The standard Capon beamformer (SCB) is known to suffer from severe performance degradation when there is a mismatch between the presumed signal steering vector and the actual one. There may be several reasons leading to this result such as mutual coupling between array elements. The problem of adaptive beamforming in the presence of mutual coupling is studied based on a uniform linear array (ULA). By setting a group of auxiliary elements on each side of the ULA, the authors prove that the proposed method can inherently compensate the effect caused by the mutual coupling and greatly decrease the sensitivity of the SCB against mutual coupling. This technique can also be applied to most of the existing robust beamforming methods to further improve their performances. Theoretical analysis and simulation results demonstrate the robustness and effectiveness of this technique.

Non-sensitive adaptive beamforming against mutual coupling

In this study, a novel algorithm for detecting the ground-moving targets and estimating their motion parameters is proposed Based on the fact that clutter has a certain phase pattern, clutter cancellation is accomplished using a phase modification and finite impulse response filtering Then a type of coherent Hough transform is utilised to estimate the moving target velocity By employing the coherent Hough transform, the proposed algorithm leads to the significant signal-to-noise ratio (SNR) and signal-to-clutter ratio improvement in comparison with similar previous Hough- or Radon-based ground-moving target indication and velocity estimation methods In fact, uncorrelated noise samples have random phases that cancel each other, therefore, a noticeable SNR improvement would be achieved Moving target detection is done by conventional constant false alarm rate (CFAR) techniques over different range gates

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rsn.2016.0262

Coherent method for ground-moving target indication and velocity estimation using Hough transform

Conventional space-time adaptive processing (STAP) requires large numbers of independent and identically distributed (i.i.d) training samples to ensure the performance of clutter suppression, which is hard to be achieved in practical complex nonhomogeneous environment. In order to improve the performance of clutter suppression with small training sample support, a robust and fast iterative sparse recovery method for STAP is proposed in this paper. In the proposed method, the sparse recovery of clutter spatial-temporal spectrum and the calibration of space-time overcomplete dictionary are achieved iteratively. Firstly, the robust solution of sparse recovery is derived by regularized processing, which can be calculated recursively based on the block Hermitian matrix property, afterwards the mismatch of space-time overcomplete dictionary is calibrated by minimizing the cost function. The proposed method can not only alleviate the effect of noise and dictionary mismatch, but also reduce the computational cost caused by direct matrix inversion. Finally, the proposed method is verified based on the simulated and the actual airborne phased array radar data, which shows that the proposed method is suitable for practical complex nonhomogeneous environment and provides better performance compared with conventional STAP methods.

Robust and fast iterative sparse recovery method for space-time adaptive processing

In order to reduce the amount of echo data and achieve a wider observation swath, a parametric sparse representation method for the motion parameter estimation of ground moving targets is proposed with low pulse repetition frequency (PRF). First, the displaced phase center antenna technique is introduced to cancel the clutters. Second, since range cell migration is not subject to PRF limitations, Hough transform is adopted to estimate the across-track velocities and range positions of moving targets. Then, the parametric method is put forward to estimate the along-track velocities and azimuth positions based on the parametric over-complete chirplet basis matrix, where the basis matrix is determined by the along-track velocities of moving targets. The proposed algorithm can adaptively refine the basis matrix to achieve the optimal sparse representation result. Meanwhile, the azimuth position is obtained by the optimal sparse representation result. Finally, the feasibility and the effectiveness of the proposed method are proved by the simulation results.

Parametric Sparse Representation Method for Motion Parameter Estimation of Ground Moving Target

The authors propose a novel compressive sensing (CS)-based Dechirp-Keystone algorithm (DKA) for synthetic aperture radar (SAR) moving target imaging, which is called the CS-DKA. The DKA can focus on moving targets in range-Doppler domain efficiently through only keystone transform (KT), complex multiplication and Fourier transform (FT)/inverse Fourier transform (IFT) operations. It has been shown that the non-interpolation implementation of KT can be expressed by an orthonormal basis, and it is known that the complex multiplication and FT/IFT are linear and invertible; therefore, the Dechirp-Keystone operator (DKO) is also linear and invertible. In the proposed algorithm, the authors take the inverse of DKO (IDKO) rather than the exact SAR echo model to construct the representation basis in the CS frame owing to its high implementation efficiency. After that, a random transmitting/receiving scheme is considered, to implement the down-sampling operation, and then reconstruct the moving target image by solving a regularisation problem. Both simulated and real SAR data are processed to show that the CS-DKA with down-sampled data can focus the target as well as the conventional DKA does with full data, and at the same time can achieve much lower sidelobes.

http://ir.nssc.ac.cn/bitstream/122/4646/1/201595509.pdf

Novel compressive sensing-based Dechirp-Keystone algorithm for synthetic aperture radar imaging of moving target

Multi-channel synthetic aperture radar (SAR) system has excellent performance of main-lobe clutter suppression. However, the resulting enormous amount of sampling raw data increases storage and transmission load. To alleviate such payloads, the authors propose a SAR/ground moving target indication (GMTI) method using compressive sensing (CS) with a very limited number of echo samples, based on the fact that the moving targets are usually sparse although clutter scattering centres are non-sparse in most cases. In the proposed method, dual channel SAR data are sampled sparsely in the azimuth direction and jointly processed. Firstly, a transform matrix is constructed to separate the energy support areas of moving targets from that of all scattering centres. Then, the authors can roughly obtain the energy support areas of all scattering centres via CS. Finally, based on the acquired energy support areas above, GMTI is achieved by solving a weighted l 1 optimisation problem. Simulated and real data experiments demonstrate that the proposed method performs well with reduced sampled raw data, even if clutter scattering centres have a low-sparse level.

Compressive sensing-based ground moving target indication for dual-channel synthetic aperture radar

This paper deals with the problem of robust adaptive array beamforming for cyclostationary signals. By exploiting the signal cyclostationarity, blind adaptive beamforming algorithms have found numerous important applications in signal processing. However, these methods suffer from severe performance degradation even if there is a small mismatch in the cycle frequency of the desired signal. In this paper, we propose the efficient robust scheme based on the conventional cyclic adaptive beamforming (CAB) algorithm against the mismatch in the cycle frequency. The performance of the proposed techniques is compared with that of existing blind cyclic beamforming approaches by simulation examples, the results of which demonstrate a marked improvement in the robustness against imprecisely knowledge of the cycle frequency

Robust blind adaptive beamforming for cyclostationary signals

Modern aerial refueling aircraft and its aerial refueling technology have become an important measure to enhance the mobility and strike capability of aviation forces, and are highly valued by countries all over the world. With the continuous development of computer image and machine learning, it is possible to locate and identify the refueling cone sleeve in real time in the process of aerial refueling docking. At present, the mainstream method is target recognition based on deep learning, but the premise of deep learning is to cut a large number of data samples. Aiming at the problem that deep learning requires a lot of manpower and energy to cut training samples, this paper proposes an automatic cutting method of cone sleeve training samples based on the combination of color features and circular features to realize the automatic pre-processing of massive sample data Cutting, greatly improve work efficiency, lay the foundation for UAV autonomous refueling.

Zhang Jie

Papers

Compressive sensing-based ground moving target indication for dual-channel synthetic aperture radar

Robust blind adaptive beamforming for cyclostationary signals

Research On Automatic Cutting Method of Oil Cone Sleeve Sample