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This paper investigates the problem of mainlobe deceptive jammer suppression via joint transmit–receive design in a multiple-input multiple-output (MIMO) radar. At the design stage, a discrete element-pulse coding (DEPC) phase is implemented in both the slow-time pulses and transmit array antennas. After decoding and compensation of the delayed pulses, the true and false targets can be identified in the joint transmit–receive (JTR) spatial frequency domain. Furthermore, to eliminate the false targets, a data-dependent beamforming technique is adopted, where an optimization problem is constructed to maximize the output signal-to-interference-plus-noise ratio (SINR) by optimizing the DEPC coefficients and the weight vector of the receive filter in an alternating way. Specifically, two methods, including the Single Jammer Suppression (SJS)-Alternating Direction Method of Multipliers (ADMM) and Multiple Jammers Suppression (MJS)-Gradient Descent (GD) methods are developed for the cases of a single jammer and multiple jammers, respectively. A series of numerical results are utilized to verify the effectiveness of the proposed methods for mainlobe deceptive jammer suppression. • The DEPC-MIMO scheme is proposed by discretizing element pulse coding coefficients. • The true and false targets are distinguished in the transmit–receive spatial domain. • The false targets are suppressed by optimizing DEPC coefficients and weight vector. [ABSTRACT FROM AUTHOR]

Waveform design is a significant topic in multiple-input-multiple-output (MIMO) radar. In this paper, we address the problem of waveform design for colocated narrowband MIMO radar transmit beampattern synthesis. The existing methods have not comprehensively considered the transmit beampattern synthesis under multiple practical constraints, such as constant modulus constraint, Doppler tolerance, high range resolution and low auto-correlation sidelobe. We consider these practical constraints and propose a novel chirp rate diversity-based linear frequency modulated (CRD-LFM) waveform design for approximating the desired MIMO radar transmit beampattern. To tackle the problem, we improve the classical two-step approach. Firstly we design the covariance matrix using the existing closed-form solution. Then we model the problem of obtaining the waveforms from the covariance matrix as a nonlinear optimization problem, and we utilize the Sequential Quadratic Programming (SQP) algorithm to solve it. The simulations demonstrate that the waveforms obtained by this method can well approximate the desired transmit beampattern and have higher range resolution. Furthermore, we verify that the suppression of auto-correlation sidelobe can be achieved using amplitude weighting, which is easily realizable in practical engineering. • A chirp rate diversity-based waveform design method for MIMO radar is proposed. • It is verified that varying the chirp rate of waveforms can adjust the beampattern. • An improved two-step method for MIMO radar waveform optimization is proposed. [ABSTRACT FROM AUTHOR]

• The problem of Vandermonde constrained CP tensor decomposition for angle estimation in MIMO radar under spatially colored noise is addressed. • The smoothed estimate of the temporal cross-correlation matrix is presented to provide improved robustness to colored noise in the case of the limited number of pulses. • Constrained alternating least squares (ALS) based on new rectification strategy for Vandermonde factors is used to solve CP decomposition model. • With the help of temporally smoothed cross-correlation and constrained ALS, the proposed method exhibits superior angle estimation performance. We address the problem of Vandermonde constrained CANDECOMP/PARAFAC (CP) tensor decomposition in application to angle estimation for bistatic multiple-input multiple output (MIMO) radar under spatially colored noise. By exploiting the temporally uncorrelated characteristic of colored noise, a new denoising scheme based on the temporally smoothed cross-correlation approach is presented. Then, after rearranging the smoothed cross-correlation matrix into a fourth-order tensor, a Vandermonde constrained CP tensor decomposition model is formulated, which fully exploits the multidimensional structure of the array measurement and the Vandermonde structure of the factor matrices. To solve this model, an efficient constrained alternating least squares (ALS) algorithm is developed to decompose the Vandermonde factor matrices. Finally, joint estimates of direction of departure (DOD) and direction of arrival (DOA) are obtained by the generators of the Vandermonde factor matrices. Compared with the state-of-the-art approaches, the proposed method can achieve better angle estimation performance by jointly using the temporally smoothed cross-correlation denoising operation and enforcing the Vandermonde structure information in the tensor decomposition. Numerical simulation results verify the effectiveness and improvement of our algorithm. [ABSTRACT FROM AUTHOR]

The recently developed frequency diverse array (FDA) radar applies a small frequency increment across the array elements, which provides advantages in clutter suppression, jamming suppression, and target detection for existing radar systems. In this paper, the frequency increment is enlarged to guarantee that the transmit waveforms are orthogonal in frequency domain for multiple-input multiple-output (MIMO) radar. A multi-scale moving target detection method of FDA-MIMO radar is proposed. The FDA radar with a large frequency increment suffers from the deteriorated decorrelation of scatterers within the same range bin and causes the inner-bin range dependence (IRD) property in transmit spatial domain. By taking additional space–time snapshots in transmit domain as training samples, the space–time adaptive processing (STAP) is applied in joint receive and Doppler domain for clutter suppression, which alleviates the requirement of insufficient training samples in non-stationary clutter environment. In the sequel, a set of beamformers within the whole angular domain of interest is utilized for target detection, which achieves target detection under the condition of random target locations and sizes. Numerical simulation results are provided to verify the effectiveness of the proposed method. • FDA radar of large Δ f can realize waveform orthogonality and obtain range DOFs. • FDA of large Δ f improves the range resolution to distinguish inner-bin scatterers. • The proposed STAP method alleviates the pressure on insufficient training samples. • A set of beamformers are designed to detect target with random locations and sizes. [ABSTRACT FROM AUTHOR]

The issue of adaptive detection is considered for the colocated multi-input multi-output (MIMO) radar in this paper. Meanwhile, the background is partially homogeneous environments (PHE), where the power mismatch is present between the training data and test data. The training data in PHE are utilized to derive effective detectors on the basis of generalized likelihood ratio test, Rao, Wald, Gradient, and Durbin tests. Since the Durbin test-based detector coincides with the Rao test-based detector, the two-step design approach for the Durbin test is used for deriving the new detector. The outcomes of simulation experiments illustrate that the proposed detectors achieve superior effectiveness to existing approaches. Furthermore, the results also show that when the signal mismatch exists, the Wald and Durbin tests maintain robust characteristics. Meanwhile, the Rao test ensures selective property. [ABSTRACT FROM AUTHOR]

In this paper, we propose a pulse train coding and decoding matrix design based electronic counter-countermeasure (ECCM) scheme for multiple-input multiple-output (MIMO) radar against interrupted sampling repeater jamming (ISRJ). The ECCM scheme includes two steps: jamming pulse compression (PC) sidelobes suppression and jamming peaks identification. By joint design of coding and decoding matrix, the proposed ECCM scheme can simultaneously suppress PC sidelobes of target echoes and jamming signals. Compared with the existing methods, the proposed scheme can achieve the improvement in jamming suppression and target detection without signal-to-noise ratio loss, and the coding and decoding matrix design can be realized offline. Besides, the phase-only coding matrix can be directly applied to the MIMO radar with multi-beam transmit beampattern. After sidelobes suppression, an identification matrix is proposed to identify the jamming peaks. In specifical situation, the identification process does not require ISRJ parameter, and the ISRJ parameter estimation can be converted into a convex optimization problem in other situations. At last, simulated results corroborate the effectiveness of the anti-ISRJ performance with the proposed ECCM scheme. • An overall ECCM scheme is proposed, including ISRJ sidelobes suppression, ISRJ parameter estimation and ISRJ peaks identification. • The proposed ECCM scheme can be directly applied to the MIMO radar with multi-beam. • In a particular case, the proposed ECCM scheme can perform without ISRJ parameters. • The proposed ECCM scheme does not suffer SNR loss. [ABSTRACT FROM AUTHOR]

Frequency diverse array (FDA) multiple-input multiple-out (MIMO) radar has an inherent higher-dimensional data structure that can be exploited in temporal, spatial, and range-dependent dimensions for space–time-range adaptive processing (STRAP). This paper addresses the problem of tensor filters in application to space–time-range clutter suppression for FDA-MIMO radar. The characteristics of space–time-range clutter are discussed to establish the multidimensional signal models. FDA-MIMO radar can divide the secondary range cell within the range resolution cell of MIMO radar when they have the same matching filter bandwidth in the receive channel. Specifically, the target-free covariance information can be estimated from more secondary training cells for FDA-MIMO radar. According to the discussed properties of space–time-range clutter, namely secondary clutter rings, clutter degrees of freedom (DOFs), and the range-dimensional non-stationary characteristic, a tensor-based STRAP approach with range compensation is proposed to suppress clutter for FDA-MIMO radar. Numerical results verify the mentioned clutter characteristics and illustrate the importance of the samples number and range compensation operation for the proposed tensor filter. Finally, the STRAP performance benefits of FDA-MIMO radar over MIMO radar are discussed by simulations. • For the same bandwidth, FDA-MIMO radar can divide more secondary range cells within one range cell than MIMO radar. • The space–time-range clutter of FDA-MIMO radar has the range-dependent non-stationary property. • The data cube obtained in array dimension, pulse dimension and range dimension can be directly processed by tensor methods. • The tensor-based filter utilizing range compensation can effectively suppress clutter for FDA-MIMO radar. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the point-like target fusion detection in a partially homogeneous environment with distributed MIMO radar. Specifically, we consider the imperfect waveform separation problem, which means that the matched filter output contains not only the auto-correlation term of the current matched waveform but also the cross-correlation terms, called waveform residuals, with the remaining transmitted waveforms. To this end, a hybrid-order Gaussian (HOG) model is utilized, where the target amplitude is deterministic but unknown, and the waveform residuals obey the Gaussian distribution. Then two adaptive detectors are developed according to the GLRT and Wald test, named HOG-GLRT and HOG-Wald respectively. At the fusion detection stage, we focus on the problem of signal-to-noise ratio (SNR) diversity, i.e., the detection performance degradation due to the average weighting of spatial diversity channels when the echo SNR differs. Combined with the Model Order Selection criterion and multiple hypothesis test, two modified fusion detectors based on channel selection are proposed, named MHOG-GLRT and MHOG-Wald. Finally, the numerical simulation results show that the HOG-GLRT is sensitive against waveform residuals, while the HOG-Wald exhibits strong robustness. And it also demonstrates the effectiveness of the MHOG-GLRT and MHOG-Wald facing the extreme scene of SNR diversity. • We propose two detectors for distributed MIMO radar under imperfect waveform separation. • We develop two modified detectors to solve the problem of SNR diversity. • These detectors can improve the detection performance compared with the existing ones. [ABSTRACT FROM AUTHOR]

In this work, we propose a time-varying wave-shape extraction algorithm based on a modified version of the adaptive non-harmonic model for non-stationary signals. The model codifies the time-varying wave-shape information in the relative amplitude and phase of the harmonic components of the wave-shape. The algorithm was validated on both real and synthetic signals for the tasks of denoising, decomposition, and adaptive segmentation. For the denoising task, both monocomponent and multicomponent synthetic signals were considered. In both cases, the proposed algorithm can accurately recover the time-varying wave-shape of non-stationary signals, even in the presence of high levels of noise, outperforming existing wave-shape estimation algorithms and denoising methods based on short-time Fourier transform thresholding. The denoising of an electroencephalograph signal was also performed, giving similar results. For decomposition, our proposal was able to recover the composing waveforms more accurately by considering the time variations from the harmonic amplitude functions when compared to existing methods. Finally, the algorithm was used for the adaptive segmentation of synthetic signals and an electrocardiograph of a patient undergoing ventricular fibrillation. • A fully adaptive method for time-varying wave-shape extraction is proposed. • The proposed tool can accurately follow the time-varying pattern of the oscillations. • Results on synthetic signals show high performance in common signal processing tasks. • Experiments on real signals show that our proposal outperforms current techniques. [ABSTRACT FROM AUTHOR]