of high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) imaging based on CS A phase-preserved CS approach for high-range resolution imaging is proposed The phase of a Bi-ISAR signal can be extracted by constructing a phase-preserved Fourier basis, which is crucial to azimuth processing of Bi-ISAR imaging After performing CS reconstruction in range, we present an improved version of CS-based cross-range imaging by combining modified Fourier basis and weighting with CS optimization Simulated data are used to test the robustness of the Bi-ISAR imaging framework with two-dimensional (2-D) CS method The results show that the framework is capable of accurate reconstruction of Bi-ISAR image in both range and cross-range

High-Resolution Bistatic ISAR Imaging Based on Two-Dimensional Compressed Sensing

The existing bistatic inverse synthetic aperture radar (Bi-ISAR) imaging methods usually uses a “stop-and-go” assumption where the target can be considered not in motion (stop condition) during the fast-time and in motion (go condition) during the slow time. However, for the high-speed target, this assumption is violated; furthermore, the conventional compression via Fourier transform is also invalid due to the quadratic phase term induced by the high-speed motion. In this case, a range compression method using the fractional Fourier transform (FrFT) based on minimum entropy criterion is presented to obtain high-resolution one-dimensional (1-D) range profile. Moreover, to achieve azimuth focusing for the complex-motion target, a new method of imaging time selection based on frequency smooth degree (FSD) is proposed. Simulated and real data are provided to verify the effectiveness of the proposed method.

High-Resolution Bistatic ISAR Image Formation for High-Speed and Complex-Motion Targets

In pulse Doppler (PD) radar, a novel motion compensation method based on keystone transform is proposed for handling with the data of a low signal-to-noise ratio (SNR). The keystone transform is used to correct the linear range migration which results from the radial velocity of targets. The SNR is improved through coherently accumulating all pulses. The simulation results show that the performance of the method based on keystone transform is better than that of the conventional coherent integration method using fast Fourier transform (FFT).

Multiple targets’ detection in terms of Keystone transform at the low SNR level

Passive radar system exploits illuminators of opportunity (IOs), such as television and radio, to detect and track moving targets. In this paper, we propose a new passive radar detection scheme with DVB-T signals. A large time-bandwidth-product signal is first generated by using multiple channels DVB-T signals. Then, a Radon-Fourier transform (RFT) algorithm is applied to achieve long-time coherent integration. In doing so, the moving target can be detected in low signal-to-noise ratio (SNR) situation. Simulation results validate the effectiveness of the proposed scheme.

Passive radar detection with DVB-T signals

The invention belongs to the technical field of bistatic synthetic aperture radar (BiSAR) transmitting and receiving, and discloses a space synchronizing method of a BiSAR with unknown target position coordinates. The method comprises the steps that initialization processing is conducted; according to a transmitter, the unit direction vector of antenna orientation is determined, the vector of a carrier coordinate system and the vector of a geographic coordinate system are converted, the vector of the transmitter to the center of a target area is determined, the geographic coordinate system and an earth coordinate system are converted, the vector of the transmitter under the earth coordinate system is determined, and the position vector of the center of the target area is determined; according to a receiver, the position vector of the receiver on the earth coordinate system is determined, the pointing vector of the receiver which points to the center of the target area is determined, the pointing vector under the geographic coordinate system is determined, the pointing vector under the carrier coordinate system is determined, and thus antenna orientation space synchronization of the receiver and the transmitter is achieved. The space synchronizing method of the BiSAR with the unknown target position coordinates has the advantages that the space synchronizing efficiency of the BiSAR can be effectively improved, the accuracy is high, the airplane mode is flexible, and the method is beneficial to being widely used.

Wei Zhang

Papers

High-Resolution Bistatic ISAR Imaging Based on Two-Dimensional Compressed Sensing

High-Resolution Bistatic ISAR Image Formation for High-Speed and Complex-Motion Targets

Multiple targets’ detection in terms of Keystone transform at the low SNR level

Passive radar detection with DVB-T signals

Space synchronizing method of bistatic synthetic aperture radar (BiSAR) with unknown target position coordinates