Monostatic MIMO Research Articles

A Nested-Nested Sparse Array Specially for Monostatic Colocated MIMO Radar with Increased Degree of Freedom.

This paper mainly investigates the problem of direction of arrival (DOA) estimation for a monostatic MIMO radar. Specifically, the proposed array, which is called a nested-nested sparse array (NNSA), is structurally composed of two nested subarrays, a NA with N1+N2 elements and a sparse NA, respectively, with N3+N4 elements. The design process of NNSA is optimized into two steps and presented in detail. Setting NNSA as transmitter/receiver arrays, we derive the closed-form expression of consecutive DOFs and calculate the mutual coupling coefficient. Eventually, extensive simulations are carried out and the results verify the superiority of the proposed array over the previous arrays in terms of consecutive DOFs, array aperture and mutual coupling effect.

Optimal placement of sensors to enhance degrees of freedom in monostatic collocated MIMO radar

Compared to the traditional monostatic MIMO radar which uses uniform linear arrays (ULAs) for transmitting and receiving signals, sparse linear arrays (SLAs) monostatic MIMO radars can achieve greater Degrees Of Freedom (DOF) and a higher resolution. The optimal placement of sensors in both transmit and receive arrays to attain the maximum DOF is, however, a basic problem in the collocated monostatic MIMO radar. Optimum solution of such a problem is restricted to an exhaustive computer search. Some popular arrays such as nested and coprime arrays have been previously proposed as alternative solutions that can achieve a good DOF, but they do not necessarily lead to the maximum one. In this paper at first, we formulate the optimal placement of sensors in the collocated monostatic MIMO radar to improve the sum coarray as well as the difference coarray of the sum coarray. After that, we present a mathematical framework to achieve the optimal solution for both problems. Finally, we demonstrate that the proposed method yields the optimal MR-MIMO array while demanding much less computational complexity as compared with the computer search.

Online Gain-Phase Self-Calibration Method of MIMO Array Based on Statistical Characteristics of Target Angle Distribution

As a hot research topic, the gain-phase error self-calibration in MIMO radar systems has been investigated for many years. In this paper, we proposed a novel array error self-calibration method, termed online errors self-calibration based on feature learning (OES-FL). This method regards the statistical characteristics of the detected targets’ DOA as a prior knowledge and does not require the calibrated antenna subarray or external reference source to correct the array disturbances in real time. First, we analyse the monostatic MIMO signal model suffering gain-phase error. Then, we exploit the statistical characteristics of DOA of many targets for correcting gain-phase error of antenna array. Next, the gain-phase error estimation scheme based on LMS and the DOA deviation estimation method based on LSTM are proposed, respectively. Using real-life radar data collected at the integrated transportation hubs to generate simulation data, the proposed approach is shown to be effective in correcting gain-phase errors and, therefore, provides a promising model for online error self-calibration in monostatic MIMO radars.

Target Localization for Monostatic MIMO Radar Based on a Double Polynomial Rooting Technique

Target Localization for Monostatic MIMO Radar Based on a Double Polynomial Rooting Technique

Efficient real-valued DOA estimation based on the trigonometry multiple angles transformation in monostatic MIMO radar

A novel real-valued polynomial rooting-based (RV-root-MUSIC) algorithm is proposed for efficient DOA estimation in monostatic MIMO radar. RV-root-MUSIC exploits the trigonometry multiple angles formula and variable substitution technique without transformation approximations to convert the original complex steering vector into a real one with respect to the cosine and sine functions. As such, RV-root-MUSIC implements the real-valued computation for both tasks of EVD and the polynomial rooting since the coefficients of polynomial are real. Numerical simulations are applied to demonstrate that with significantly reduced complexity, such trigonometry transformation is able to provide the similar performances as compared to the conventional root-MUSIC and U-root-MUSIC.

Unitary NSF Method for Monostatic MIMO Radar with Non-orthogonal Waveforms

Unitary NSF Method for Monostatic MIMO Radar with Non-orthogonal Waveforms

Quantum-inspired cultural fireworks algorithm for direction finding of monostatic MIMO radar

Quantum-inspired cultural fireworks algorithm for direction finding of monostatic MIMO radar

Quantum-inspired cultural fireworks algorithm for direction finding of monostatic MIMO radar

For the sake of resolving the direction finding difficulties of coherent sources and obtaining high precision for monostatic MIMO radar, a novel intelligent algorithm and a new direction finding method are proposed. At the beginning, quantum-inspired cultural fireworks algorithm (QCFA) is put forward as a novel intelligent algorithm which utilises the quantum explosion formula to improve fireworks algorithm (FA) and obtain the superiority of fast convergence rate and low computational complexity. As a critical procedure of QCFA, the explosion process of fireworks is mimicked as the guided cultural behaviour, which can increase the search performance of FA to obtain the optimal solution. Afterwards a new direction finding equation of infinite-norm maximum likelihood (IML) is deduced for the monostatic MIMO radar. A novel direction finding method for monostatic MIMO radar is put forward on the basis of QCFA and IML algorithm, which can be described as QCFA-IML. QCFA-IML method not only has high direction finding precision, but also is efficient for locating coherent and independent sources. Monte-Carlo simulation results have indicated the superiority and availability of the proposed QCFA-IML method for direction finding of monostatic MIMO radar, and outperform other intelligent algorithms.

Monostatic MIMO radar with nested L-shaped array: Configuration design, DOF and DOA estimation

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(M1M2N1N2) DOFs with only O(M1+M2+N1+N2) sensors, where M1(N1) and M2(N2) 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.

Monostatic MIMO Backscatter Communications

Backscatter communications have two major antenna configurations: the bistatic configuration, in which the reader employs two different sets of antennas to transmit and receive, and the monostatic configuration, in which the reader employs one set of antennas for both transmitting and receiving. In this paper, we provide a comprehensive study on the MIMO techniques for the M × L monostatic channel. Particularly we study on the joint design of the query and the coding matrices. We show that the maximum achievable diversity order of the monostatic channel is the diversity order achieved by the block-lever unitary query and orthogonal space-time block code (BUTQ-OSTBC) design pair, which is ML 2 , exactly the half of the diversity order of the conventional MIMO channel. Then we show that uniform query, the simplest query approach, cannot achieve the maximum achievable diversity order in the monostatic channel. We generalize BUTQ-OSTBC to the general augmenting approach, and show that unitary matrix is optimal in terms of SER performance among all possible query matrices, when OSTBC is employed. The above results further indicate that in the backscatter channel, additional diversity can be obtained by varying the query signals over time slots within the channel coherent time, which is quite different from the results from the conventional MIMO channels. We verify our results by Monte Carlo simulations.

DOA estimation of coherent and incoherent targets based on monostatic co-prime MIMO array

For the traditional DOA estimation of coherent and incoherent targets based on monostatic (massive) uniform dense array, the number of resolvable targets is limited to the number of physical sensors. To exceed the limitation, it is desirable to have sparse transmitting and receiving arrays. In this study, we consider the monostatic co-prime MIMO array with N sparse transmitters and 2M-1 sparse receivers in the DOA estimation of mixed coherent and incoherent targets. The sparse co-prime MIMO array with O(M+N) physical sensors generates a non-redundant and uniform sub-coarray with O(MN) contiguous sensors in the sum co-array. Based on the defined wide-sense or narrow-sense sum co-array equivalence, we can obtain different configurations of virtual MIMO arrays with O(MN) contiguous virtual sensors, and then construct the corresponding virtual data matrices, which provides different tradeoffs between the number of resolvable targets and the maximum number of mutually coherent targets that can be resolved. On the basis of the virtual data matrix and the conventional DOA estimation approaches such as MUSIC, O(MN) mixed coherent and incoherent targets can be resolved only with O(M+N) physical sensors, namely the number of resolvable targets exceeds the limitation of the number of physical sensors. Furthermore, the application of two additional operation frequencies extends the contiguous sub-coarray accompanied with the improvement of degree-of-freedom for more resolvable coherent and incoherent targets. Finally, simulation results demonstrate the effectiveness of the proposed DOA estimation method.

Realizing joint radar-communications in coherent MIMO radars

The increasing interest on spectrum resources causes various efforts on developing smart and compact solutions as joint radar-communication (JRC) systems. A JRC system can offer cost-effective solution with concurrent operation, as target sensing via radar processing and establishing communication links. JRC capability has been proposed over the years for different types of MIMO radars. However, a JRC capable monostatic coherent MIMO radar system is yet to be developed. These radars offer several advantages as fully coherent signal processing and coherent transmit beamforming which provides beampatterns to minimize probability of intercept. In this paper, two new waveform generation techniques suitable for JRC operation without disturbing transmit beamforming requirements and waveform orthogonality condition in space and time domain are proposed for monostatic coherent MIMO radars. Then, new communication methods are introduced for phase coded monostatic coherent MIMO radars. First method uses chirp-wise information encoding inside the radar pulse as intra-pulse communications. Second rotates the phase of a specific waveform on radiated symbols to a specific direction and the last method applies a small amount of progressive phase shift to the radar waveforms emitted from the antennas to create relative phase modulation between selected radar waveforms. Then, the performance of the proposed communication techniques are investigated in terms of bit error rate (BER) and generated waveforms are examined according to the orthogonality and transmit beamforming requirements.

Fast SL0 algorithm for 3D imaging using bistatic MIMO radar

Multiple-input-multiple-output (MIMO) radar is attractive in moving targets imaging, which is able to solve the difficulty of complex motion compensation. In this article, a three-dimensional imaging method using bistatic MIMO radar is proposed. Compared with monostatic MIMO radar, the bistatic system can provide the complementary information, and the imaging process is not the same as the monostatic case. Furthermore, considering the image sparsity and the spatial limitation of radar targets, a fast smoothed L0 norm algorithm is proposed to achieve the high resolution in cross-range directions with limited antennas. The experimental results demonstrate the validity and efficiency of the proposed method.

Fast Block LMS and RLS-Based Parameter Estimation and Two-Dimensional Imaging in Monostatic MIMO RADAR Systems With Multiple Mobile Targets

This paper presents novel adaptive schemes and the pertinent analysis for estimation of the number of targets, their associated radar cross section (RCS) values and Doppler velocities in monostatic MIMO radar systems. These schemes are based on the fast block least mean squares (FBLMS) and fast block recursive least squares (FBRLS) algorithms considering both stationary as well as mobile targets and/or radar platform. For the stationary scenario, schemes are proposed for estimation of RCS coefficients, target number and their angle, range locations. In addition, a procedure is also developed to obtain the 2D-image of the MIMO radar scene in angle-range dimensions. Analysis is presented for the first and second order moments of the observation as well as estimation errors resulting from the proposed FBLMS, FBRLS techniques, along with their global convergence. Both the proposed schemes are shown to converge globally to the optimal Wiener filter, which is lacking in existing schemes. Further, a Bayesian information criterion based selection rule is employed to enhance the estimation and imaging performance of the proposed adaptive frameworks. Cramer-Rao bounds are derived to characterize the mean squared error of the estimated RCS coefficients for stationary scenarios and also for joint RCS, Doppler velocity estimation in mobile MIMO radar scenarios. The FBLMS and FBRLS schemes are shown to have significantly lower computational complexities in comparison to existing MIMO radar schemes. Simulation results demonstrate the improved performance of the proposed schemes and also validate the derived analytical expressions.

The iterative search approach DOA estimation of monostatic L-shaped array MIMO radar

In the DOA estimation of monostatic L-shaped array MIMO radar, Multiple Signal Classification algorithm is efficient. But the peak searching process of Multiple Signal Classification algorithm needs large amount of spectrum calculation. Focusing on the spectrum peak searching process of Multiple Signal Classification, an iterative search approach to reduce the calculation amount is proposed. The first- and second-order derivatives of Multiple Signal Classification spectrum functions are achieved and the calculation amount is analyzed. Two-dimensional Newton iteration methods are applied with multisearching threads and derivation information. The searching approach can greatly reduce the computational complexity of Multiple Signal Classification spectrum peak searching. The total calculation amount of the first and second derivatives is about 15 times of the spectrum function. However, in the two-dimensional searching, especially in the high accuracy processes, the amount of searched points can be reduced by ten hundreds times, and the computation is much lower than the common spectrum peak searching method. The simulation results show that when the search thread number reaches 100, the searching process can effectively achieve the entire spectrum peak and get the correct DOA estimation.

Correction to "direction of arrival estimation for monostatic MIMO radar using reduced-dimension RISR algorithm"

Presents corrections to the paper, “Direction of Arrival Estimation for Monostatic MIMO Radar Using Reduced- Dimension RISR Algorithm,” (Xiaofang, D., et al), 11th International Symposium on Antennas, Propagation and EM Theory (ISAPE), Guilin, China, October 18-21, 2016.

ESPRIT-Like Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors under the Condition of Gain and Phase Uncertainties and Mutual Coupling.

In this paper, we focus on the problem of two-dimensional direction of arrival (2D-DOA) estimation for monostatic MIMO Radar with electromagnetic vector received sensors (MIMO-EMVSs) under the condition of gain and phase uncertainties (GPU) and mutual coupling (MC). GPU would spoil the invariance property of the EMVSs in MIMO-EMVSs, thus the effective ESPRIT algorithm unable to be used directly. Then we put forward a C-SPD ESPRIT-like algorithm. It estimates the 2D-DOA and polarization station angle (PSA) based on the instrumental sensors method (ISM). The C-SPD ESPRIT-like algorithm can obtain good angle estimation accuracy without knowing the GPU. Furthermore, it can be applied to arbitrary array configuration and has low complexity for avoiding the angle searching procedure. When MC and GPU exist together between the elements of EMVSs, in order to make our algorithm feasible, we derive a class of separated electromagnetic vector receiver and give the S-SPD ESPRIT-like algorithm. It can solve the problem of GPU and MC efficiently. And the array configuration can be arbitrary. The effectiveness of our proposed algorithms is verified by the simulation result.

Through-Wall Single and Multiple Target Imaging Using MIMO Radar

The ability to perform target detection through walls and barriers is important for law enforcement, homeland security, and search and rescue teams. Multiple-input-multiple-output (MIMO) radar provides an improvement over traditional phased array radars for through-wall imaging. By transmitting independent waveforms from a transmit array to a receive array, an effective virtual array is created. This array has improved degrees of freedom over phased arrays and mono-static MIMO systems. This virtual array allows us to achieve the same effective aperture length as a phased array with a lower number of elements because the virtual array can be described as the convolution of transmit and receive array positions. In addition, data from multiple walls of the same room can be used to collect target information. If two walls are perpendicular to each other and the geometry of transmit and receive arrays is known, then data can be processed independently of each other. Since the geometry of the arrays is known, a target scene can be created where the two data sets overlap. The overlapped scene can then be processed so that image artifacts that do not correlate between the data sets can be excised. The result gives improved target detection, reduction in false alarms, robustness to noise, and robustness against errors such as improperly aligned antennas. This paper explores MIMO radar techniques for target detection and localization behind building walls and addresses different mitigation techniques, such as a singular value decomposition of wavelet transform method to improve localization and detection of targets. Together, these techniques demonstrate methods that show a reduction in size and complexity of traditional through-wall radar systems while still providing accurate detection and localization. The use of the range migration algorithm in single and multi-target scenarios is shown to provide adequate imaging of through the wall targets in near and far field. Also, a multi-view algorithm is used to provide improved target detection and localization by fusing together multiple wall views.

Direction of arrival estimation via reweighted $$l_1$$ l 1 norm penalty algorithm for monostatic MIMO radar

In this paper, a reweighted $$l_1$$ norm penalty algorithm for direction of arrival (DOA) estimation in monostatic multiple-input multiple-output radar is proposed. In the proposed method, exploiting the inherent multidimensional structure of received data after matched filtering, the singular value decomposition (SVD) technique of the data matrix is employed to reduce the dimension of the received signal. Then a novel weight matrix is designed for reweighting the $$l_1$$ norm minimization by exploiting the coefficients of the reduced-dimensional Capon (RD-Capon) spatial spectrum. The proposed algorithm enhances the sparsity of the solution by the reweighted $$l_1$$ norm constraint minimization, and the DOAs can be estimated by finding the non-zero rows of the recovered matrix. Owing to utilizing the SVD technique and the novel weight matrix, the proposed algorithm can provide better angle estimation performance than RD-Capon and $$l_1$$ -SRACV algorithms. Furthermore, it is suitable for coherent sources and has a low sensitivity to the incorrect determination of the source numbers. The effectiveness and superior performance of the proposed algorithm are demonstrated by numerical simulations.

Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors

We study two-dimensional direction of arrival (2D-DOA) estimation problem of monostatic MIMO radar with the receiving array which consists of electromagnetic vector sensors (EMVSs). The proposed angle estimation algorithm can be applied to the arbitrary and unknown array configuration, which can be summarized as follows. Firstly, EMVSs in the receiver of a monostatic MIMO radar are used to measure all six electromagnetic-field components of an incident wavefield. The vector sensor array with the six unknown electromagnetic-field components is divided into six spatially identical subarrays. Secondly, ESPRIT is utilized to estimate the rotational invariant factors (RIFs). Parts of the RIFs are picked up to restore the source’s electromagnetic-field vector. Last, a vector cross product operation is performed between electric field and magnetic field to obtain the Pointing vector, which can offer the 2D-DOA estimation. Prior knowledge of array elements’ positions and angle searching procedure are not necessary for the proposed 2D-DOA estimation method. Simulation results prove the validity of the proposed method.