Mainlobe Interference Suppression Based on Eigen-Projection Processing and Covariance Matrix Reconstruction

Amainlobe interference suppression method is proposed in this paper, which can still work when the signal of interest (SOI) is present in the training data. In this method, the iterative adaptive approach (IAA) is applied to spatial spectrum estimation at first. Then, IAA spatial spectrum is used to reconstruct the interference-plus-noise covariance matrix (INCM). Next, the eigenvector associated with mainlobe interference in INCM is determined and the eigen-projection matrix can be calculated to suppress the mainlobe interference. Meanwhile, the sidelobe-interference-plus-noise covariance matrix (SINCM) can be reconstructed. Finally, the adaptive weight vector is obtained. One main advantage is that the proposed method can deal with coherent mainlobe interference and sidelobe interferences simultaneously. Simulation results demonstrate the effectiveness of the proposed method.

Traditional adaptive beamformers are sensitive to model mismatch and they have poor performances when the input signal-to-noise ratio (SNR) is close to or more than interference-to-noise ratio (INR). In this letter, a novel robust adaptive beamforming (RAB) algorithm based on twice interference-plus-noise covariance (INC) matrix reconstruction is proposed. Firstly, the desired signal (DS) steering vector is estimated by the alternating projection algorithm based on the antenna array geometry and angular sector prior information. Then, the proposed method obtains the eigenvalue corresponding to DS by correlation coefficient and takes the place of it by noise power to obtain the first INC matrix. Furthermore, the second precise covariance matrix is reconstructed based on the theoretical INC matrix expression. The interferences powers are estimated by the theoretical formulation of the interference covariance matrix (ICM), which is derived from the first INC matrix. The simulation results demonstrate that the proposed algorithm achieves good performance especially when the input SNR is high, and its performance is close to the optimal beamformer in the case of look direction error.

Radar networks can provide spatial diversity by observing targets from different perspectives with adequately separated receiving stations. This article proposes a mainlobe interference suppression approach for radar networks based on covariance matrix reconstruction. First, we formulate the mainlobe interference suppression under multiradar configuration as a multichannel signal enhancement problem and perform range and Doppler equalization to improve interferences’ spatial correlation. Then, we estimate the signal and interference covariance matrix from the sampling covariance matrix by solving a robust principal component analysis (RPCA) model. After that, we devise a time-domain-constrained (TDC) linear estimator dependent on the reconstructed covariance matrices to enhance target echo. To remove those duplicate false targets caused by signal enhancement and estimate the state of the physical targets, we establish and solve a generalized multiple measurement vector (GMMV) model based on the fact that targets have unique locations in the state space. Finally, we confirm the effectiveness of the proposed approach with numerical experiments and demonstrate better performance in comparison with the subspace projection method.

Here a novel mainlobe interference suppression technique based on eigenprojection algorithm and beam pattern similarity constraints is proposed. This method can effectively suppress the multiple mainlobe interferences and sidelobe interferences. The output of adaptive beamforming is calculated via adaptive weight vector obtained by the convex objective function and processed echo data based on the eigenprojection matrix. The simulations have been presented to evaluate the theoretical analysis.

In order to solve the problem of mainlobe interference in small hydroacoustic array signal processing, this paper proposes a beamforming method based on the high-resolution direction of arrival (DOA) estimation and interference coherence matrix (ICM) reconstruction. The DOA estimation is first performed using an improved sparse iterative covariance-based (SPICE) method, unaffected by the coherent signal, and it can provide highly accurate DOA estimation for multiple targets. The fitted signal energy distribution obtained from the SPICE is then utilized for the reconstruction of the signal coherence matrix. The reconstructed ICM matrix is used to construct a blocking masking matrix and an eigen-projection matrix to suppress the mainlobe interference signal. Compared with existing methods, the method in this paper possesses better mainlobe interference suppression ability. Within the mainlobe interference interval angle of 3° to 13.5° from the signal of interest (SOI) based on eight-element uniform linear arrays, the method in this paper can enhance the signal-to-interference ratio (SIR) by about 15.59 dB on average compared with the interference-free suppression of conventional beamforming (CBF) and outperforms the other interference suppression methods simultaneously. Simulations and experiments demonstrate the effectiveness of this method in mainlobe interference scenarios.

Robust adaptive beamforming (RAB) based on interference-plus-noise covariance (INC) matrix reconstruction can experience performance degradation when model mismatch errors exist, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we devise an efficient RAB technique for dealing with covariance matrix reconstruction issues. The proposed method involves INC matrix reconstruction using an idea in which the power and the steering vector of the interferences are estimated based on the power method. Furthermore, spatial match processing is computed to reconstruct the desired signal-plus-noise covariance matrix. Then, the noise components are excluded to retain the desired signal (DS) covariance matrix. A key feature of the proposed technique is to avoid eigenvalue decomposition of the INC matrix to obtain the dominant power of the interference-plus-noise region. Moreover, the INC reconstruction is carried out according to the definition of the theoretical INC matrix. Simulation results are shown and discussed to verify the effectiveness of the proposed method against existing approaches.

An advanced interference plus noise covariance matrix (ICM) reconstruction approach was proposed to facilitate robust adaptive beamforming for antenna sensor arrays The proposed method involves the calculation of the eigenvectors and eigenvalues of the signal of interest (SOI) and the interference through eigen-projection processing. Following this, the covariance matrix without the required signal component is formed by amending the diagonal matrix of the eigenvalue Then, the mixed ICM is constructed using the shrinkage method. A quadratically constrained quadratic programming (QCQP) dilemma can be worked out to ascertain the difference between the proposed steering vector (SV) and the true vector. The benefit of the proposed method is that the ICM can be obtained on the move and also can be obtained more precisely in the situation of high signal-to-noise ratio (SNR). Finally, the results of the theoretical analysis and simulation demonstrate the robustness and efficacy of the proposed method.

The major problem in adaptively processing data from a large array is inversion of the covariance matrix. The matrix must be well-conditioned to be inverted. The number of independent samples that is needed to form a well-conditioned matrix is at least twice the number of array elements. The integration time to form a well-conditioned large array covariance matrix may then be unreasonably long relative to the coherent time of signal in a changing environment. A sub-optimal subarray processing has been developed. The adaptive weight vectors are calculated for each subarray. These weight vectors are linearly combined to form a full-array adaptive weight vector and are applied to the full-array covariance matrix. The required integration time is reduced to that which is needed to form the well-conditioned subarray covariance matrices. In a recent shallow water experiment, a three-dimensional array had been deployed. The array consisted of 5 VLAs each with 30-element spaced evenly on a circular baseline of a radius of 200 m. The subarray processing approach had been used to localize a moving source that had a fast range/bearing change relative to the baseline.

The performance of the conventional Gram-Schmidt orthogonalization of covariance matrix (RGS) beamforming method will decrease significantly when non-ideal factors exists such as the appearances of fast moving interferences, array platform movement. In order to improve the robustness of interference suppression, a derivative constrained Gram-Schmidt orthogonalization of covariance matrix (CRGS) beamforming method with widened nulls is proposed in this paper. In the proposed method, the number of interference P is initially estimated and the first subspace is reconstructed by Gram-Schmidt orthogonalization of the first P columns of sample covariance matrix. Afterwards, the derivative constrained vectors and the second subspace spanned of these vectors are constructed. At last the adaptive weight vector is obtained by orthogonally projecting the quiescent weight vector onto the interference subspace made up of the first subspace and the second subspace. Based on the numeral simulation results, it is verified that the proposed method can form widened nulls and effectively improve the robustness of interference suppression.

Robust adaptive beamforming via covariance matrix reconstruction with diagonal loading on interference sources covariance matrix

Mainlobe interference suppression has been a challenge in radar and communication systems for the last few decades. Here, a new anti-interference approach based on a bistatic radar system is proposed, and mainlobe interference is suppressed through cooperation of the two radars. The interference direction information of a single radar is extracted from the radar scene using multiple signal classification, and the location of the interference is obtained by the cross location method, which uses two different azimuthal angles of the interference. According to the azimuthal information of interference, the blocking matrix method is used to block mainlobe interference and protect the effective echo signal, and adaptive processing technology is adopted to suppress sidelobe interference. Finally, mainlobe interference can be cancelled by the received data of the two radars. A simulation verifies the performance of the model.

When the deceptive targets are in the ambiguious range bin but are received at the same range gate with the desired target by the array, the traditional multiple-input multiple-output (MIMO) radar is not able to discriminate between them. Based on the unique range-dependent beampattern of the frequency diverse array (FDA)-MIMO radar, we propose a novel robust mainlobe deceptive target suppression method based on covariance matrix reconstruction to form nulls at the frequency points of the transmit–receive domain where deceptive targets are located. First, the proposed method collects the deceptive targets and noise information in the transmit–receive frequency domain to reconstruct the jammer-noise covariance matrix (JNCM). Then, the covariance matrix of the desired target is constructed in the desired target region, which is assumed to already be known. The transmit–receive steering vector (SV) of the desired target is estimated to be the dominant eigenvector of the desired target covariance matrix. Finally, the weighting vector of the receive beamformer is calculated by combining the reconstructed JNCM and the estimated desired target SV. By implementing the weighting vector at the receiving end, the deceptive targets can be effectively suppressed. The simulation results demonstrate that the proposed method is robust to SV mismatches and provides a signal-to-jamming-plus-noise ratio (SJNR) output that is close to the optimal.

A new beamformer based on covariance matrix reconstruction is introduced. The essence of the new approach is to eliminate the desired signal component in the sample covariance matrix and thus complex integral operation is avoided in the procession of covariance matrix reconstruction. Besides, the actual array steering vector is estimated by a new technique. Contrary to other reference beamformers, simulation results demonstrate the effectiveness of our proposed method.

Aiming at the problem of beamformer performance degradation caused by coherent signals, We propose an adaptive beamforming algorithm for interference plus noise covariance matrix (INCM) reconstruction. The idea of the proposed algorithm is that the sum of eigenvalues (SE) is not affected by coherent signals, so that the power of the eigenvalues can be used to reconstruct INCM. First, we perform eigenvalue decomposition on the covariance matrix after the forward and backward spatial smoothing, which proves that spatial smoothing does not affect SE. On this basis, a new method of signal power estimation is proposed. The estimated signal power and direction vector are then used to reconstruct INCM. Finally, the weight vector is calculated from the estimated desired signal steering vector (DSDV) and the reconstructed INCM. The innovation of this algorithm lies in the combination of coherent signal processing and beamforming, which effectively improves the output interference plus noise ratio (SINR) performance of the beamformer. Simulation analysis verifies the superiority of the algorithm and the validity of theoretical analysis.

The performance of adaptive beamforming degrades in presence of signal model mismatches. In particular, when the desired signal is present in training snapshots, the adaptive beamforming is quite sensitive to desired signal steering vector mismatch. Therefore, a robust adaptive beamforming for actual system is proposed based on the reconstruction of interference covariance matrix and mismatched steering vector compensation. In the proposed method, the interference covariance matrix is firstly reconstructed by using Root-MUSIC method to estimate Direction-of-Arrival (DOA) of signals, where the desired signal component is removed from the training snapshots. Subsequently, the mismatched desired signal steering vector is compensated by solving a quadratically constrained quadratic programming problem. Simulation results show that the performance of proposed algorithm outperforms the existing robust adaptive beamforming, and the output signal-to-interference-plus-noise ratio (SINR) is close to optimal values. Therefore, the proposed algorithm could be significantly effective for actual system.