Non-orthogonal multiple access based cooperative spectrum sharing between MIMO radar and MIMO communication systems

PREFACE. CONTRIBUTORS. 1 MIMO Radar - Diversity Means Superiority (Jian Li and Petre Stoica). 1.1 Introduction. 1.2 Problem Formulation. 1.3 Parameter Identifiability. 1.4 Nonparametric Adaptive Techniques for Parameter Estimation. 1.5 Parametric Techniques for Parameter Estimation. 1.6 Transmit Beampattern Designs. 1.7 Conclusions. Appendix IA Generalized Likelihood Ratio Test. Appendix 1B Lemma and Proof. Acknowledgments. References. 2 MIMO Radar: Concepts, Performance Enhancements, and Applications (Keith W. Forsythe and Daniel W. Bliss). 2.1 Introduction. 2.2 Notation. 2.3 MIMO Radar Virtual Aperture. 2.4 MIMO Radar in Clutter-Free Environments. 2.5 Optimality of MIMO Radar for Detection. 2.6 MIMO Radar with Moving Targets in Clutter: GMTI Radars. 2.7 Summary. Appendix 2A A Localization Principle. Appendix 2B Bounds on R(N). Appendix 2C An Operator Norm Inequality. Appendix 2D Negligible Terms. Appendix 2E Bound on Eigenvalues. Appendix 2F Some Inner Products. Appendix 2G An Invariant Inner Product. Appendix 2H Kronecker and Tensor Products. Acknowledgments. References. 3 Generalized MIMO Radar Ambiguity Functions (Geoffrey San Antonio, Daniel R. Fuhrmann, and Frank C. Robey). 3.1 Introduction. 3.2 Background. 3.3 MIMO Signal Model. 3.4 MIMO Parametric Channel Model. 3.5 MIMO Ambiguity Function. 3.6 Results and Examples. 3.7 Conclusion. References. 4 Performance Bounds and Techniques for Target Localization Using MIMO Radars (Joseph Tabrikian). 4.1 Introduction. 4.2 Problem Formulation. 4.3 Properties. 4.4 Target Localization. 4.5 Performance Lower Bound for Target Localization. 4.6 Simulation Results. 4.7 Discussion and Conclusions. Appendix 4A Log-Likelihood Derivation. Appendix 4B Transmit-Receive Pattern Derivation. Appendix 4C Fisher Information Matrix Derivation. References. 5 Adaptive Signal Design For MIMO Radars (Benjamin Friedlander). 5.1 Introduction. 5.2 Problem Formulation. 5.3 Estimation. 5.4 Detection. 5.5 MIMO Radar and Phased Arrays. Appendix 5A Theoretical SINR Calculation. References. 6 MIMO Radar Spacetime Adaptive Processing and Signal Design (Chun-Yang Chen and P. P. Vaidyanathan). 6.1 Introduction. 6.2 The Virtual Array Concept. 6.3 Spacetime Adaptive Processing in MIMO Radar. 6.4 Clutter Subspace in MIMO Radar. 6.5 New STAP Method for MIMO Radar. 6.6 Numerical Examples. 6.7 Signal Design of the STAP Radar System. 6.8 Conclusions. Acknowledgments. References. 7 Slow-Time MIMO SpaceTime Adaptive Processing (Vito F. Mecca, Dinesh Ramakrishnan, Frank C. Robey, and Jeffrey L. Krolik). 7.1 Introduction. 7.2 SIMO Radar Modeling and Processing. 7.3 Slow-Time MIMO Radar Modeling. 7.4 Slow-Time MIMO Radar Processing. 7.5 OTHr Propagation and Clutter Model. 7.6 Simulations Examples. 7.7 Conclusion. Acknowledgment. References. 8 MIMO as a Distributed Radar System (H. D. Griffiths, C. J. Baker, P. F. Sammartino, and M. Rangaswamy). 8.1 Introduction. 8.2 Systems. 8.3 Performance. 8.4 Conclusions. Acknowledgment. References. 9 Concepts and Applications of A MIMO Radar System with Widely Separated Antennas (Hana Godrich, Alexander M. Haimovich, and Rick S. Blum). 9.1 Background. 9.2 MIMO Radar Concept. 9.3 NonCoherent MIMO Radar Applications. 9.4 Coherent MIMO Radar Applications. 9.5 Chapter Summary. Appendix 9A Deriving the FIM. Appendix 9B Deriving the CRLB on the Location Estimate Error. Appendix 9C MLE of Time Delays - Error Statistics. Appendix 9D Deriving the Lowest GDOP for Special Cases. Acknowledgments. References. 10 SpaceTime Coding for MIMO Radar (Antonio De Maio and Marco Lops). 10.1 Introduction. 10.2 System Model. 10.3 Detection In MIMO Radars. 10.4 Spacetime Code Design. 10.5 The Interplay Between STC and Detection Performance. 10.6 Numerical Results. 10.7 Adaptive Implementation. 10.8 Conclusions. Acknowledgment. References. INDEX.

Spectrum sharing between radars and cellular systems is an emerging area of research. In this paper, we model channel between a seaborne MIMO radar and MIMO cellular system. We model a two-dimensional (2D) channel to capture the azimuth aspect of the spectrum sharing scenario. Our channel modeling methodology allows MIMO radar to place accurate nulls in the azimuth location of base stations, thus, protecting them from harmful radar interference. We use a projection based approach, where radar waveform is projected onto null space of channel, for mitigating radar interference to BSs. This is also known as an approach based on eigen-nulling which is different from spatial-nulling commonly employed by radars. We show through simulations that the proposed spatial channel model allows eigen-nulling which performs superior to traditional spatial-nulling for interference mitigation. The proposed channel model can be leveraged to use eigen-nulling that enhances target detection and beampattern resolution of MIMO radar while mitigating interference to BSs.

Joint radar communication systems have been recently proposed for radar and communication sharing spectrum and hardware, to reduce the resource cost of platforms. In this paper, we consider the waveform design problem for a joint multiuser MIMO communication and MIMO radar system. We propose an optimization problem for waveform design, in which the signal-to-interference-plus-noise ratio (SINR) at users and the transmit beam pattern of radar are jointly optimized. To guarantee the efficiency of power amplifiers, the transmit waveform is constrained to be constant modulus. To deal with the non-convex constant modulus constraint, we propose an alternating descend method to solve the problem. The simulation results indicate that under high transmit signal-to-noise ratio (SNR), the communication quality can be guaranteed, along with a reasonable performance loss of radar. Moreover, the performance gap between the constant modulus constraint and the more relaxed per-antenna power constraint, is not significant.

We propose and demonstrate a pulsed-chaos multiple-input-multiple-output (MIMO) radar system in this paper. In the proposed MIMO radar system, multi-channel pulsed chaotic signals are extracted from an optical seed chaos source with Delta-like autocorrelation and flat spectrum. The seed chaos source is generated by passing the chaotic output of an external-cavity semiconductor laser through a dispersive self-feedback phase-modulation loop and used for MIMO radar signal generation. The cross-correlation characteristics of MIMO radar signals, the maximum channel number of separable mixed echoes, as well as the performances of multi-target ranging and anti-interference in the proposed pulsed-chaos MIMO radar system are systematically investigated. The results indicate that multi-channel pulsed-chaos signals with Delta-like autocorrelation can be simultaneously generated from the seed chaos source, and excellent quasi-orthogonality of transmission radar signals can be guaranteed. Moreover, it is demonstrated that the proposed pulsed-chaos MIMO radar supports multi-target ranging with a centimeter-level resolution and can maintain satisfactory performance under low SNR scenarios with various interferences.

This paper focuses on the problem of reconfigurable intelligent surface (RIS) aided co-design for multiple-input-multiple-output (MIMO) radar and MIMO communication systems with shared spectrum resources. The purpose is to maximize the radar output signal-to-interference-plus-noise-ratio (SINR) while ensuring the communication functionalities, where there is no line-of-sight (NLOS) propagation between the radar and the target. To achieve the goal, we develop a cyclic framework based on semi-definite programming (SDP), semi-definite relaxation (SDR), and alternating direction method of multiplier (ADMM) to jointly optimize the radar transmit waveforms, the receive filters, the communication codebook, and the RIS coefficients. Numerical experiments are conducted to evaluate the effectiveness of the proposed algorithm.

Recently proposed multiple input multiple output radars based on matrix completion (MIMO-MC) employ sparse sampling to reduce the amount of data that need to be forwarded to the radar fusion center, and as such enable savings in communication power and bandwidth. This paper proposes designs that optimize the sharing of spectrum between a MIMO-MC radar and a communication system, so that the latter interferes minimally with the former. First, the communication system transmit covariance matrix is designed to minimize the effective interference power (EIP) to the radar receiver, while maintaining certain average capacity and transmit power for the communication system. Two approaches are proposed, namely a noncooperative and a cooperative approach, with the latter being applicable when the radar sampling scheme is known at the communication system. Second, a joint design of the communication transmit covariance matrix and the MIMO-MC radar sampling scheme is proposed, which achieves even further EIP reduction.

Exploitation of coherency gain and diversity gain to improve the MIMO system performance is a hot research topic. This paper deals with the performance analysis of phased MIMO radar by utilizing the above said gains. This paper also includes the analysis of the trade-off between the coherency and spatial diversity gain in terms of the signal to noise (SNR) and performance of the phased MIMO system has been compared with the MIMO Radar system in term of the output SNR and probability of detection.

Multiple-input-multiple-output (MIMO) radar is attractive for target detection, parameter identification, and target classification due to diversity of waveform and perspective. However, the mutual interference among the waveforms may lead to performance degradation in resolving spatially close returns. In this paper, we consider the use of space-time coding (STC) to mitigate the waveform cross-correlation effects in MIMO radar. First, it turns out that a joint waveform optimization problem can be decoupled into a set of individual waveform design problems. Second, a number of monostatic waveforms can be directly used in a MIMO radar system, which offers flexibility in waveform selection. We provide conditions for the elimination of waveform cross correlation, and discuss four kinds of space time codes. In addition, we also extend the model to partial waveform cross-correlation removal based on waveform set division. Numerical results demonstrate the effectiveness of STC in MIMO radar for waveform decorrelation.

A method for generating diverse waveforms is necessary for future radar systems. In general, waveform diversity refers to adaptively changing transmitted waveforms based on the target and interference environment. The project team investigated the utility of diverse waveforms for better radar performance. The team also sought a process for generating mutually orthogonal, high performance waveform families for MIMO radar employment. The team developed an easy-to-use Matlab® “Waveform Toolbox” for generating waveform families suitable for employment on MIMO radar systems. The tool used the Matlab® Optimization toolbox to create waveform families. A scoring function was used to evaluate the utility of waveforms using the optimization algorithm.

This work considers the joint transmit design of multiuser MIMO communications and MIMO radar. In particular, we propose to maximize the sum-rate capacity of multiuser MIMO communications under certain transmit correlation mismatch level for MIMO radar, and solve the maximization via convex optimization. Then, a practical transmit design scheme based on Tomlinson-Harashima precoding is proposed to approach the sum-rate capacity. The numerical results on the sum-rate capacity indicate that the average capacity under Rayleigh fading channel is the highest when radar intends to form a omnidirectional transmit beam. The bit error rate simulation shows that the proposed transmit design scheme is about 7dB from the theoretic capacity.

Electronic Warfare (EW) and Radio Detection and Ranging (RADAR) are two of the many applications that rely on multi-channel and phase-coherent configurations for signal processing. We provide an overview of the complexities and requirements of a multi-channel phase-coherent measurement system. Multiple Input Multiple Output [MIMO] systems have to overcome key technical challenges related to phase, time, and frequency synchronization to coherently receive and process the data acquired/ generated from each input/output. In practical MIMO systems, the radio hardware should be capable of acquiring and/or generating such phase coherent signals across the multiple channels. Further, the systems need to be able to sustain the phase coherence over considerable duration of time, depending on the sensitivity of the system. However, drifts will occur, owing to the effects of, e.g., temperature, thermal expansion, mismatched cable lengths, uncorrelated phase noise, ADC sample, clock phase noise, and quantization noise. Thus, a calibration process is required to compensate for the drift, whenever it crosses a particular threshold value that defines the accuracy of the phase-coherent system. In this paper, an FPGA based software-defined calibration method is presented for synchronizing the phase and magnitude across multiple channels of a system. This method allows the phase/magnitude drift over time to be periodically monitored and calibrated when there is a need. With the FPGA built into the system, the calibration can take place remotely without the need of connecting the system to an external calibration kit. Also, measurement results are provided for a state of the art super heterodyne receiver system to show that the phase drift is lesser than ± 1 degree across a 500 MHz to 26.5 GHz frequency range at 23 °C ± 5 °C for two and four channel configurations. Such systems can be useful in a variety of real-world MIMO implementations such as Direction Finding, Beam Steering, Passive Radars, MIMO and Phased-Array Radar Systems, where phase coherence, alignment, and/or synchronization has added advantages to multi-channel systems.

This paper focuses on a simple beamforming problem and compares the MIMO and SIMO radar systems for the case where the transmitter and receiver are collocated. The simplicity of the application allows one to see clearly where the advantages of MIMO radar come from, and what the tradeoffs are. The comparison also includes a third system called the IFIR radar (interpolated FIR radar). The MIMO radar offers advantages in terms of clutter reduction, especially when the extra coherent integration time can be exploited, but it does not offer an SNR advantage - in fact the SNR is worse than that of the SIMO radar when extra integration cannot be performed. The IFIR radar, which is essentially a simple variation of phased array radars, has an advantage over the SIMO radar in terms of clutter reduction, and does not compromise SNR in order to achieve this. The multiple orthogonal waveforms used in MIMO radar typically require extra bandwidth for fixed range resolution but the IFIR radar does not require extra bandwidth.

Antenna beam pattern in MIMO radar systems, like any other radar, exhibits sidelobes. Naturally it is possible for clutter and other sort of interference to be detected accidently via these sidelobes. This has the effect of mis-aligning the main beam steering vector and reducing detection performance. In addition, the performance is dependent on the precision of the array calibration process. This paper examines the competing effects of both those components. Specifically, we examine adaptive processing that operates across the whole MIMO system aiming to reduce sidelobe clutter, restore the steering vector to its true position and hence maximize signal to clutter ratio and therefore detection performance. The MIMO version of an Adaptive Detector with Conic Acceptance and Rejection (ADCAR) is developed, resorting to a generalized likelihood ratio principle. Constant False Alarm Rate (CFAR) properties of the detector are demonstrated. The performance of the MIMO- ADCAR is numerically evaluated. The results show the proposed detector is capable of achieving a compromise between sensitivity of mainlobe signal and rejection of sidelobe signal.

We consider a MIMO radar system and derive a theoretical framework for the recoverability of targets in the azimuth-range-Doppler domain via compressive sensing type recovery algorithms. In particular we prove bounds on the achievable resolution and number of detectable targets in the presence of additive noise. Furthermore our theory reveals that even weak targets can be recovered reliably with the proposed approach.

In this paper, we innovatively introduce coupled canonical polyadic decomposition (C-CPD) into a Multi-Static-Multi-Pulse MIMO radar system, which consists of one transmit subarrays and multiple spatially-distributed receive subarrays, to localize targets. In the system, the received signals at each receive array can be stacked into a third-order tensor that admits CPD, and these tensors together follow C-CPD. Therefore C-CPD based algorithms can be applied to these stacked tensors to calculate the related factor matrices, some of which can be used to extract the position information of the targets, as presented in this paper. It is worth noting that our used method does not require the probing signals are mutually orthogonal. Nor do their waveforms need to be known. Experimental results shown in the last section will validate all of our claims.