Conventional Multiple-input Multiple-output Radar Research Articles

Target polarization scattering matrix estimation with conformal MIMO radar

Conformal multiple-input multiple-output (MIMO) radar is a wide generation of conventional MIMO radar and it uses multiple antennas of different polarization transmit beampatterns for a larger aperture and better performance of target scattering measurement. In this paper, we develop a signal processing algorithm for conformal MIMO radar. According to Least Square (LS) criterion and Maximum Likelihood (ML) criterion, the target Sinclair matrix is estimated. Both unbiased but the ML estimator can achieve the Cramer–Rao bound. The radar polarization measurement capability is evaluated by the rank of Fisher information matrix (FIM). Numerical results with a three-dimensional transmit beampattern of a conformal antennas array show the estimation performance in theory measured by the root mean square error (RMSE).

A dual-resolution unitary ESPRIT method for DOA estimation based on sparse co-prime MIMO radar

In this article, a novel structure of sparse co-prime multiple input multiple output (MIMO) radar is proposed. The proposed sparse co-prime MIMO radar employs one co-prime planar subarray (CPSA) as transmit array and multiple separated co-prime planar subarrays as receive arrays. The receive subarrays are deployed separately yielding long aperture baselines to improve the direction of arrival (DOA) estimation accuracies. A dual-resolution 2-D unitary ESPRIT method is derived for DOA estimation based on such sparse co-prime MIMO radar. First, a noncoherent unitary ESPRIT method is employed to yield coarse DOA estimates using the output signals of sparse co-prime MIMO radar noncoherently. Second, a coherent 2-D unitary ESPRIT method is derived to obtain fine DOA estimates and resolve DOA ambiguity utilizing the coarse DOA estimates. The DOA estimation in presence of unknown inter-subarray phase offsets is also addressed. Then an optimization method is developed to select the appropriate shift distances of the receive CPSAs. Finally, the Cramer-Rao Bound (CRB) of DOA estimation is given to evaluate the accuracy of the proposed estimator. Simulation results confirm that the 2-D DOA estimation accuracies of the proposed approach outperform unitary ESPRIT methods employing conventional co-prime MIMO radar and MIMO radar with rectangular arrays.

On Parameter Identifiability of Diversity-Smoothing-Based MIMO Radar

Diversity smoothing has been widely used for angle estimation with multiple input multiple output (MIMO) radar in the presence of coherent or correlated targets, and the parameter identifiability is an interesting issue. Previous works have shown sufficient conditions for some special cases, such as the coherent targets with conventional MIMO radar, and the array size are derived as a function of the target number and target structure. In this article, we further introduce the interelement spacings to build a complete parameter identifiability scheme. For monostatic MIMO radars, the antenna numbers and interelement spacings of transmit and receive arrays are derived as functions of the target number and the target structure. The optimization models are built to calculate the maximum number of detectable targets for a given target structure. Additionally, the conditions for the bistatic MIMO radar are derived from two-dimension viewpoint. It is shown that the new results improve upon previous spatial smoothing or diversity smoothing methods and recover them in special cases. Simulation results are presented that corroborate our theoretical findings.

A reduced-dimension RML method for transmit beamspace-based MIMO radar

In this paper, the problem of low angle estimation for multiple-input multiple-output (MIMO) radar is considered. Though the refined maximum likelihood (RML) algorithm can be directly applied to low angle estimation in MIMO radar, its computational burden is huge due to the large virtual aperture. Moreover, the conventional MIMO radar employs full waveform diversity, which actually sacrifices the emission gain. To reduce the computational burden and improve the transmit gain, a reduced-dimension RML method for transmit beamspace-based (TB-based) MIMO radar is developed. Firstly, the transmit beamspace (TB) matrix is designed to focus the transmit energy within the desired spatial sector. Then, to reduce the computational complexity and retain the estimation precision of the direct RML (D-RML) method for TB-based MIMO radar, a transmit-receive separate RML (TRS-RML) method is developed by applying the RML algorithm to the transmit and receive side respectively. Moreover, we propose to fuse the estimation results of the transmit and receive side together to improve the estimation precision. Theoretical analysis shows the TRS-RML method requires much lower computational burden than D-RML method. Numerical experiments show the proposed method has higher estimation accuracy compared with the D-RML method for the conventional MIMO radar in most angle region.

MIMO Radar With Array Manifold Extenders

This article is concerned with the problem of multitarget parameter estimation in arrayed multiple-input multiple-output (MIMO) radar. In particular, the radar operates in the presence of moving targets, where the parameters of interests to be estimated for each target are the relative delay, Doppler frequency, direction-of-arrival (DOA) and complex path coefficients (or target's radar cross section). Using the novel concept of “array manifold extender,” the dimensionality of the observation space increases from $N$ to $N\mathcal {N}_{\mathrm{ext}}$ , making the radar more powerful than conventional MIMO radar systems by handling more complex targets and estimating their parameters with increased accuracy. Two “manifold extenders” are proposed in this article in conjunction with a novel spatiotemporal subspace-type framework for estimating the target parameters. The performance of proposed framework is examined using computer simulation studies.

A MIMO Radar-Based DOA Estimation Structure Using Compressive Measurements.

In this paper, we propose a novel direction-of-arrival (DOA) estimation structure based on multiple-input multiple-output (MIMO) radar with colocated antennas, referred to as compressive measurement-based MIMO (CM-MIMO) radar, where the compressive sensing (CS) is employed to reduce the number of channels. Therefore, the system complexity and the computational burden are effectively reduced. It is noted that CS is used after the matched filters and that a measurement matrix with less rows than columns is multiplied with the received signals. As a result, the configurations of the transmit and receive antenna arrays are not affected by the CS and can be determined according to the practical requirements. To study the estimation performance, the Cramér–Rao bound (CRB) with respect to the DOAs of the proposed CM-MIMO radar is analyzed in this paper. The derived CRB expression is also suitable for the conventional MIMO radar by setting the measurement matrix as an identity matrix. Moreover, the CRB expression can work in the under-determined case, since the sum-difference coarray structure is considered. However, the random measurement matrix leads to high information loss, thus compromising the estimation performance. To overcome this problem, we consider that the a prior probability distribution of the DOAs associated with the targets can be obtained in many scenarios and an optimization approach for the measurement matrix is proposed in this paper, where the maximum mutual information criterion is adopted. The superiority of the proposed structure is validated by numerical simulations.

Two-stage ESPRIT for unambiguous angle and range estimation in FDA-MIMO radar

Frequency diverse array (FDA) offers potential applications for joint range and angle estimation, but ambiguous estimates may be generated due to its range-angle coupling and time-variant beampattern. This problem can be addressed by jointly utilizing FDA and multiple-input multiple-output (MIMO) radar, but only multiple signal classification (MUSIC) algorithm was considered in the FDA literature. In order to avoid high computational complexity in the MUSIC algorithm due to the required 2-D peak searching, in this paper, we propose a two-stage estimating signal parameters via rotation invariance technique (ESPRIT) algorithm for FDA-MIMO radar to estimate both range and angle of targets, along with the proposed pairing method for unambiguous estimates. Moreover, closed-form expressions of the mean squared error (MSE) and Cramér-Rao lower bound (CRLB) for angle and range estimations are also derived. All proposed methods and derivations are verified by both theoretical analysis and numerical results, which show the superiority of FDA-MIMO radar over conventional phased-array radar and MIMO radar in target localization.

Multi-dimensional Ambiguity Function for Coherent Pulsed-LFM FDA Radar

Frequency diverse array (FDA) which employs a small frequency increment between adjacent elements can provide a range-angle-dependent beampattern with new application potentials. In this paper, the proposed coherent pulsed-LFM FDA scheme enables full spatial coverage with a single transmit waveform, wherein the transmit beamforming can be performed at the receiver with flexible signal processing means. In the coherent pulsed-LFM FDA radar configuration, the range sidelobe level (SLL) can be reduced significantly. Analyses of the multi-dimensional ambiguity function in terms of the low sidelobe characteristics, spatial coverage capability, and resolution properties in range, angle and Doppler domains are given by ambiguity function profiles. Comparisons with conventional phased array and MIMO (Multiple-Input Multiple-Output) radar are presented in the simulation. Results demonstrate the superiority of the proposed approach in angular coverage and sidelobe level with simplicity in engineering.

Generalized Co-Prime MIMO Radar for DOA Estimation With Enhanced Degrees of Freedom

In this paper, we propose a generalized co-prime multiple-input multiple-output (MIMO) configuration for direction of arrival estimation. Compared with the conventional co-prime MIMO radar that requires prototype co-prime arrays for transmitter and receiver, the proposed configuration enlarges the inter-element spacing of transmitter with an integer expansion factor. By vectorizing the sample covariance matrix, the generalized sum and difference coarray (GSDC) concept is defined for exploiting the advantages of proposed configuration. The analytical expressions for the expansion factor, the maximum consecutive lags, and the number of unique lags are derived carefully. It is verified that GSDC can obtain more degrees of freedom (DOFs) with the increase of expansion factor. Specifically, with $\mathcal {O}(M+N)$ sensors, GSDC can provide $\mathcal {O}(M^{2}N^{2})$ DOFs, whereas the conventional one only has $\mathcal {O}(MN)$ DOFs. Simulation results demonstrate the usefulness of proposed configuration utilizing both spatial smoothing and sparse representation algorithms.

FDA-MIMO Radar Range–Angle Estimation: CRLB, MSE, and Resolution Analysis

Multiple-input multiple-output (MIMO) radar enjoys the advantage of increased degrees-of-freedom and spatial diversity gain, but it cannot effectively resolves the targets closely spaced in the same angle cell (but different range cells). Frequency diverse array (FDA)-MIMO radar can handle this problem by exploiting its range-dependent beampattern. FDA-MIMO radar was, thus, suggested for range–angle estimation of targets. Nevertheless, it is necessary to provide theoretical performance analysis for such a relatively new radar technique. Since multiple signal classification (MUSIC) algorithm is widely adopted in most of the FDA-MIMO literature, this paper derives the Cramer–Rao lower bound and mean square error expressions in MUSIC-based range–angle estimation algorithms for a general FDA-MIMO radar. Furthermore, the corresponding range and angle resolution thresholds in target detection and localization are also derived. Numerical results verify that the FDA-MIMO indeed outperforms conventional MIMO radar in both range–angle estimation and resolution threshold performance.

DFT-Based Closed-Form Covariance Matrix and Direct Waveforms Design for MIMO Radar to Achieve Desired Beampatterns

In multiple-input multiple-output (MIMO) radar, for desired transmit beampatterns, appropriate correlated waveforms are designed. To design such waveforms, conventional MIMO radar methods use two steps. In the first step, the waveforms covariance matrix ${\mathbf R}$ is synthesized to achieve the desired beampattern. Whereas in the second step, to realize the synthesized covariance matrix, actual waveforms are designed. Most of the existing methods use iterative algorithms to solve these constrained optimization problems. The computational complexity of these algorithms is very high, which makes them difficult to use in practice. In this paper, to achieve the desired beampattern, a low complexity discrete-Fourier-transform based closed-form covariance matrix design technique is introduced for an MIMO radar. The designed covariance matrix is then exploited to derive a novel closed-form algorithm to directly design the finite-alphabet constant-envelope waveforms for the desired beampattern. The proposed technique can be used to design waveforms for large antenna array to change the beampattern in real time. It is also shown that the number of transmitted symbols from each antenna depends on the beampattern and is less than the total number of transmit antenna elements.

MIMO radar pseudo-orthogonal waveform generation by a passive 1 ×Mmode-mixing microwave cavity

Multiple Input Multiple Output (MIMO) Radar has many advantages compared with conventional Radars including improved target parameters estimation, improved angular resolution while keeping a small number of antennas. Nevertheless, these advantages can be obtained when probing simultaneously the scattering matrix with orthogonal transmit signals. Recently, a passive orthogonal waveform generation technique was introduced using a 1 × M ports Mode-Mixing microwave cavity whose transfer functions are uncorrelated. This approach makes MIMO Radar system able to satisfy transmit beamforming constraint with a cost-efficient architecture. In this paper, more conceptual clarifications are brought and the orthogonality metrics are assessed. Furthermore, the proposed method is experimentally compared with a conventional MIMO Radar based on frequency hopping waveforms.

Cognitive chaotic UWB-MIMO radar based on nonparametric Bayesian technique

This work presents a cognitive waveform selection mechanism for chaotic ultra-wideband multiple-input multiple-output (MIMO) radars. It utilizes the target discrimination capability of a Dirichlet process mixture model (DPMM)-based clustering approach to discriminate individual extended targets and applies a mutual information (MI)-based mechanism to select the best transmission waveform. This joint DPMM-MI cognitive mechanism aims at enhancing target discrimination and detection, showing a 3-dB performance gain in achieving 0.9 target detection probability over conventional MIMO radar waveforms.

Clutter rank estimation rule for MIMO radar with arbitrary transmitted waveform synthetic strategies

A computational-efficient estimated rule of clutter rank for multiple-input multiple-output (MIMO) radar with arbitrary transmitted waveform synthetic strategies is presented and proved. It is suitable for both conventional phased array radar and MIMO radar with a uniform sparse or non-uniform sparse transmitting subarray, and is also valuable for the design of optimal waveform synthetic strategies and space-time adaptive processing algorithms. The effectiveness of the proposed rule is verified by simulation results.