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In the present complex electromagnetic environment, radar target detection is threatened by different kinds of interferences, especially mainlobe deceptive interference, which occupies the same energy distributions of targets spatially, meaning that targets and interferences cannot be discriminated. To make matters worse, the number of suppressible interferences is limited by the number of physical array elements, leading to the degradation of the suppression performance of traditional radar. In this work, we propose a frequency-increment-based interference suppression method for minimum redundancy frequency diverse array multiple-input multiple-output (MR-FDA-MIMO) radar, which effectively solves the aforementioned two problems. The interference suppression method consists of two steps: (i) in the sidelobe barrage interference suppression stage, the interference-plus-noise covariance matrix is reconstructed to overcome the influence of the true targets and mainlobe deceptive interference on the performance of the beamformer; (ii) in the mainlobe deceptive interference suppression stage, a nonadaptive beamforming method is employed to suppress mainlobe deceptive interference and overcome the impact of insufficient virtual samples on interference suppression performance. Additionally, we design a frequency-increment-based MR-FDA-MIMO radar, fully utilizing the advantages of the virtual array to enhance interference suppression performance and increase the number of interferences. Numerical experiments undertaken demonstrate the effectiveness of the algorithm under different scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperated range ambiguous clutter suppression method for moving target detection in the background of range-ambiguous clutter with a phased array (PA)–frequency diverse array (FDA) dual-mode radar. With the FDA mode, the range-ambiguous clutters are discriminated in the transmit spatial frequency domain, and thus the clutter covariance matrixes (CCMs) corresponding to unambiguous and ambiguous regions can be independently estimated. Therefore, the enhanced CCM can be reconstructed by using a linear combination of these distinguished CCMs from different range regions. With the PA mode, the enhanced CCM is applied, thus taking advantages of its high beampattern gain as well as alleviating the range ambiguous clutter suppression problem. Simulation results are presented to verify the effectiveness of the proposed method in range-ambiguous clutter scenarios. [ABSTRACT FROM AUTHOR]

Clutter suppression is a key problem for airborne radar, and space-time adaptive processing (STAP) is a core technology for clutter suppression and moving target detection. However, in practical applications, the non-uniform time-varying environments including clutter range dependence for non-side-looking radar lead to the training samples being unable to satisfy the sample requirements of STAP that they should be independent identical distributed (IID) and that their number should be greater than twice the system's degree of freedom (DOF). The lack of sufficient IID training samples causes difficulty in the convergence of STAP and further results in a serious degeneration of performance. To overcome this problem, this paper proposes a novel autoencoder neural network for clutter suppression with a unique matrix designed to be decoded and encoded. The main challenges are improving the accuracy of the estimation of the clutter-plus-noise covariance matrix (CNCM) for STAP convergence, designing the form of the data input to the network, and making the network successfully explored to the improvement of CNCM. For these challenges, the main proposed solutions include designing a unique matrix with a certain dimension and a series of covariance data selections and matrix transformations. Consequently, the proposed method compresses and retains the characteristics of the covariances, and abandons the deviations caused by the non-uniformity and the deficiency of training samples. Specifically, the proposed method firstly develops a unique matrix whose dimension is less than half of the DOF, meanwhile, it is based on a processing of the selected clutter-plus-noise covariances. Then, an autoencoder neural network with l 2 regularization and the sparsity regularization is proposed for the unique matrix to be decoded and encoded. The training of the proposed autoencoder can be achieved by reducing the total loss function with the gradient descent iterations. Finally, an inverted processing for the autoencoder output is designed for the reconstruct ion of the clutter-plus-noise covariances. Simulation results are used to verify the effectiveness and advantages of the proposed method. It performs obviously superior clutter suppression for both side-looking and non-side-looking radars with strong clutter, and can deal with the insufficient and the non-uniform training samples. For these conditions, the proposed method provides the relatively narrowest and deepest IF notch. Furthermore, on average it improves the improvement factor (IF) by 10 dB more than the ADC, DW, JDL, and original STAP methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, a new approach to space-time adaptive processing (STAP) is proposed that is robust against different deviations in real application, such as array calibration error, steering vector mismatch and so on. The proposed method aims at designing spatial-temporal separable filter by using the magnitude and phase constrained iterative optimization. Applying multiple magnitude and phase constraints on the uncertainty set, the main-beam of the two-dimensional (2-D) frequency response of STAP can be maintained, thus effectively circumventing the performance loss due to the steering vector mismatch. Numerical results demonstrate that, by introducing the magnitude and phase constraints for STAP, the proposed robust 2-D beamformer considerably outperforms the conventional linearly constrained minimum variance (LCMV) algorithm in terms of robustness. [ABSTRACT FROM AUTHOR]

The clutter suppression and target radial velocity estimation are essential in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. In reality, the heterogeneous clutter, the image coregistration error, and channel mismatch will remarkably decline the estimation performance of the target radial velocity. To address these issues, a robust radial velocity estimation algorithm is proposed in this letter. Based on the joint-pixel signal model, the joint-pixel normalized sample covariance matrix (JPNSCM) is employed to mitigate the effect of heterogeneous clutter, and the shift vector determined by JPNSCM is used to obtain the actual target steering vector. Then, the adaptive matched filtering algorithm is adopted to estimate the target radial velocity. Compared with traditional estimation algorithms, the proposed method obtains better performance in both simulations and real SAR data experiments. [ABSTRACT FROM AUTHOR]

Sum ($\Sigma$) and difference ($\Delta$) beampatterns are commonly utilized to estimate the bearings of targets in the radar system. However, the nulls of $\Delta $ -pattern are not always aligned with those of $\Sigma $ -pattern, and this phenomenon would become more severe in practical scenario since the array errors exist. Meanwhile, the sidelobe level (SLL) of the array would also increase due to the unavoidable errors. The above-described factors would dramatically decrease the performance of radar in the adaptive suppression of interference. To increase the output signal-to-interference-plus-noise ratio of a radar system, this paper presents an approach to design the low SLL $\Sigma \Delta $ -patterns where the nulls of the $\Delta $ -pattern, excluding the central null, are always aligned with those of the $\Sigma $ -pattern in the presence of errors. Two schemes are addressed to obtain the covariance matrix for null-alignment constraint, which is exploited to force nulling in desired angles accordingly. In the sequel, the matrix tapering method is adopted to broaden the nulls and enhance the robustness for the possible errors. With the proposed approach, the low SLL and nulls alignment can be achieved simultaneously. Numerical examples are provided and discussed to demonstrate the effectiveness of the proposed solution, including the results based on a linearly spaced microstrip array antenna. [ABSTRACT FROM AUTHOR]

Conventional phased-array space-time adaptive processing (STAP) radar combines angle and Doppler domains to realize clutter suppression. However, it suffers from severe performance degradation in electronic countermeasures scenarios, especially in the presence of mainbeam deceptive jamming. In this paper, a multiple-input multiple-output (MIMO) STAP radar is studied with a frequency diverse array (FDA) acting as the transmit array, which is referred to as FDA-MIMO STAP radar. FDA can provide controllable degrees of freedom in both range and angle domains. As a result, in FDA-MIMO STAP radar, the target can be discriminated from the clutter and jamming in joint range-angle-Doppler domains. By combining the range, angle and Doppler domains in FDA-MIMO STAP radar, a space-time-range adaptive processing method is proposed based on subspace projection technique. Simulation results demonstrate the effectiveness of the proposed approach in suppressing the clutter and jamming. [ABSTRACT FROM PUBLISHER]

Frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radars can generate a range-angle two-dimensional transmit steering vector (SV), which is capable of suppressing mainbeam deceptive jamming in the transmit–receive frequency domain by utilizing additional degrees of freedom (DOFs) in the range dimension. However, when there are target SV mismatch, covariance matrix estimation error and target contamination, the jamming suppression performance degrades severely. In this paper, a robust adaptive beamforming algorithm for anti-jammer application based on covariance matrix reconstruction is proposed in FDA-MIMO radar. In this method, the residual noise is further determined by using the spatial power spectrum estimation approach, which results in improved estimation accuracy of the signal covariance matrix and the desired target SV. The jamming SV is obtained from vectors in the intersection of two subspaces (namely, the signal-jamming subspace derived from the sample covariance matrix (SCM) and the jamming subspace generated from the jamming covariance matrix) by an alternating projection algorithm. Furthermore, the jamming power is obtained by exploiting the orthogonality between the different SVs. With the obtained parameters of target and jamming, the optimal adaptive beamformer weight vector is calculated. Simulation results demonstrate that the proposed algorithm can cope with the mainbeam deceptive jamming suppression under various model mismatches and has excellent performance over a wide range of signal-to-noise ratios (SNRs). [ABSTRACT FROM AUTHOR]

Medium pulse repetition frequency (MPRF) is an important mode in airborne radar system. Since MPRF mode brings both Doppler and range ambiguities, it causes difficulty for the airborne radar to suppress ground or sea clutter. In recent years, it has been pointed out that the frequency diverse array (FDA) radar is capable of separating the range ambiguous clutter, which is helpful for the airborne radar in detecting weak moving targets originally buried in ambiguous clutter. To further improve the ambiguous clutter separation performance, an enhanced pre-STAP beamforming for range ambiguous clutter suppression is proposed for the vertical FDA planar array in this paper. With consideration of range dependence of the vertical spatial frequency, a series of pre-STAP beamformers are designed using a priori knowledge of platform and radar parameters. The notches of the beamformers are aligned with the ambiguous clutter to extract echoes from desired range region while suppressing clutter from ambiguous range regions. The notches can be widened by using covariance matrix tapering technique and the proposed method can improve the performance of range ambiguous clutter separation with limited degrees-of-freedom (DOFs). Simulation examples show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

For statistic space-time adaptive processing (STAP), a critical issue is estimating the clutter covariance matrix (CCM). However, sufficient training samples are difficult to obtain that satisfy the independent and identically distributed (IID) condition. It is because of the realistic heterogeneous environment faced by airborne radar. Moreover, one should eliminate contaminated training samples before CCM estimation. Aiming at the problems of the computational complexity and susceptibility to the outlier of the traditional generalized inner product (GIP) method, a clutter subspace-based training sampling selecting method is proposed combined with specific distribution in the space-time plane of clutter spectrum. Theoretical analysis and simulation results verified the proposed method and indicate that the proposed method is easy to construct CCM and has lower computational complexity and sensitivity to outliers. [ABSTRACT FROM AUTHOR]

Highlights • Propose novel robust beamformer against large errors using multiple uncertainty sets. • Use multiple uncertainty sets to describe large errors. • Propose two algorithms to solve the derived nonconvex problem. • The proposed method offers significant SINR improvement in existence of large errors. Abstract Many efforts have been recently devoted to adaptive beamformers using one uncertainty set to achieve robustness against steering vector mismatch. However, these robust adaptive beamformers based on worst-case performance optimization suffer from difficulty in selecting an appropriate size of the uncertainty set. Besides, their performances degrade dramatically if large steering vector mismatch occurs. This motivates us to develop a robust beamforming approach to handle the large uncertainty set problem. In this paper, we utilize multiple small uncertainty sets, instead of using a single large set, to cover the whole large uncertainty region. Two efficient algorithms, iterative semidefinite programming-based robust adaptive beamforming (ISDP-RAB) and iterative linearization-based robust beamforming (IL-RAB), are developed to solve the nonconvex problem. Simulation results indicate that the proposed method offers a significant performance improvement in case of large steering vector mismatch. [ABSTRACT FROM AUTHOR]