Shengqi Zhu

A Persymmetric GLRT for Adaptive Detection in Compound-Gaussian Clutter With Random Texture

We focus on the problem of detecting a signal in compound-Gaussian clutter, where the texture is a random variable with Gamma or inverse Gamma distribution. The persymmetric structure of the covariance matrix is exploited and a persymmetric generalized likelihood ratio test (Per-GLRT) using a three-step procedure is proposed. In addition, we prove that the Per-GLRT ensures constant false alarm rate (CFAR) property with respect to the covariance matrix. Finally, the detector is assessed by Monte Carlo simulations. Performance comparison of the Per-GLRT with the traditional GLRT shows that the former improves the detection performance in training-limited scenarios.

Unambiguous Radial Velocity Estimation based on Delay-Interferometry in Range Frequency Domain

Abstract To solve the problem that the estimated radial velocity of fast moving target is ambiguity in synthetic aperture radar/ground moving target indication (SAR/GMTI), an approach is proposed to estimate the radial velocity unambiguously in the paper. The method utilizes the processing of delay and interferometry in range frequency domain. After range compression and delay, the dual-channel data possess the same Doppler chirp rate. Then, interferometric processing is done in range frequency domain, obtaining the linear relationship between the interferometric phase and range frequency, with the slope containing the information of radial velocity. Since the slope is no ambiguity, radial velocity is estimated unambiguously. The maximum unambiguous radial velocity of the proposed method is also analyzed. Numerical simulations demonstrate the validity of the proposed method with high accuracy of estimation.

Covariance matrix transforming based moving jammers suppression method

In this paper, an adaptive moving jammers suppression method based on covariance matrix transforming is proposed. According to the knowledge of parameters of the jammers, we construct a transform matrix thus the covariance matrix could be forecasted through iteratively matrix transforming. This method will save many computation resources and ensure the application of adaptive beamforming in large-scale arrays. Simulation results demonstrate the effectiveness of the proposed method.

Knowledge-aided adaptive beamforming against signal steering vector mismatch with small sample support

In this paper, a knowledge-aided adaptive beamforming approach against signal steering vector mismatch with small sample support is proposed. First, a combination covariance matrix of the prior covariance matrix, the sample covariance matrix and an identity matrix is obtained by the minimum mean-square error criterion. Then the mismatch between the actual and the presumed signal steering vector is modeled as a worst-case performance optimization constraint. Based on the combination covariance matrix and the constraint, the adaptive beamformer is formulated. The validity of the proposed beamformer is evaluated by simulation results, when the sample support is small and the signal steer vector mismatch exists.

Clutter spatial-temporal characteristics for forward-looking missile-borne diving radar

A multi-channel forward-looking missile-borne radar diving model is proposed in this paper with a formulation characterizing the relationship of clutter angle-Doppler response. The difference between the horizontal and diving platform is that an additional Doppler frequency is induced by the diving velocity. We show that the diving Doppler frequency leads to the clutter spectrum serious varying across range which increases the non-homogeneity of the training data and degrades the performance of the space-time adaptive processing (STAP). We numerically characterize the spatial-temporal clutter spectrum of forward-looking missile-borne diving array radar and discuss the influence of additional Doppler frequency caused by the diving velocity component.

Dual channel SAR-based GMTI using weighted l 1 optimization

Ground moving targets indication (GMTI) system based on dual-channel Synthetic Aperture Radar (SAR) increases data transmission and storage load. To alleviate such payloads, we propose a GMTI method using weighted l1 optimization with reduced amount of echo samples. In this method, compressive sensing (CS) is utilized to process jointly the dual channel SAR data sampled sparsely in the azimuth direction. A transform matrix is firstly constructed to separate the energy support areas of moving targets from the energy support areas of all scattering centers. Then, clutter suppression and moving target imaging are achieved by solving a weighted l1 norm optimization. The proposed method works well even if clutter scattering centers have a low sparse level. Simulation results demonstrate the effectiveness of the method.