Designing Unimodular Sequence Sets With Good Correlations—Including an Application to MIMO Radar

In multiple-input multiple-output (MIMO) radar, the efficiency of target detection in clutter that is distributed over a certain range bin-Doppler frequency region depends on the sidelobe level of the waveformspsila auto- and cross-ambiguity functions within this region. We introduce the MIMO ambiguity function as the sum of all auto- and cross-ambiguity functions of the K MIMO radar waveforms, with the integral of this function over some range-Doppler region serving as a measure of the orthogonality (separability) of the waveforms within this region of interest. We derive an upper bound on the attainable ldquoclear areardquo for the MIMO ambiguity function, and show that, with respect to the conventional single-waveform ambiguity function, the maximum clear area is K times smaller for the MIMO system.

This study investigates the problem of low probability of intercept (LPI)-based distributed multiple-input multiple-output (MIMO) radar waveform design against barrage jamming in signal-dependent clutter and coloured noise. Given the priori knowledge of the extended target impulse response, signal-dependent clutter, barrage jamming signals and coloured noise, the LPI-based scheme for optimal radar waveform design is proposed to minimise the total power consumption of the MIMO radar system by optimising the transmitted waveforms of different transmitters with a predetermined mutual information (MI) constraint for target characterisation performance. Firstly, the MI between the received echoes from the target at each receiver and the target impulse response is derived as a practical metric to characterise the parameter estimation performance of a target. Then, the LPI-based distributed MIMO radar waveform design strategy is developed. The resulting radar waveform optimisation problem is convex and solved analytically, whose solutions represent the optimum power allocation for each transmitter in the MIMO radar system. With the aid of numerical simulations, it is illustrated that to minimise the total transmission power, the optimal waveform should match with the target, clutter, jamming and coloured noise. In addition, it is also demonstrated that the LPI performance of the MIMO radar system can be significantly improved by employing the proposed radar waveform design scheme.

Multiple input multiple output (MIMO) radars have been intensively studied for their superior performance merits compared to conventional radars including improved spatial resolution and parameter estimation. To achieve these merits, the transmitted waveforms from the MIMO transmitting antennas should be mutually orthogonal. Herein, the orthogonality is attained in the Doppler domain, namely Doppler division multiple access (DDMA), as it enjoys the simplest structure among other orthogonality schemes. Heretofore, the research in MIMO radars has focused on optimizing MIMO radar performance from different aspects including efficient waveform design, antenna beam-forming, and adaptive processing techniques. However, MIMO radars have been also a target of deliberate jamming scenarios. In this paper, the focus is turned to the evaluation of the detection performance of DDMA MIMO radars in jamming environments by creating a two-sided engagement scenario. On the jammer side, a jamming technique is proposed, Doppler-range block jamming (DRBJ), to deny the detection of a DDMA MIMO radar equipped with a moving target detector (MTD). On the radar side, a comparative detection performance analysis of two different MIMO radar detectors, normalized adaptive matched filter (NAMF) and fast 3D constant false alarm rate (CFAR), is conducted using Monte Carlo simulations. This analysis unveils divergent design philosophies of the two detectors, with each prioritizing distinct performance objectives.

A multiple input multiple output (MIMO) radar is an emerging field which has attracted many researchers recently. In this type of radar, unlike an standard phased array radar, one can choose transmitted probing signals freely to maximize the power around the locations of the targets of interest, or more generally to approximate a given transmit beam-pattern, or to minimize the cross-correlation of the signals reflected back to the radar by the targets of interest. These signals can be correlated to each other or noncorrelated. Many papers have investigated beampattern design and waveforming in MIMO radars. One of the most famous approaches for beampattern design is named covariance based method which design cross-correlation matrix of transmitted signals instead of the signals. On the other hand, many papers have investigated beamforming of an standard phased array radar in different ways. In this paper it is desirable to design beampattern of a phased array radar by means of the MIMO radar covariance based beampattern design method, as a special case of general MIMO radar. Although many approaches are available for beamforming of phased array radar this approach would have less complexity compare to them. MATLAB software is being employed for simulations. (6 pages)

As a promising technology, reconfigurable intelligent surface (RIS) can customize the wireless channel through the adjustment of its phase, and strengthen the localization performance and anti-jamming capability of multiple input multiple output (MIMO) radar. Thus, it is imperative to investigate a jamming strategy against the RIS-aided MIMO radar localization system. In this paper, we consider a jamming power allocation problem for RIS-aided MIMO radar based on cramér-rao bound (CRB) criteria. To this end, we first derive the CRB regarding target location estimation of RIS-aided MIMO radar in the presence of jammers. Subsequently, we formulate the jamming power allocation problem to maximize the minimum of the average CRB of RIS-aided MIMO radar via jointly optimizing the jamming power and RIS phase under the power budget and peak power constraints. Due to the variable coupling and high non-convexity of CRB, the optimization problem is extremely challenging to tackle. To solve the non-convex problem, we develop an alternating iterative algorithm with respect to the jamming power allocation and RIS phase design optimization subproblems. Specifically, we first transform the jamming power allocation subproblem into the form of linear programming for solution. Then, the RIS phase design subproblem is relaxed by the arithmetic-harmonic mean inequality to circumvent the complex matrix inversion, and converted into a fractional programming problem that can be solved through quadratic transformation approach. Owing to a degeneration of the solution induced by the inequality relaxation, we further propose a tightening-relaxation strategy to improve the solution quality, and re-optimize the RIS phase based on the previously derived solution. The ultimate solution can be achieved by alternately optimizing the jamming power and the re-optimized RIS phase. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

Multiple Input-Multiple Output (MIMO) radar is the recent emergence of a new radar technology. This radar system is a new concept advanced for the inadequacy of the traditional system, it use spatial diversity and angel diversity to avoid multipath fading. In this paper, we mainly analyze the detection performance of MIMO radar, and at the same time compared its detection performance with phased array radar under the same conditions. This paper first established MIMO radar’s signal model based on the distribution of sources, and then analyzes the affect of radar’s detection performance through changing the number of antenna. The simulation result showed that the MIMO radar has a good detection performance compared with phased array radar in low signal-noise-ratio(SNR).

Multiple-input multiple-output (MIMO) radars have attracted much attention for their superior ability to enhance a system's performance. In this study, the authors' goal was the study of the spatial multiplexing gain of MIMO radars with widely separated antennas (WS-MIMO), which the authors showed that is equal to the number of unambiguously detectable targets. They obtained this number from two different aspects: first, by defining the ambiguity function of a WS-MIMO radar in the case of multiple targets, suitable for such purpose; Second, by modelling the MIMO radar system with a MIMO wireless channel. They showed that a MIMO radar is indeed a MIMO wireless system communicating the information about the existence of the targets. By such modelling, they could easily make the relation between dual concepts of MIMO radar and MIMO communication, one of which is the multiplexing gain.

A multiple input multiple output (MIMO) radar system is an emerging research field which has attracted many studies in recent years. This type of radar, unlike conventional phased array radar, can transmit different probing signals via its antennas, that may be correlated or uncorrelated with each other. This waveform diversity offered by MIMO radar systems enable superior capabilities compared to conventional phased array radar. Many papers have introduced different ways for beamforming of MIMO radar. In this paper it is desirable to analysis some methods for beamforming of MIMO radars with planar arrays constellation and examine their 3D result beam-pattern of such constellations for covariance based MIMO radar waveforming method. MATLAB software is being employed for simulations.

In this letter, the coexistence of a multiple-input multiple-output (MIMO) radar and a distributed MIMO communication systems is studied. Target returns contributed from both the radar transmitters and communication transmitters are employed to complete the radar task, leading to a hybrid active-passive MIMO radar network. For the communication task, not only are the communication signals received directly from communication transmitters, but also those bounced off from the target are exploited to extract useful information. The target localization Cramer–Rao bound and mutual information are derived for the radar and communication systems, respectively. It is shown that there is a performance gain due to the cooperation between the radar and communication systems.

Multiple-Input Multiple-Output (MIMO) technologies utilize multiple antennas at the transceivers and enable enhanced diversity or multiplexing gain. The first implementations of MIMO technology appeared in cellular communication and Wi-Fi systems. One of the main problems with early MIMO systems is their limited performance in meeting the requirements of next generation communication systems. Specifically, the parameters like peak data rates, spectrum efficiency and energy efficiency constraints cannot be met with existing MIMO architectures. Hence, early MIMO techniques evolved to massive MIMO (mMIMO) type of communication as the number of antennas at both transmitters and receivers increased. Included in 5G New Radio standard, mMIMO enabled an increase the spectral efficiency and coverage compared with earlier MIMO techniques. Nowadays, there exists an ever-increasing interest in the application of advanced MIMO technologies in 6G networks. These applications include ultra massive MIMO (mMIMO), holographic MIMO and intelligent reflecting surface (IRS) assisted MIMO. These new advanced MIMO techniques either employ larger number of antennas or extremely large aperture arrays compared with mMIMO and are named as extremely large-scale MIMO. This paper presents a detailed overview of all these techniques. Joint design of wireless communication and radar sensing, named as integrated sensing and communication (ISAC) as well, has shown to improve spectrum efficiency and reduce power consumption. Recent novel research on ISAC exploits the application of MIMO technology specifically in joint communication and radar sensing (JCS) systems. In this aspect, the other objective of this work is to exploit MIMO communication and MIMO radar coexistence. After summarizing the current state of the art on MIMO radar systems, this paper exploits joint application of advanced MIMO communication and radar sensing. Finally, it aims to offer a perspective on future research directions and the potential of overall MIMO systems discussed in the paper.

In the work presented here we explore the interest of target localization using multiple-input multiple-output (MIMO) radar systems with widely separated antennas. The time delays between every transmit/receive antenna are an integral part of target localization using MIMO radar systems. We assume that these time delays have already been estimated by a preprocessing algorithm. Accordingly, we propose a target localization algorithm using the least squares estimation which has a closed form without requiring any initial condition. Simulation results evaluate the performance of the proposed method in comparison with the Cramer-Rao lower bound (CRLB) and the best linear unbiased estimator (BLUE) method. The effect of the arrangement and number of transmitters and receivers on the algorithm performance is also investigated.

Designing unimodular waveforms with good auto- and cross-correlation is one of the main problems for multiple-input multiple-output (MIMO) radar. Although long MIMO radar sequences improve long-range detection performance, it is difficult to design such long sequences owing to limitations of code theory and the cost of exhaustive searches. An autoencoder is a type of neural network that has had great success in designing long code sequences for wireless communication. This paper proposes a framework for designing long MIMO sequences with good auto- and cross-correlation properties using an autoencoder. Numerical results show that sequences designed using the proposed framework demonstrate better correlation properties than existing algorithms.

For airborne radar systems, space-time adaptive processing (STAP) is the approach that is normally adopted to detect ground moving targets in ground clutter, but the technique becomes less effective if the speed of ground moving targets is very low because of the proximity of the Doppler frequency of targets to that of ground clutter. Typical low-speed ground target detection scenarios include detecting ground troops and slowly-moving ground vehicles. Multiple-Input Multiple-Output (MIMO) radar possesses multiple transmitting and receiving channels providing additional diversified reflections for fusion processing in target detection and measurements. MIMO radar has been extensively investigated [1, 2]; but the exact structure of MIMO radar is defined widely different. In this work, an innovative multi-level MIMO radar signal processing architecture is proposed for airborne systems to effectively detect low-speed ground moving targets. The new MIMO-based approach is demonstrated to be effective for detecting such low-speed ground targets in this work, and some simulation results are presented subsequently.

The moving target detection based on compressed sending (CS) multiple-input multiple output (MIMO) radar with widely separated antennas is studied. The centralized processing is considered. The traditional centralized MIMO radar requires an exhaustive search on every possible two dimensional target velocity, resulting in huge computational complexity. In this paper, we adopt the CS technique to drastically reduce the computational burden and evaluate the reduction of the required complexity. We derive the moving target detector for the CS MIMO radar. The performance of the proposed detector is analyzed, and the receiver operating characteristic (ROC) curves are provided.

Diversity-multiplexing tradeoff for cooperative MIMO radar and communications system