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In this paper, we propose a computationally efficient algorithm with improved effective aperture for coherent angle estimation in a monostatic multiple-input multiple-output (MIMO) radar. First, the direction matrix of MIMO radar is mapped into a low-dimensional matrix of virtual uniform linear array (ULA). Then, an augmented data expansion matrix with improved effective aperture is obtained by exploiting the Vandermonde-like structure of the low-dimensional direction matrix and radar cross section (RCS) matrix to enlarge the aperture of the array. Next, a unitary transformation is used to transform the augmented matrix into a real value and the approximate signal subspace of the augmented matrix is obtained by the Nyström method, which can reduce the computational complexity. The eigenvectors of the approximate signal subspace are used to reconstruct the matrix for direct decorrelation processing. Finally, direction of arrivals (DOAs) can be estimated faster by utilizing the unitary ESPRIT algorithm since the rotation invariance of the extended reconstruction matrix still exists. The proposed algorithm has a lower total computational complexity, and the estimation accuracy is improved by utilizing real values and enlarging the array aperture for estimation. Several theoretical analyses and simulation results confirm the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

In a road environment, the clutter-induced multipath components cause errors in the estimations of the direction of departure (DOD), direction of arrival (DOA), and Doppler frequencies of nearby target vehicles. In this paper, two ESPRIT-based bistatic MIMO radar algorithms are proposed to solve the pairing problem while reducing the computational complexity in a multiobject environment. The first method, named the object subspace algorithm, solves the pairing problem by extracting and processing each object return on an eigenvector basis, but it is shown to exhibit performance degradation. In the second method, named the Doppler preprocessing algorithm, the object return is first separated in the frequency domain and the ESPRIT algorithm is then applied or the object subspace algorithm is reused, if necessary. Finally, the multiobject, multipath environment is simulated to show the parameter estimation (DOD, DOA, Doppler frequency) performances of the two proposed algorithms. [ABSTRACT FROM AUTHOR]

In multiple-input multiple-output radar, to localize a target and estimate its reflection coefficient, a given cost function is usually optimized over a grid of points. The performance of such algorithms is directly affected by the grid resolution. Increasing the number of grid points enhances the resolution of the estimator but also increases its computational complexity exponentially. In this paper, two reduced complexity algorithms are derived based on Capon and amplitude and phase estimation (APES) to estimate the reflection coefficient, angular location, and Doppler shift of multiple moving targets. By exploiting the structure of the terms, the cost function is brought into a form that allows us to apply the two-dimensional fast Fourier transform (2D-FFT) and reduce the computational complexity of estimation. Using low resolution 2D-FFT, the proposed algorithm identifies suboptimal estimates and feeds them as initial points to the derived Newton gradient algorithm. In contrast to the grid-based search algorithms, the proposed algorithm can optimally estimate on- and off-the-grid targets in very low computational complexity. A new APES cost function with better estimation performance is also discussed. Generalized expressions of the Cramér–Rao lower bound are derived to assess the performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

• This paper aims to jointly design transmit waveform and mismatched filter to achieve low sidelobe level limited discussion in the open literature. We formulate an l_p-norm metric for pulse compression model which suits for either the ISL or PSL minimization only with different initializations of p. The waveform and filter can be updated simultaneously (not in alternating manner). A new algorithm with guaranteed convergence is proposed to solve the introduced problem. A number of new Lemmas are proved to find proper majorizers. Low sidelobe level is required for pulse compression (PC) radar systems since it can reduce false alarm triggered by the high sidelobe and improve the ability to detect weak targets nearby strong scattering points. In most of the existing literature, sidelobe suppression is achieved by either transmit waveform design at the transmitter or mismatched filter optimization at the receiver. Joint design of waveform and filter is an approach with limited discussion. Thus, this paper aims to jointly design transmit waveform and mismatched filter to achieve low sidelobe level. We first formulate an L p -norm metric for PC model which suits for either the integrated sidelobe level or peak sidelobe level (PSL) minimization (only with different initializations of p). Then we present a new iterative method to deal with the problem by using Dinkelbach's scheme and majorization minimization method. The proposed method is shown to be convergent and the computational complexity is also analyzed. Numerical examples demonstrate that waveforms and filters designed by using the proposed method produce lower PSL than the existing techniques. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the joint design of a transmit beampattern and angular waveform (AW) for colocated multiple-input multiple-output (MIMO) radars. The importance of the AW in the proposed signal processing strategy is first clarified, and then, two optimization models are established, which are aimed at either the power spectral density (PSD) design or the spectral compatibility and similarity design of the AW. There are two main differences between the proposed models and existing models. First, instead of matching a desired template or maximizing the transmit power on specific regions, the transmit beampattern in this paper is optimized to approach several key points, which guarantees the high transmit gain and the flexible adjustment of each beam gain. Second, instead of optimizing the performance of the transmit waveform, only the characteristics of the AW are examined, and they can be constrained quantitatively according to their relationship with the transmit gain. The two models can be unified into the same framework, and an efficient algorithm is proposed to solve the problem under a constant modulus constraint. The convergence of the proposed algorithm is demonstrated, and some improvements to reduce the computational complexity are proposed. Numerical simulations showed that compared to the existing methods, the proposed approach can be used to obtain a higher transmit gain, flexibly adjust each beam gain, and more accurately control the PSD, spectral compatibility, and similarity of the AW. Moreover, numerical simulations showed that, compared to the use of existing methods, the proposed algorithm has higher computational efficiency. [ABSTRACT FROM AUTHOR]

Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

• Parameters are estimated by joint space-time-frequency domain optimization scheme. • Improving the system flexibility and reduce system cost. • Expanding array aperture and signal bandwidth by SA-MCFO framework. • Decoupling DOD and range and reduce computational complexity by RDMD algorithm. • Parameter automatic pairing problem is solved. The frequency diverse array-multi-input multi-output (FDA-MIMO) radar can locate target in angle and range dimensions by transmitting little frequency offset across the transmit sensors. In this paper, a joint space-time-frequency domain optimization scheme is designed to estimate the direction of departure (DOD), direction of arrival (DOA) and range of target. We propose synthetic aperture-multi coprime frequency offset (SA-MCFO) framework which synthesizes all subarrays generated by array motion to expand array aperture and signal bandwidth. We also propose reduce dimension multiple signal classification with decoupling (RDMD) algorithm to detect target by combining reduce dimension (RD) transformation and subarray based estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which results in decoupling of DOD and range as well as fine estimation accuracy. The proposed SA-MCFO framwork enjoys remarkable system flexibility but with lower system cost. The proposed RDMD algorithm significantly reduce computational complexity but with ignorable performance degradation compared with the conventional 3D multiple signal classification (3D-MUSIC) algorithm. We also provide the Cramer-Rao bounds (CRBs) of DOD, DOA and range as performance benchmark, and numerical simulations are conducted to verify the superiorities and effectiveness of the proposed SA-MCFO framework and RDMD algorithm. [ABSTRACT FROM AUTHOR]

The frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar is capable of producing the angle-range-dependent beampattern by utilizing a small frequency increment across the transmit elements, which enables to jointly calculate the angle and range estimates of the targets. However, the methods with tremendous computational burden, e.g., two-dimensional (2D) spectrum peak search (SPS), are involved due to the coupling of parameters, i.e., angle and range. In this study, We propose a reduced-dimension root MUSIC (RD-root-MUSIC) algorithm to detect the angle and range of targets in the FDA-MIMO radar. Specifically, we firstly decompose the angle and range by reconstructing the 2D-MUSIC spatial spectrum, where, resultantly, the range component is dismissed and the angle-dependent spatial spectrum function can be achieved. Moreover, to circumvent the SPS, the polynomial root finding is employed to obtain the angle estimates. Furthermore, we derive the closed-form solution to the range estimates which can be directly calculated based on the angle estimates. The proposed algorithm remarkably reduces the computational complexity without estimation performance degradation. In addition, the Cramér-Rao bounds (CRBs) are presented and the simulation results are provided to demonstrated the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Forreducingthecomputationalcomplexityofdirection-findingalgorithminsparsemultipleinput multiple-output (MIMO) radar, a low-complexity partial spatial smoothing (PSS) algorithm is presented to estimate the directions of multiple targets. Firstly, by dealing with a partly continuous sampling covariance vector in PSS technology, an incomplete signal subspace can be obtained. Then, a special matrix can be obtained by using this incomplete signal subspace. Meanwhile the incomplete signal subspace can also be repaired by the special matrix. At last, the multiple signal classification (MUSIC) algorithm is used to obtain direction estimations. In the process of obtaining signal subspace, no eigenvalue decomposition (EVD) needs to be performed. Compared with the traditional spatial smoothing (SS) technology, the proposed algorithm has lower computational complexity and higher estimation accuracy. Many simulation results are provided to support the proposed scheme. [ABSTRACT FROM AUTHOR]

The problem of DOA estimation for sub-array multiple-input multiple-output radar is concerned. Employing compressive sampling concept and the minimum mean-square error (MMSE) technology, the proposed algorithm alternates between updating sample covariance matrix and the MMSE filter bank values until convergence. Simplified array manifolds are considered to decrease the computational complexity. The core idea of the algorithm is to determine the DOA by calculating the spatial distribution of signal power adaptively. Simulation results show that the new algorithm performs well both in a wide SNR range and limited samples, compared with the MUSIC, PIAA-APES, and OGSBI algorithms. The most outstanding advantage of the new algorithm is that it can maintain high estimation accuracy under limited samples without knowing the number of targets. [ABSTRACT FROM AUTHOR]

We propose an efficient algorithm to design waveforms via relative entropy for multiple-input-multiple-output radar in the presence of (signal-dependent) clutter. The proposed algorithm is devised under the framework of minorization-maximization. Unlike the existing algorithm employing a highly nonlinear function to minorize the objective function, the proposed algorithm uses a quadratic function as the minorizer, leading to a much lower computational cost per iteration. In addition, we exploit an accelerated scheme called squared iterative method, to enhance the convergence rate of the proposed algorithm. Moreover, we extend the proposed algorithm to deal with additional constraints (i.e., constant-modulus constraint, similarity constraint, or both). Particularly, the extension allows for a single-stage design of the constrained waveforms and achieves a larger relative entropy than the existing algorithm. Numerical results are provided to show that the proposed algorithm is computationally more efficient and outperforms the existing algorithm when designing the constrained waveforms. [ABSTRACT FROM AUTHOR]

We study the problem of angle estimation for non-circular sources in bistatic multiple-input multiple-output (MIMO) radar system and propose a low-complexity ESPRIT-Root-MUSIC algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation. By taking the non-circularity characteristics of the impinging signals, the received data can be extended, which corresponds to double the number of elements in MIMO radar. Then a novel signal subspace, based on the multistage wiener filter, can be determined directly from the extended data, which does not involve the estimation of covariance matrix and its eigenvalue decomposition (EVD), thus implying that the proposed method is computationally efficient. Finally, the DOD and DOA are estimated by ESPRIT and Root-MUSIC, respectively. Compared with EVD-based algorithms, the proposed method provides the comparable angle estimation performance with lower computational complexity. Some simulation results verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, the problem of joint direction of arrival (DOA) and Doppler frequency estimation using parallel factor (PARAFAC) analysis in the monostatic multiple-input multiple-output radar is investigated and a reduced-dimensional PARAFAC (RD-PARAFAC) algorithm is proposed. In order to overcome the shortcoming of the heavy computational load in conventional PARAFAC algorithm, we firstly utilize a RD transformation by employing the property of uniform linear arrays, which can remove the redundant entries of steering matrix. By means of the reduced-dimensional transformation, the proposed algorithm can reduce the computational complexity, save memory capacity significantly and has very close joint DOA and Doppler frequency estimation performance when compared with conventional PARAFAC algorithm. Meanwhile, it outperforms the estimation of signal parameters via rotational invariance technique and the propagator method. Furthermore, our algorithm needs no spectral peak searching or pair matching. The complexity analysis and the Cramer-Rao Bound of the DOA and frequency estimation is derived. Simulations are provided to verify its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér–Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results. [ABSTRACT FROM AUTHOR]