Considering that the performance of adaptive arrays is sensitive to any type of mismatches, an innovative robust adaptive beamforming method based on covariance matrix reconstruction, subspace decomposition, steering vector estimation and correction is proposed. Based on Capon spatial spectrum, a group of angle sets containing all interfering signals are determined, and the interference covariance matrix can be reconstructed with a smaller integration interval. On the other hand, the sample covariance matrix can be decomposed into signal subspace and interference-plus-noise by using the principle of maximum correlation. Based on the interference-plus-noise subspace and the reconstructed signal-plus-noise covariance matrix, a new convex optimization model is built to estimate the steering vector of the desired signal. Then, an improved projection approach based on signal subspace is designed for correction to improve the robustness against the nominal direction vector mismatches. Simulation results demonstrate that the proposed method achieves better overall performance under multiple mismatches over a wide range of input signal-to-noise ratios.

When jammers move rapidly or an antenna platform travels at high speed, interference signals may move out of the null width in the array beampattern. Consequently, the interference suppression performance can be significantly degraded. To solve this problem, both the null broadening technique and robust adaptive beamforming are considered in this paper. A novel null broadening beamforming method based on reconstruction of the interference-plus-noise covariance (INC) matrix is proposed, in order to broaden the null width and offset the motion of the interfering signals. In the moving case, a single interference signal can have multiple directions of arrival, which is equivalent to the existence of multiple interference sources. In the reconstruction of the INC matrix, several virtual interference sources are set up around each of the actual jammers, such that the nulls can be broadened. Based on the reconstructed INC and signal-plus-noise covariance (SNC) matrices, the steering vector of the desired signal can be obtained by solving a new convex optimization problem. Simulation results show that the proposed beamformer can effectively broaden the null width and deepen the null depth, and its performance in interference cancellation is robust against fast-moving jammers or array platform motion. Furthermore, the null depth can be controlled by adjusting the power parameters in the reconstruction process and, if the direction of interference motion is known, the virtual interference sources can be set to achieve better performance.

If the active interference enters the main lobe of a radar antenna, the detection performance of the radar system will be severely degraded. Many traditional techniques for suppressing this type of interference are not sufficiently effective. Through analysis of the characteristics of linear frequency modulated echoes and jamming in the fractional Fourier transform (FrFT) domain, this paper proposes a novel algorithm for moving ground target detection, which can eliminate multipath signals effectively and suppress the main-lobe jamming significantly. First, by choosing the right pulse number taken into the accumulation, the effect of the active interference and moving target echoes can be suppressed simultaneously in the fractional domain, and then the FrFT of multipath signals can be obtained. Second, the FrFT of moving target echoes can be extracted by differential cancellation, and the second coherent integration is employed to detect the moving targets and construct the FrFT-Doppler plane. By constant false-alarm rate detection, the peaks can be extracted from the FrFT-Doppler plane, and then the target parameters can be estimated properly. It is not necessary to estimate the direction of arrival of each interference or limit the number of interferences in the main lobe. The processing results of simulated and actual datasets verify that for the weak or low-velocity targets, the detection performance of the proposed algorithm is superior to that of conventional match filter processing.

As unmanned aerial vehicles and other small, low-flying, and low-speed aircrafts are being extensively used, studies on their detection are being extensively conducted in radar application research. However, weak echoes, low Doppler frequencies, and target echoes mixed with ground clutter can considerably degrade the detection performance. Therefore, specific methods for the detection of such targets should be devised. We propose herein a phase compensation and coherent accumulation algorithm based on the fractional Fourier transform (FRFT) for detection and speed estimation of this type of target. First, the energy of the target echo is converged using the FRFT. Next, the phase between the peaks of the target echo is analyzed. Phase compensation and coherent accumulation determined from the expected target speed in the fractional domain eliminate ground clutter and further improve the signal-to-interference-plus-noise ratio. Finally, constant false alarm rate detection is used to identify the target, for which radial speed can be estimated directly according to the peak coordinates. The validity of the algorithm is verified via data simulation and application to real data.

This study presents a robust digital beamforming (DBF) method for wideband digital array radar, which uses linear frequency modulation (LFM) waveforms. Firstly, received signals are digitally dechirped, followed by narrowband filtering and decimation. Then the authors generate time‐variant factors to compensate phase perturbations induced by the dechirp processing. It is proved that the wideband LFM signals can be treated as narrowband signals after compensation. Finally, a robust adaptive narrowband DBF method based on uncertain set constraints and interference‐plus‐noise matrix reconstruction is applied to suppress the interferences. The proposed method is computationally efficient and feasible for real‐time applications. It behaves robustly in the presence of direction of arrival estimation errors across a wide range of signal‐to‐noise ratios. Simulations demonstrate the effectiveness and feasibility of the proposed method.