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Meter-wave MIMO radar has multipath effects in the low elevation angle area, seriously affecting the target elevation estimation performance. Therefore, the mainstream height measurement methods are generalized MUSIC and maximum likelihood estimation algorithms that do not require decoherence processing. Still, their complexity is high, and their performance is poor at low signal-to-noise ratios. Compressed sensing and time-reversal have been applied in height measurement, with good accuracy but high complexity. To quickly and accurately obtain the height parameters of the target, this paper proposes a low-elevation height measurement method for meter-wave MIMO radar based on adaptive beamforming. First, we analyze and simplify the signal model; then use the channel matching matrix to eliminate the influence of the reflected wave on the direct wave; then use the target direct wave direction and the reflected wave direction as the interference direction, and perform adaptive beamforming on it to obtain the target elevation angle value. Furthermore, the dimension reduction matrix is used to reduce the dimensionality of the received data signal. Then an algorithm suitable for undulating terrain and inclined ground is proposed and simulated. In addition, this paper gives the complexity of the proposed algorithm and the comparison algorithm. At last, in terms of the signal-to-noise ratio, the number of snapshots, the number of array elements, and the elevation angle, many simulation experiments are carried out to compare the proposed height measurement algorithm with the mainstream height measurement algorithm. The simulation results show that the accuracy and robustness of the proposed height measurement algorithm are the best under these factors. [ABSTRACT FROM AUTHOR]

• The proposed approach satisfies the need for precise focusing of spatial energy and, at the same time, meets spectral compatibility requirements. • The approach with a strict convergence and low computational complexity is proposed to solve the joint optimization problem. • A new update method for similarity constraint is proposed to utilize the freedom degree of signal space better. This paper aims to design multiple-input multiple-output (MIMO) radar transmit waveform that is able to focus energy on the target space-frequency domains while avoiding specific space-frequency domains for communicational devices. Specifically, the waveform design is recast as a joint optimization problem wherein the interference-to-signal energy ratio cost function turns out to be non-convex and non-smooth. In order to solve such an optimization problem, in this paper, an iterative optimization algorithm is proposed, wherein each iteration the majorization minimization (MM) scaling is firstly performed to the logarithmic cost function, and then the best alternative performance waveform vector is analytically achieved. As a result, the proposed algorithm can form a deep radiation energy null in the space-frequency domain where the communicational devices are expected to be located, and at the same time develop a higher radiation energy gain in specific target areas. The computational complexity of the proposed approach is quadratic to the number of array elements and snapshots, which shows potential for practical applications. Finally, the performance of the proposed algorithm has been verified via simulation experiments. [ABSTRACT FROM AUTHOR]

This paper discusses beamspace target height estimation for bistatic multiple-input multiple-output (MIMO) radars. Beamspace super-resolution algorithms for target height estimation have attracted significant attention due to low data transmission, storage, and computation. However, current literature has not yet considered beamspace target height estimation for a bistatic MIMO radar. Although target height estimation algorithms can be directly applied to a bistatic MIMO radar after minor modifications, such a direct application does not afford optimum performance. Therefore, we develop a three-dimensional beamspace maximum likelihood data fusion (3D-BMLF) algorithm appropriate for the target height estimation algorithm of a bistatic MIMO radar. The 3D-BMLF algorithm first converts target signals from the element space to the 3D beamspace, separates the transmitter and the receiver signals, and obtains two closed-form solutions of target height using the beamspace ML algorithm. Finally, the proposed method fuses the two closed-form solutions by minimizing the mean square error (MSE) of estimation. As a result, the 3D-BMLF algorithm has a very low computational burden and good estimation accuracy. Our computational complexity analysis and simulation results demonstrate the suitability of the 3D-BMLF algorithm for engineering applications. [ABSTRACT FROM AUTHOR]

Frequency diverse array multiple-input multiple-output (FDA-MIMO) produces an angle-range-dependent and time-varying transmit beampattern due to the small frequency increment across its array elements, which provides potential applications in new radar techniques. Due to this particularity, multiple signal classification (MUSIC) algorithm can be used to simultaneously estimate the angle and range of the target through a two-dimensional search. The MUSIC need to estimate the covariance matrix. In this paper, we introduce a shrinkage processing for estimating large covariance matrices when the number of samples is substantially fewer than the number of variables which is more common in FDA-MIMO than phase array (PA). Firstly, we propose a receiving model of multi-channel mixing multi-matched filter to eliminate time-varying effect. Secondly, the taper processing, Toeplitz rectification processing and shrinkage-to-identity processing are analyzed. Then, we propose two algorithms, namely, shrinkage-to-taper and shrinkage-to-Toeplitz-rectification on the basis of these algorithms. The proposed processing method improves upon both shrinkage and Toeplitz rectification or tapering processing by shrinking the sample covariance matrix (SCM) to its Toeplitz rectification or tapered version. Thirdly, a closed form expression of shrinkage coefficient for the shrinkage-to-Toeplitz-rectification, and shrinkage-to-taper are used. Finally, simulations results for FDA-MIMO radar show that the proposed algorithm is superior to the sample covariance matrix, shrinkage LW algorithm proposed by Ledoit-Wolf and shrinkage RBLW algorithm which is improved shrinkage LW according to the Rao-Blackwell theorem, in the sense of minimum mean-squared error (MMSE). [ABSTRACT FROM AUTHOR]

In this paper, we investigate low probability interception (LPI) in passive phased array radars. Power control and transmit beamforming (BF) are used to minimize the received power by the interceptor. Meanwhile, desired target detection performances and the limited power budget of radar are guaranteed. The LPI problem is solved analytically and some theorems are derived for the design of optimum BF weights. Two LPI optimization problems from different perspectives are formulated and solved analytically in preparing closed-form solutions for designing transmit BF weights. LPI performance and computation time of the proposed algorithms are compared with that of the benchmark algorithms. Simulation results show that proposed algorithms not only minimize the interception probability of radar in comparison with benchmark algorithms but also satisfy desired detection performance and power budget constraints simultaneously. The proposed algorithms outperform the standard numerical Barrier algorithm in complexity and optimality. [ABSTRACT FROM AUTHOR]

This paper considers the waveform design for colocated MIMO radar to improve target detectability embedded in signal-dependent interferences, and maximize the SINR (signal-to-interference-plus-noise ratio). Due to constant modulus constraint, the problem is indeed non-convex and NP-hard. To address the challenge of this intractable problem, we propose a novel optimization framework for solving the problem based on convex relaxation. In the proposed algorithm, the original optimization problem is reformulated heuristically by introducing an auxiliary variable. Then it can be relaxed to a convex optimization problem and efficiently solved by alternative optimizing. Numerical results show that the proposed algorithm improves the beampattern performance significantly by generating much deeper notches at the interference directions, then obtains higher SINR over the state-of-the-art method. [ABSTRACT FROM AUTHOR]

As an indispensable part of array signal processing, direction-of-arrival (DOA) estimation has been well investigated over the past few decades, and many excellent DOA estimation methods have been proposed. In this paper, a receiving domain covariance vector (RxCV) based unitary SBL algorithm is proposed for the off-grid DOA estimation of monostatic multiple-input multiple-output (MIMO) radar in unknown non-uniform noise environment. In the proposed algorithm, the data received by MIMO radar is firstly transformed into receiving domain by a reshape operation. Then the RxCV-based unitary off-grid sparse model without non-uniform noise is constructed through unitary transformation and first-order linear approximation, where the unknown non-uniform noise is got rid off by a linear transformation. Based on the RxCV-based unitary off-grid sparse model, the sparse Bayesian learning (SBL) criterion is adopted to estimate the parameters, where signal variance and off-gird error are estimated by using expectation-maximization (EM) strategy. The DOA estimation is ultimately realized through 1-dimensional spectrum search of the received data. Results of the simulation experiments have provided the evidence of that the proposed algorithm is robustness against nonuniform noise and off-grid error, and it can maintain superior DOA estimation performance compared with other reported sparse signal representation based methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel direction of arrival (DOA) estimation algorithm is proposed for monostatic multiple-input multiple-output (MIMO) radar. Due to redundancy of the received data of monostatic MIMO radar, the computational complexity of the inversion of the covariance matrix is increased. In order to reduce the large computational complexity of existing subspace-based algorithms, we perform two reduced-dimensional operations. First, the high-dimensional received data is transformed the low-dimensional received data through a special transformation matrix. Second, the low-dimensional received data is transformed into beamspace. The receive beamspace filter is designed using convex optimization, and it can flexibly control the bandwidth and limit the sidelobe level. To ensure that the converted noise is Gaussian white noise, the prewhitening process is performed. Based on the above precondition, the reduced-dimensional beamspace technique can be effectively combined with the unitary ESPRIT model. Finally, the Cramer-Rao bound (CRB) on angle estimation in element space and reduced-dimensional beamspace is calculated for performance analysis. Numerical simulations verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The frequency diverse array multiple-input multiple-output (FDA-MIMO) radar can detect target range by exploiting a small frequency offset across the transmit sensors, which can be utilized to jointly estimate angle and range. Nevertheless, the detection performance is basically restricted by the array aperture and signal bandwidth. In this paper, we propose a joint optimization design scheme for FDA-MIMO radar, i.e., the virtual coprime planar array with 'unfolded' coprime frequency offsets (VCPA-UCFO) framework, for 3D localization without ambiguity. The VCPA-UCFO framework can significantly save sensors and physical space while remarkably extend array aperture and signal bandwidth, which brings about remarkably enhanced economic benefits and estimation performance. Additionally, we construct the 3D localization problem as 3D-MUSIC spatial spectrum function and transform the 3D total spectrum search of the conventional 3D-MUSIC algorithm into 1D local spectrum search by cooperating ESPRIT algorithm and twice reduce dimension MUSIC (TRD-MUSIC) algorithm. The TRD-MUSIC algorithm can significantly relieve computational burden but with no performance degradation. The CRBs are given as performance benchmark. The analysis and simulations have verified the effectiveness and advantages of VCPA-UCFO framework and TRD-MUSIC algorithm in system cost, localization accuracy, resolution and computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of two-dimensional (2-D) direction of arrival (DOA) estimation for L-shaped monostatic MIMO radar with two-level nested linear array (NLA). To obtain more degrees of freedom (DOFs) than traditional nested L-shaped (NLs) MIMO radar, we propose the extensional NLs (ENLs) MIMO radar by extending all of the sensor positions in transmitter array with an identical magnification compared with NLs MIMO radar. The ENLs MIMO radar can offer O (M 1 M 2 N 1 N 2) DOFs with only O (M 1 + M 2 + N 1 + N 2) sensors, where M 1 (N 1) and M 2 (N 2) respectively represents the numbers of the first-level and second-level sensors of NLA in transmitter (receiver) array. Furthermore, to avoid the eigenvalue decomposition, spatial spectral search and polynomial root finding technique in spatial smoothing multiple signals classification (SS-MUSIC) and SS-ROOT-MUSIC, and to improve the estimation accuracy simultaneously, we propose a search-free iterative Taylor expansion (SF-ITE) algorithm to perform DOA estimation, which employs the Discrete Fourier transform (DFT) method to get the coarse estimation, and then utilizes the iterative Taylor expansion technique to obtain fine estimation. SF-ITE can get more precise estimation by performing iteration only once than both SS-MUSIC and SS-ROOT-MUSIC. In addition, we derive an angle-pairing procedure to help to eventually resolve more targets than the total number of sensors. Finally, simulation results are provided to validate the superiority of the ENLs MIMO radar and SF-ITE algorithm. [ABSTRACT FROM AUTHOR]

Compared with the traditional subspace methods, the application of compressed sensing (CS) to the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar can achieve robustness to coherent targets and high localization accuracy with a limited number of snapshots. However, the performance of grid-based CS reconstruction methods degrades since there is an unavoidable basis mismatch between the actual DOD/DOA and the assumed basis. As a gridless CS method, the atomic norm minimization (ANM) has attracted much attention. Specifically, the use of multiple snapshots in ANM has improved the parameter estimation accuracy in contrast with a single snapshot. In this paper, we address the problem of gridless DOD and DOA estimation in bistatic MIMO radar, and develop a multiple-snapshot 2D-ANM algorithm and its two low complexity versions. We start with a rigorous derivation on how to convert the multiple-snapshot 2D-ANM into a semi-definite programming problem, and then explore its dual problem. To overcome the heavy computational burden of the 2D-ANM when the numbers of snapshots and array elements increase, we further propose a 2D-ANM algorithm with snapshot reduction (2D-ANM-SR), as well as an improved 2D-ANM-SR algorithm based on alternating direction method of multipliers (2D-ANM-SR-ADMM). Numerical examples show that with similar accuracy in comparison to 2D-ANM, much lower computational complexity can be achieved via 2D-ANM-SR and 2D-ANM-SR-ADMM. [ABSTRACT FROM AUTHOR]

Direction-of-arrival (DOA) estimation is a fundamental problem in many applications of array signal processing. In practice, it is more important to track the DOAs of moving target signals. In this paper, we study the problem of tracking the DOAs of multiple signals in the frequency diversity array (FDA)-MIMO radar system. Aiming at FDA signals, we propose an efficient algorithm for DOA tracking, which combines subspace updating with the DOA estimation of the FDA signal. The proposed algorithm effectively solves the problem of angle-range coupling in the FDA signal. Moreover, it achieves excellent tracking performance and requires low computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]