Multiple-input Multiple-output Radar Research Articles

FDA-MIMO Beampattern Synthesis with an Analytical Method

Since the beampattern has the characteristics of range-angle dependence, frequency diverse array multiple-input multiple-output (FDA-MIMO) radar has a good application prospect. There have been many studies to improve the performance of the beampattern by optimizing the frequency offset. However, on the basis of fully understanding the time parameters, the relationship between the array element frequency offset and the beampattern performance still needs to be clarified. Based on a new FDA-MIMO radar framework, this paper presents an analytical solution of the beampattern, which removes the influence of the time parameter. Taking the minimum main lobe as the objective function, an analytical method for solving a better frequency offset is given. Then, a method of using the window function was proposed to reduce the high side lobes of the range dimension. Comparing with the existing FDA radar beampattern design methods, it can achieve a more focusing beampattern. The simulation results verify the correctness of the theory.

Three‐dimensional decoupling imaging method for wideband two‐dimensional multiple‐input‐multiple‐output radar

Wideband two-dimensional multiple input multiple output (MIMO) radar can be used for target three-dimensional (3-D) imaging. As the echo of wideband MIMO radar is derived from both time and spatial sampling, the time-frequency coupling is contained, resulting in imaging defocussing. To solve this problem, a time-frequency decoupling algorithm based on matrix enhancement and matrix pencil (MEMP) is proposed to achieve high-resolution 3-D imaging. By combining the enhanced matrix constructed via MEMP with the modern spectral estimation theory, the time-frequency coupling term is regarded as a space rotation factor and is eliminated in the process of target position estimation. Simulation results show that the proposed algorithm can reduce the negative influence of time-frequency coupling and improve the imaging accuracy of wideband two-dimensional MIMO radar.

Range Dividing MIMO Waveform for Improving Tracking Performance.

A multiple-input multiple-output (MIMO) method that shares the same frequency band can efficiently increase radar performance. An essential element of a MIMO radar is the orthogonality of the waveform. Typically, orthogonality is obtained by spreading different signals into divided domains such as in time-domain multiplexing, frequency-domain multiplexing, and code domain multiplexing. This paper proposes a method of spreading the interference signals outside the range bins of interest for pulse doppler radars. This is achieved by changing the pulse repetition frequency under certain constraints, and an additional gain can be obtained by doppler processing. This method is very effective for improving the angular accuracy of the MIMO radar for a small number of air targets, although it may have limitations in use for many targets or in high clutter environments.

FDA–MIMO radar transmitting subaperture design and anti-interference performance analysis

The hybrid of the frequency diversity array (FDA) radar and multiple-input multiple-output (MIMO) radar, namely, FDA–MIMO radar, provides more degrees of freedom and improves the overall performance of the system. The essence of FDA–MIMO radar transmitting subaperture design is to divide the transmit array into multiple subapertures to improve the interference suppression capability of the radar. Among them, orthogonal waveforms are transmitted between each subaperture, and each element inside the subaperture transmits the same waveform. Aiming at the problem of how the subaperture design of the FDA–MIMO radar affects its interference suppression performance, we analyzed the two aspects of the normalized beampattern and (non-)adaptive beamforming. We conclude that when interference is dominant, setting the number of subapertures to half of the number of transmit array elements can effectively reduce the sidelobe level and increase the output signal-to-interference-plus-noise ratio. A large number of simulation results verify the effectiveness and superiority of this method.

Performance Analysis and Waveform Optimization of Integrated FD-MIMO Radar-Communication Systems

Multiple-input multiple-output (MIMO) has been used in wireless communications to increase data rates via multiplexing or improve performance via diversity. Additionally, in radar systems, MIMO promises improved parameter identifiability and target resolution. Frequency diverse (FD)-MIMO radar can effectively distinguish targets that are closely spaced in the same angle cell by exploiting its range-angle-dependent transmit beamform. Therefore, in this paper, we first propose an integrated waveform design by embedding weighted, phase-modulated communication signals in the FD-MIMO radar waveform. Then, we derive the Cramer-Rao lower bound (CRLB) of the location estimation and analyze the communication performance achieved when the communication receiver extracts information from the integrated signals. Next, transmit beamforming is optimized to achieve the best tradeoff between the radar and communication performances of the system. Due to the nonconvexity of the optimization problem, we apply sequential parametric convex approximation (SPCA) and semidefinite relaxation (SDR) methods to solve the problem. The simulation results reveal the effects of some parameters, such as the frequency interval, symbol period, and communication symbol sequence, on radar and communication performance and demonstrate the tradeoff between radar and communication obtained by optimizing the transmit beamforming vector.

Multitarget Vital Signs Measurement With Chest Motion Imaging Based on MIMO Radar

Simultaneous multitarget vital signs measurement has become a hot issue for noncontact vital signs perception. However, there is still challenge in the multitarget heartbeat measurement due to the weakness of heartbeat signal and interference from complex environment. In this article, a new multiple-input–multiple-output (MIMO) continuous-wave (CW) radar system equipped with 2-D digital beamforming (DBF) is presented to measure the respiration and heartbeat of multiple human subjects at unknown positions simultaneously. Through 2-D beam scanning of the whole scene, a 2-D radar image is generated. From the image, the chest motion of multiple targets is accurately located. Then, the vital signs of targets are obtained through forming individual beams focusing on the chests of targets. Moreover, the low intermediate frequency (low-IF) architecture is adopted to minimize the impact of flicker noise in low-frequency amplifier stages. The experimental results demonstrate that the proposed MIMO 2-D imaging radar system can locate chest areas of multiple targets, suppress the clutters, and make vital signs measurement, heartbeat measurement in particular, more robust compared with single-input–multiple-output (SIMO) radar system in complex environment.

A New Method for SIAR Transmitting Waveforms to Reduce the Range-Angle Coupling

Synthetic impulse and aperture radar (SIAR) is an orthogonal multiple input multiple output radar. It has several advantages, including the ability to detect multiple targets, Antiradiated Missiles (ARM), and stealth targets. Despite its four-dimensional (4-D) nature, it exhibits a strong coupling between range and angle, which dictates how well SIAR performs in the range-angle domain. Not only does the SIAR use frequency codes to separate the antennas, but also to ensure orthogonality. They can, however, mitigate the strong coupling between range and angle. Pulse-to-pulse frequency code agility is a technique for suppressing the SIAR radar's sidelobes. In this letter, we propose a method for reducing the coupling between range and angle by combining sequential positive, negative and random code with the dot product. The idea of coupling reduction presented in this letter is based on the ratio of each antenna position and frequency codes, which is referred to $\alpha $ k , and multiple $\alpha $ k in an array provides new advantages for target detection. This technique has the potential to significantly reduce the sidelobe levels.

Performance Evaluation and Optimization of MIMO Radars Using Biomimetic Antenna Arrays

The improvement of the angular resolution of radar sensors is one of the crucial goals of current radar research. A promising approach to achieve this goal is inspired by the ears of a fly called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ormia ochracea</i> . The working principle was adapted for antennas, and the so-called biomimetic antenna arrays (BMAAs) aroused the interest of several research groups. In this work, BMAAs are incorporated into multiple-input multiple-output (MIMO) arrays, a very common approach of improving the angular resolution, to gain more degrees of freedom in array design. The MIMO BMAAs are modeled utilizing the effective biomimetic antenna distance, a fundamentally new measure introduced in this article to translate the special biomimetic phase progression into a spatial quantity. We present straightforward antenna configurations but also describe how a genetic algorithm can be utilized to optimize both antenna positions and BMAA parameters. The proposed arrays show various beneficial effects such as a wider angular range for unambiguous angle estimation or a narrower beamwidth. The impact of MIMO BMAAs on the angular resolution is thoroughly analyzed both theoretically and by radar measurements in the range of 77 GHz. The measurements confirm the modeling method very well and show a significant increase in the angular separability.

Joint transmit filter and sparse array design with transmit subaperturing FDA‐MIMO radar

Increasingly complex electromagnetic environment makes some troubles for a radar system. Improving the performance of the radar detection and suppressing the interferences are important challenges. In this study, joint transmit filters and sparse array design enabling the maximum signal to interference plus the noise ratio (SINR) beamforming at the transmit subaperturing frequency diverse array multiple-input multiple-output (TS-FDA-MIMO) radar is examined. However, the optimisation problem is non-convex. A cycle iteration algorithm is proposed to obtain the optimal transmit filters and sparse array. The total problem is divided into two sub-problems. The first optimisation sub-problem is the sparse design and the Successive Convex Approximation (SCA) with two-dimensional reweighted mixed l1, 2-norm method is introduced to solve the sub-problem. In addition, the transmit filters design is the second optimisation problem. The sub-problem is changed to the semi-definite relaxation (SDR) problem and the Gaussian random method is utilised to obtain the optimal transmit filters. Through the cycle iteration of the two sub-problems, the optimal solutions are obtained. Finally, the simulation results verified the effectiveness of the proposed method.

Robust Localization in Distributed MIMO Radar Using Delay and Angle Measurements with Impulsive Noise Robust TD/AOA Localization in Impulsive Noise

This paper considers target localization using time delay (TD) and angle of arrival (AOA) measurements in distributed multiple-input multiple-output (MIMO) radar. Aiming at the problem that the localization performance of existing algorithms degrades sharply in the presence of impulsive noise, we propose a novel localization algorithm based on ℓ p -norm minimization and iteratively reweighted least squares (IRLS). Firstly, the TD and AOA measurement equations are established in the presence of zero-mean symmetric α-stable noise; then, the localization problem is transformed to a ℓ p -norm minimization problem by linearizing the measurement equations; and finally, the ℓ p -norm minimization problem is solved using IRLS by which the target position estimate is obtained, and the optimal choice of norm order p is deduced. Moreover, the Cramér–Rao bound (CRB) for target position estimation in impulsive noise is also derived, generalizing the Gaussian CRB. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of localization accuracy and robustness in impulsive noise.

A new method for Synthetic Impulse and Aperture Radar transmitting waveform design based on non‐sequential positive, negative and random carrier frequency coding with dot product approach

Synthetic Impulse and Aperture Radar (SIAR) is a kind of Multiple Input Multiple Output (MIMO) radar. It is a four-dimensional radar that has advantages against stealth targets, multi targets, anti-radiated missile, reconnaissance and interference, as well as valuable features in high range resolution and high detection probability. SIAR uses special frequency coding separately in each antenna. This code has an essential function in SIAR, such as separating frequency bandwidth, satisfying orthogonality in transmitted signals, suppressing range-angle and range-Doppler frequency coupling. Pulse-to-pulse frequency code agility is a kind of radar signal coding that is used to suppress the sidelobes in range-Doppler frequency. In this study, a design method is suggested to transmit waveform based on non-sequential positive, negative and random carrier frequency coding with the dot product approach in pulse-to-pulse frequency code agility, to reduce sidelobes in range-Doppler frequency coupling. The simulation results showed that this method could effectively remove the strong coupling between range-Doppler frequency. Removing coupling in the range-Doppler frequency decreases the sidelobes.

Reconfigurable Linear Antenna Arrays for Beam-Pattern Matching in Collocated MIMO Radars

Beam-pattern matching plays an important role in multiple-input multiple-output radars. In the vast majority of research done in this area, the aim is to find the covariance matrix of the waveforms fed into the transmit array. Also, reconfiguring a preset array of antennas (antenna selection), which means turning <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> some of the antennas in the array, is an effective technique to reach the desired beam patterns, dynamically. In this article, we introduce a novel multistep method to implement this reconfiguration technique to a uniform linear array. In each step, by exploiting the relation between the diagonal elements of a covariance matrix resulted from solving a beam-pattern matching problem and the transmitted power of the antennas, we find the least important antenna of the array and turn it <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> accordingly. Then, we repeat this process until a predefined number of antennas remains. Our proposed method outperforms its counterparts in the literature in terms of beam-pattern matching as well as computational complexity, which makes it an appropriate method for real-time applications. Simulations are used to show the validity and superiority of the proposed method.

A Realistic Radar Ray Tracing Simulator for Large MIMO-Arrays in Automotive Environments

With the rise of high resolution multiple input multiple output (MIMO) systems, radar became an important sensor in the development of Advanced Driver Assistance Systems (ADAS) and autonomous driving applications. Autonomous driving will rely strongly on artificial intelligence. Since most modern classification algorithms are based on neural networks, they require huge amounts of data to perform well, especially in unexpected traffic situations. Radar sensor simulation can potentially produce a great variety of training data for machine learning algorithms, which makes it an important cornerstone in the development of ADAS. Furthermore, with radar simulators, different antenna configurations and various edge cases can be simulated. In this work, a versatile ray tracing toolchain based on the shoot and bouncing rays (SBR) approach is presented. The program is able to simulate complex urban environments including realistic clutter, by utilizing simplistic reflection models. The program does not only produce realistic radar images, but also generates camera-like images using the same materials. Furthermore, this work deals with the adaption of the SBR method to radar sensors with an arbitrary number of transmit (TX)- and receive (RX) antennas, which enables the simulation of large MIMO arrays. A novel performance optimization approach is proposed for large numbers of TX antennas, which reduces the runtime dramatically. The quality of the simulation is verified by measuring a complex and realistic scenario with a high resolution automotive MIMO radar. Also, a study of the effect on quality and runtime is being investigated for various optimization approaches, including the proposed method.

Height Measurement with Meter Wave Polarimetric MIMO Radar: Signal Model and MUSIC-like Algorithm

Inview of the advantages of polarization diversity and waveform diversity, polarimetric multiple-input multiple-output (MIMO) radar, which combines the two advantages, is a subject worthy of in-depth study. Height measurement is one of the key problems to be solved for meter wave radar. Therefore, this paper focuses on height measurement with meter wave polarimetric MIMO radar. First, we derive the height measurement signal model for flat ground reflection combined with the two diversity techniques. Then, we properly classify the signal model and modify it to make it suitable for the classical super-resolution height measurement algorithms, including the generalized multiple signal classification(MUSIC) algorithm and steering vector synthesis (SVS) MUSIC algorithm. The split beam question in SVS-MUSIC is found and the resolution is provided. The proposed algorithms are also extended to long dipoles and large loop MIMO radar. Moreover, the computational complexity of the two algorithms and the derivation of Cramer Rao bound (CRB) for polarimetric MIMO radar are given. These methods do not require decoherence. The innovation of this paper is to provide a height measurement signal model for meter wave polarimetric MIMO radar along with its effective solution. The simulation results prove the accuracy of this approach.

DOA estimation in beamspace for monostatic MIMO radar utilizing real polynomial rooting

A novel angle estimation method in beamspace for monostatic multiple-input multiple-output (MIMO) radar based on a real polynomial rooting technique is presented. In order to eliminate the redundancy of the received data, the reduced-dimensional matrix is used to reduce the dimension of the received data. To further reduce computational complexity, the reduced-dimensional data is transformed into the beamspace. In order to avoid computationally expensive spectrum search, we will introduce conformal transformation to determine the extreme value of the MUSIC spectrum by utilizing the real polynomial rooting technique. Finally, the DOA of the target can be obtained from these extreme values. Numerical results demonstrate the effectiveness of the proposed algorithm.

Joint Transmit and Receive Beamforming for Cognitive FDA-MIMO Radar With Moving Target

Cognitive radars can possess intelligent active sensing ability and adapt complex variable environment. Inspired by this technology, a cognitive frequency diverse array multiple-input multiple-output (FDA-MIMO) radar is proposed to apply for surveillance and detect the moving target timely. FDA-MIMO radar is a novel array structure which employs the small frequency offset across the transmit waveform to produce the range-angle dependent beampattern. The range-angle dependent beampatterns have ability to suppress the jammers when they are located at the same angle of the target. To obtain the maximum signal-to-interference-plus-noise-ratio (SINR) and suppress the jammers effectively, we consider the transmit and receive beamforming filters design and proposed an iterating optimization algorithm to obtain optimal beamforming filters. For the moving target, the Extended Kalman filter (EKF) method is utilized to update the position of the target and the new position information is applied for the filters design to obtain optimal performance of SINR timely. In addition, expressions for the detection probability and SINR are deduced for the FDA-MIMO radar target detection. Finally, numerical results show that the proposed cognitive FDA-MIMO radar can effectively suppress the jammers and detect the moving target.

A Four Cumulant-Based Direction Finding Method for Bistatic MIMO Radar with Mutual Coupling

The mutual coupling effects of the transmitter and receiver are known to degrade the performance of direction finding for a bistatic Multiple Input Multiple Output (MIMO) radar system. A four cumulant-combinatorial matrix-based algorithm is proposed to estimate jointly the Direction Of Departure (DOD)and Direction Of Arrival (DOA) of targets under the coexistences of unknown mutual coupling and Gaussian colored noise. Firstly, the multiple groups of four cumulant matrices both on transmitter and receiver are constructed by using the Kronecker product and the banded symmetric Toeplitz characteristics of the mutual coupling matrices. The block four-cumulant matrix is further constructed by combining the transmitter and receiver four cunmulant matrices. Then the new matrix is combined to extract the transmit and receive shift invariance matrices by using the transmitter and receiver four cumulant matrices. The results illustrate that: The proposed method can estimate the DOD and DOA of the targets efficiently in the presence of the strong mutual coupling effect, and parameters are paired automatically without extra pairing operation. The parameter estimation performance of the proposed method is better than those of the existing methods under the strong mutual coupling effect conditions.

Constant modulus waveform design for MIMO radar via manifold optimization

This paper concentrates on MIMO (multiple-input multiple-output) radar waveform design to improve SINR (signal-to-interference-plus-noise ratio). Due to the constant modulus constraint, the problem is essentially an NP-hard and high dimensional problem. Many conventional methods generally transform the original problem to a convex optimization problem through relaxation. To address it more efficiently, we propose a new approach based on a Riemannian manifold, which is the product of complex circles and a Euclidean space. The original constraint problem in our proposed method is considered as unconstrained over a restricted search space. Then, the reformulated problem is solved by Riemannian conjugate gradient (RCG) algorithm efficiently. Numerical results reveal that the proposed algorithm can achieve higher SINR via different input SNR while consumes a lower computation time.

Coded OFDM Waveforms for MIMO Radars

Emerging digital radar concepts such as orthogonal frequency-division multiplexing (OFDM) allow flexible signal generation. This opens up new opportunities in waveform design in a multiple-input multiple-output (MIMO) system such as introducing coding for signal multiplexing. In this article, coded MIMO OFDM waveforms are proposed and investigated that allow continuous and simultaneous wideband transmission for all transmitters of a multiple transmit and receive antenna array for spatial radar environment perception. Challenges for coded MIMO OFDM radar operation are derived, and three coded MIMO strategies are introduced and analyzed. Their potential is validated and compared to the standard subcarrier interleaving OFDM approach using simulations and measurements of an experimental 4 × <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ 4$</tex-math></inline-formula> MIMO OFDM radar at 77GHz.

Beampattern synthesis and spectral compatibility based MIMO radar waveform design

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.