Beamforming plays a crucial role in synthetic aperture radar (SAR) for interference mitigation and ambiguity unaliasing. In this article, a series of novel beamforming methods for SAR systems is proposed based on nulling the transceive beampattern accurately in frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) scheme. In general, these methods are implemented by assigning artificial interferences with prescribed powers within the given rectangular regions in the joint transmit–receive spatial frequency domain. In specific, according to the predefined null depths, closed-form expressions of artificial interference powers are first formulated. Then, iteration algorithms are developed to update the interference-plus-noise covariance matrix and the designed weight vector. In such a way, a trough-like transceive beampattern with arbitrarily distributed broadened nulls is formed in the joint transmit–receive spatial frequency domain. As a result, interferences mixed in signals received by SAR can be suppressed effectively. Numerical simulations and experimental results are provided to corroborate the effectiveness of the proposed methods.

Transceive Beamforming With Accurate Nulling in FDA-MIMO Radar for Imaging

A performance comparison of measurement matrices in compressive sensing

Dual-polarized GPS antenna array algorithm to adaptively mitigate a large number of interference signals

https://re.public.polimi.it/bitstream/11311/1140135/1/PreprintSubmitted.pdf

Surveying pervasive public safety communication technologies in the context of terrorist attacks

High-resolution coded-aperture design for compressive X-ray tomography using low resolution detectors

Projection matrix design using prior information in compressive sensing

Robust minimum dispersion distortionless response beamforming against fast-moving interferences

Unmanned aerial vehicle (UAV) has been widely used in various fields, which mainly relies on global navigation satellite system (GNSS) to obtain position information. However, fast-moving jamming poses a big challenge for UAV navigation. This motivates us to develop new robust adaptive beamforming technique that is capable of enhancing the navigation signal and suppressing the jamming efficiently. Since the navigation signal and jamming present non-Gaussianity in statistics, the minimum dispersion distortionless response (MDDR) beamformer is utilized, which exhibits better performance of non-Gaussian signal reception over the minimum variance-based beamformer. To suppress the fast-moving jamming, the minimum power constraint is added to the optimization problem regarding to the MDDR beamformer, leading to a resultant problem with a quadratic constraint. Furthermore, instead of the quadratic constraint, a linear version is derived under the power constraint to achieve a shape null sector towards the fast-moving jamming, and as a consequence, a linearly constrained MDDR beamforming is established. This allows us to develop two algorithms, i.e., quasi-MVDR algorithm and Newton's method, to accelerate the solution. Simulation results show that the proposed beamformer is able to suppress the fast-moving jamming efficiently for UAV navigation.

Fast-Moving Jamming Suppression for UAV Navigation: A Minimum Dispersion Distortionless Response Beamforming Approach

A projection matrix design method for MSE deduction in adaptive compressive sensing

Compressive sensing (CS) has been widely used in vehicular technology including compressive spectrum sensing, sparse channel estimation, and vehicular communications. The complete procedure of CS consists of sparse representation, sparse measurement, and sparse recovery. In the CS, measurement matrix $\mathbf {\Phi }$ is utilized to sample a sparse signal, while sensing matrix $\mathbf {\Psi }$ is exploited for sparse recovery. It is important to properly design the sensing matrix that significantly affects the signal recovery performance. This paper addresses the issue of the sensing matrix design for the CS with multiple measurement vectors (MMV). We first study sufficient conditions for the MMV-CS and then develop three sensing matrix design approaches in different situations. The proposed methods have their roots in the principle of minimum variance distortionless response (MVDR) beamformer. This allows us to optimally determine the sensing matrix in the framework of the MVDR, which amounts to minimizing local cumulative cross-coherence between $\mathbf {\Phi }$ and $\mathbf {\Psi }$ while keeping the columns of $\mathbf {\Psi }$ highly correlated to their counterparts of $\mathbf {\Phi }$ . Simulation results are presented to verify the effectiveness of the proposed methods.

Liang Zhang

Papers

Projection matrix design using prior information in compressive sensing

Robust minimum dispersion distortionless response beamforming against fast-moving interferences

Fast-Moving Jamming Suppression for UAV Navigation: A Minimum Dispersion Distortionless Response Beamforming Approach

A projection matrix design method for MSE deduction in adaptive compressive sensing

Sensing Matrix Design for MMV Compressive Sensing: An MVDR Approach