Mainlobe Interference Suppression Research Articles

Mainlobe Interference Suppression Based on Compressive Sensing and Covariance Matrix Reconstruction

Mainlobe Interference Suppression Based on Compressive Sensing and Covariance Matrix Reconstruction

AWB-FCNN Algorithm for Mainlobe Interference Suppression

AWB-FCNN Algorithm for Mainlobe Interference Suppression

Robust Mainlobe Interference Suppression Based on Joint Oblique Projection for Dual Polarization Conformal Array

In this paper, a robust mainlobe interference suppression algorithm is proposed in a space-polarization joint domain based on a dual-polarized conformal array. An oblique projection filter can eliminate the interference while retaining the desired signal information. However, little deviation in signal parameters will lead to significant performance degradation. To solve such problems, the joint steering vector of the desired signal is corrected by solving the quadratic convex optimization problem. Parameters of interferences are first estimated by using multiple signal classification (MUSIC) joint spectrum, and then, the interference covariance matrix can be reconstructed individually to further estimate the interference steering vector. After that, the joint oblique projection operator can be established by the estimated steering vectors. Consequently, an antijamming beamformer can be obtained by the joint oblique projection filter to achieve robustness on the interference parameter estimation deviation and desired signal mismatch. Simulation experiments verify the effectiveness of the proposed method.

Mainlobe Interference Suppression for Radar Network via RPCA-Based Covariance Matrix Reconstruction

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.

Main lobe interference suppression by FDA-MIMO radar based on a novel log function frequency offset

Frequency diverse array (FDA), which can be combined with Multiple-input-Multiple-output (MIMO) to improve the performance of system, and the range information provided by FDA-MIMO radar provides a new idea for suppressing main lobe interference. However, the periodicity of the angle-range two-dimensional beam pattern also causes some safety hazards to the radar beamforming process. When the interference is located in the grating lobe of FDA-MIMO radar, the related algorithm of beamforming will appear serious distortion. The traditional nonlinear frequency offset method to remove the periodicity of the beam pattern will cause the problem of large main lobe width. In this paper, a new log function frequency offset is proposed, which can not only achieve the periodic removal of the FDA-MIMO radar beam pattern but also has a narrower main lobe bandwidth of 3dB compared with the traditional nonlinear frequency offset method

Multiple Mainlobe Interferences Suppression Based on Eigen-Subspace and Eigen-Oblique Projection.

When the desired signal and multiple mainlobe interferences coexist in the received data, the performance of the current mainlobe interference suppression algorithms is severely challenged. This paper proposes a multiple mainlobe interference suppression method based on eigen-subspace and eigen-oblique projection to solve this problem. First, use the spatial spectrum algorithm to calculate interference power and direction. Next, reconstruct the eigen-subspace to accurately calculate the interference eigenvector, then generate the eigen-oblique projection matrix to suppress mainlobe interference and output the desired signal without distortion. Finally, the adaptive weight vector is calculated to suppress sidelobe interference. Through the above steps, the proposed method solves the problem that the mainlobe interference eigenvector is difficult to select, caused by the desired signal and the mismatch of the mainlobe interference steering vector and its eigenvector. The simulation result proves that our method could suppress interference more successfully than the former methods.

Frequency Diverse Array Auto-Scanning Beam Characteristics and Potential Radar Applications

Different from the phased-array with the same carrier frequency for all elements providing only angle-dependent beampattern, frequency diverse array (FDA) using a small frequency offset across its array elements offers new application capabilities due to its both angle and range dependent beampattern, which allows auto beam-scanning capability even without the employment of phase shifters, which are required in phased-array. This paper studied FDA auto-scanning beam characteristics and several potential radar applications. The FDA auto-scanning beam characteristics including range and angle coupling, beam auto-scanning rate and integral array again are analyzed. To fully utilize FDA specific auto-scanning beam characteristics, several potential applications such as radio frequency (RF) stealth or RF localization deception, range-ambiguous clutter suppression and mainlobe interference suppression are discussed. Moreover, several discussions about space-time adaptive processing and possible future investigations are provided. This paper is formulated from a top-level system description, which aims to introduce this promising FDA technique to radar community so that more investigations can be facilitated.

Mainlobe Interference Suppression via Eigen-Projection Processing and Covariance Matrix Reconstruction in Array Antenna

When the received data contains the desired signal, the performance of most mainlobe interference suppression methods is seriously degraded. To solve this problem, a novel mainlobe interference suppression method based on eigen-projection processing and covariance matrix reconstruction for array antenna is proposed in this paper. The method constructs the sidelobe interference plus noise covariance matrix (SINCM) and remove the mainlobe data of the covariance matrix. Then, it uses eigen-projection technology to remove the mainlobe interference in the received data and correct the SINCM to make the proposed method has better ability to suppress the mainlobe interference and sidelobe interference when the received data contains the desired signal. Simulation results demonstrate the superior performance of the proposed beamformer relative to other existing methods.

An Interference Suppression Method for Multistatic Radar Based on Noise Subspace Projection

The mainlobe jamming suppression technique is one of the key techniques of radar electronic counter-countermeasure (ECCM). In the view of multistatic radar, the interference signals received with different view angles would be highly coherent, on the contrary, the target echoes would be independent due to the radar cross section (RCS) of target would randomly fluctuate as the variation of view angle. As a consequence, After the interference signals have been aligned, a steering vector can be found of interference signals, the energy of the interference signals will be concentrated in a single-rank subspace whereas the energy of the target echo signals will be scattered throughout the whole signal space of the multistatic radar. By exploiting this phenomenon, a new mainlobe interference suppression method for multistatic radar is proposed. In this method, the projection matrix is trained based on the time domain sampling data. The interference signals received by multiple node radars with different view angles can be effectively suppressed through the noise subspace projection operation. Then, a general likelihood ratio test detector used for the residual signals after projection operation is derived. The results of simulations indicate that the proposed method can effectively suppress various kinds of mainlobe jamming and improve the detection performance. Furthermore, this method is adaptive to the variation of the interference pattern and the geometric configuration of the node array.

Dynamic mainlobe interference suppression method based on monopulse with gray model Kalman filter

The architecture and processing algorithm of mainlobe interference suppression method is described for nulling the signal from mainlobe electronic jammer and multiple sidelobe electronic jammers while maintaining monopulse angle estimation accuracy on the target. The existing mainlobe interference suppression method is based on monopulse Wiener filtering. However, the Wiener filtering is more suitable to remove static interference. Therefore, the authors present the dynamic mainlobe interference suppression method with Gray Model Kalman filter to resolve this problem. The simulations verify the proposed method.

Mainlobe interference suppression based on independent component analysis in passive bistatic radar

The mainlobe interference is a noticeable issue in the passive bistatic radar (PBR). Because the direction of arrival of interference is close to the target echo, the mainlobe interference can hardly be suppressed effectively by the conventional method. The mainlobe interference will mask the low-level targets of interest, and the blind area of detection will be formed in the direction of the interference source. The mainlobe interference signals, which come from the unrelated transmitters can be assumed to be mutually independent on the statistics. In this study, the method based on the independent component analysis (ICA) is proposed to suppress the mainlobe interference. The method exploits the principal components analysis to extract the mainlobe interference components which are whitened and compressed. Then, the ICA based on the negentropy is applied to separate the mainlobe interference components for the samples in the time domain. The mainlobe interference can be suppressed by the extensive cancellation algorithm with the separated interference sample as the reference. The proposed method based on the ICA is able to suppress the multiple mainlobe interferences and improve the performance of detection in the PBR. The simulation results prove the feasibility of the method proposed in this study.

Transmitter/receiver polarisation optimisation based on oblique projection filtering for mainlobe interference suppression in polarimetric multiple‐input–multiple‐output radar

Controlling the polarisation states of the transmitter and the receiver enables improving the performance of radar systems, especially for mainlobe interference suppression applications. In this study, the authors consider the design of optimal polarisations at both of the radar transmitter and receiver for mainlobe interference suppression. They propose the polarimetric multiple-input-multiple-output (MIMO) radar which combines the advantages of MIMO systems with the advantages offered by optimally choosing the transmitter polarisation or the receiver polarisation to achieve a better anti-jamming performance. The polarisation diversity is employed in the transmit array and the receive array. The interferences can be suppressed by the oblique projection filter. Based on the output signal-to-interference-plus-noise ratio analysis, the optimal transmitter polarisation and the receiver polarisation can be obtained. Simulation results demonstrate that interference can be effectively suppressed with the radar configuration, and better interference suppression performance can be achieved with the optimal transmitter polarisation and receiver polarisation.

Mainlobe interference suppression via transmit phase codes optimisation in MIMO radar systems

Recent studies allow mainlobe interference suppression in MIMO radar systems by weighting the random phase codes at transmitter. However, the performance of interference suppression is not stable when the phase codes are generated randomly, especially in the case of the small number of the elements or a large number of interferences. A transmit phase codes optimisation method is proposed. The modified transmit steering vector is constructed by combining the transmit phase codes and the steering vector of the expected signal. The problem can be solved by minimising the projection of the modified transmit steering vector onto the interference subspace. Simulations verify the effectiveness of this method.

Transmitter polarization optimization with polarimetric MIMO radar for mainlobe interference suppression

Polarization diverse design of the transmit waveforms based on the property of the target scattering matrix provides better performance than transmitting waveforms with fixed polarizations, especially for mainlobe interference suppression applications. In this work, we propose a radar system combines the advantages of multiple-input–multiple-output (MIMO) systems with the advantages offered by optimally choosing the transmitter polarizations, in order to achieve a better mainlobe interference suppression performance. The polarization diversity is employed in the transmit array, while the receive array adopts 2-D vector sensors to measure the horizontal and vertical components of the received signal. The optimal transmitter polarizations can be obtained by maximizing the output signal-to-interference-plus-noise ratio (SINR) in each coherent processing interval (CPI). In addition, the discrimination method for the target and the interference is studied in this paper, along with the target scattering matrix estimation method. Simulation results demonstrate that interference can be effectively discriminated and suppressed with the radar configuration, and better interference suppression performance is achieved with the optimal transmitter polarizations.

Robust mainlobe interference suppression for coherent interference environment

A mainlobe 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 and robustness of the proposed method.

Multiple mainlobe interferences suppression based on subspace matrix filtering and covariance matrix reconstruction

In order to suppress multiple mainlobe interferences and sidelobe interferences simultaneously, a mainlobe interference suppression algorithm is proposed. In this algorithm, the number of mainlobe interferences is estimated through a matrix filter at first. Then, the eigenvectors associated with mainlobe interference are determined and the eigen-projection matrix can be calculated. Next, the sidelobe-interference-plus-noise covariance matrix is reconstructed through eigenvalue replacement procedure. Finally, we can get the adaptive weight vector. Simulation results demonstrate the effectiveness of the proposed method when multiple mainlobe interferences exist.

Mainlobe interference suppression with eigenprojection algorithm and similarity constraints

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.

Improved Mainlobe Interference Suppression Based on Blocking Matrix Preprocess

For the problem of mainlobe direction shifting that is caused by the mainlobe interference suppression based on blocking matrix preprocess, an effective method is proposed which is based on the combination of diagonal loading and linear constraints. Therein, the reason for mainlobe direction shifting is analyzed and found to be that the covariance matrix mismatch exists in the realization of the adaptive beamforming. Therefore, the diagonal loading processing is used to overcome the mismatch and correct the mainlobe direction shifting, and the linear constraints are used to make sure of the beam pattern nulling in the interference directions; then the desired performance of adaptive beamforming is obtained. Simulation results attest the correctness and effectiveness of the proposed method, and they also show that the proposed method is insensitive to the selection of diagonal loading level, which means the loading factor is easy to choose.

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

A mainlobe interference suppression method is proposed based on eigen-projection processing and covariance matrix reconstruction. In the proposed method, the eigen-projection matrix is calculated based on the eigenvector of mainlobe interference to suppress the mainlobe interference in received echo data. The interference covariance matrix is reconstructed after the eigenvalue of mainlobe interference is replaced by the average value of noise eigenvalues to eliminate the effect of mainlobe interference to adaptive weight vector. Finally, the output of adaptive beamforming is calculated by processed echo data and adaptive weight vector obtained based on reconstructed covariance matrix. Simulation results show that the proposed method can suppress the mainlobe interference effectively and achieve excellent performance .

Applying Auxiliary Array to Suppress Mainlobe Interference for Ground-Based Radar

When ground-based radar is applied to the detection of long-range targets, the interference around the target is located primarily in the radar mainlobe, such that the radar detection capabilities are significantly degraded. Conventional methods of interference suppression mainly focus on sidelobe interference suppression. In the presence of mainlobe interference, sidelobe interference suppression methods will distort and offset the mainlobe peak, so that mainlobe interference could not be effectively suppressed. However, there is currently no suitable mainlobe interference suppression method. In this letter, based on a large aperture auxiliary array, a technique of mainlobe interference suppression is proposed for ground-based radar. The geometry of auxiliary array with uniform array spacing is designed based on conventional sidelobe interference cancellation theory at first. Then, two adaptive beamforming methods for mainlobe interference suppression are presented based on the rules of minimum mean square error (MMSE) and maximum signal-to-interference-plus-noise ratio (MSINR). Since sparse large aperture auxiliary array with uniform array spacing results in high grating lobes, the array spacing is redesigned based on a modified genetic algorithm to reduce the grating lobes. The effectiveness of proposed methods is illustrated by simulations.