On Probing Signal Design for MIMO Radar

This article presents two-dimensional (2-D) scattering centres' parameter estimation based on linear uniform multi-input multi-output (MIMO) radars. Unlike single-input single-output (SISO) radars, MIMO radars transmit multiple detection signals at the same time. We assume that each antenna of a MIMO radar transmits signals with a set of different spread spectrum codes so as to distinguish the echoes from all antennas. Then, with spread spectrum and de-spread spectrum techniques, each antenna of a MIMO radar could distinguish its own stepped-up frequency echoes transmitted by itself. In this article, we take m sequences as spread spectrum codes and assume that the ratio between the MIMO radars array aperture and the target distance is much less than 1, then we use 2-D fast Fourier transform (FFT) and matrix extended matrix pencil (MEMP) methods to extract 2-D scattering centres' parameters after MIMO radar have received de-spread echoes. Simulations illustrate that both SISO radars and linear uniform MIMO radars using the spread spectrum technique can get similar estimation results, and the MEMP method can afford super-resolution estimation results, whereas the 2-D FFT method is more useful when the number of MIMO radar array elements are no more than the number of scattering centres.

Space time coding plays quite an important role in the design of multi-input multi-output (MIMO) radar. It has been shown that the correlation characteristics of waveform set have a great impact on the performance of target detection and localisation for MIMO radar. In this study, the target detection and localisation performance of co-located MIMO radar is analysed theoretically and experimentally, when correlated (not ideally orthogonal or fully coherent) waveforms are transmitted. The relationship between transmit–receive beam pattern improvement and correlation coefficients of transmitted waveforms is investigated. The signal to interference and noise ratio varying with correlation coefficients in range-Doppler matched processing is defined and analysed. Furthermore, Cramer–Rao bound for target direction of arrival estimation and target detection probability in the Neyman–Pearson criteria are derived when transmitting correlated waveforms. It is shown that the MIMO radar detection and localisation performance is much dependent with the correlation level of transmitted waveforms, which can be quite useful for MIMO radar orthogonal waveform design. Numerical simulation validates the theoretical analysis.

Use of multiple transmit and receive antennas have become a popular research area in radar community after the success of the same concept in communication. It is shown by Fishler et al. that multi-input multi-output (MIMO) radar has considerable advantages compared to traditional radar and phased array radar systems. In this paper, detection performance of MIMO radar using Neyman-Pearson detector is investigated in K-distributed sea clutter for a practical scenario. Also, spikiness of K-distributed MIMO radar clutter is discussed with respect to the number of nodes.

New correlated MIMO radar covariance matrix design with low side lobe levels and much lower complexity

Based on the array architecture of multiple transmitting/receiving antennas, Multi-Input Multi-Output (MIMO) radar provides a new mechanism for radar imaging technology. In order to explore the processing approach to this imaging mechanism, the two dimensional (2D) imaging model of MIMO radar is established first, and the spatial sampling ability is analyzed from the concept of spatial convolution of the antenna elements. The target spatial spectral filling format of MIMO radar with monochromatic transmitting signal is described. High-resolution imaging capability of MIMO radar is analyzed according to spatial spectral coverage and the corresponding imaging algorithm is presented. Finally, field imaging experiment is used to demonstrate the superior imaging performance of MIMO radar.

Multi-input multi-output (MIMO) radar systems have taken considerable attention among radar community in recent years. As stated in some publications MIMO radars have serious performance advantages compared to single-input single-output (SISO) radars. Although MIMO radars perform well theoretically, they have some practical problems. One of these problems is the data transmission rate required by MIMO radars. Quantization directly affects the amount of data to be transmitted. In this paper effects of quantization on MIMO radar performance is covered.

A novel target detection algorithm for large array multi-input multi-output (MIMO) radar in spatially correlated noise is proposed in this paper using canonical correlation technique (CCT). In the signal model, two separate sub-arrays are employed as the receive array of a transmit diversity MIMO radar system. Assume that the elementary noise in each sub-array has spatial correlation, and the number of receive elements is large and grows as the same rate with the snapshots. The detection statistics is constructed based on the generalized likelihood ratio test (GLRT) criterion and the sample canonical correlation coefficient between the two subarrays. The expression of decision threshold is derived via the Tracy-Widom distribution of order 2 in random matrix theory. The simulation results show that the detection performance of the proposed algorithm is better than that of the conventional condition number (CN)-based algorithm in the presence of spatially correlated noise and large array.

The concept of multi-input multi-output (MIMO) radar has drawn considerable attention in recent years. One of the key technologies to enable the MIMO radar is to design the orthogonal signals to support simultaneous transmission from multiple antennas, because the orthogonality characteristics have a keen impact on the performance of the MIMO radar. In this paper, a novel approach to generate and process mutually orthogonal waveforms based on orthogonal frequency division multiplexing (OFDM) signals is proposed. The ambiguity function of the suggested waveforms is derived and analyzed based on the statistical derivation. A corresponding multi-channel signal processing scheme at the receiver part is proposed to eliminate the Doppler influence and suppress the cross-channel interference. Numerical simulation results based on the Monte Carlo method are presented to validate the theoretical derivation and analysis. The findings indicate that the inter-channel interference for moving targets can be eliminated and that the proposed interleaved OFDM signals are a suitable waveform set for the MIMO radar applications.

When Multi-Input Multi-Output (MIMO) radar and communication transceivers are co-existing and operating simultaneously in the same frequency band, interference can be managed by designing signal spaces that facilitate spectrum sharing. In this paper, a spatial precoder-decoder design based on Interference Alignment (IA) is proposed assuming that an interference channel is shared by K c communication users and K r radar users. In order to find an IA based precoder-decoder solution guaranteeing desired multiplexing gain or diversity order, the design problem is formulated using a rank minimization criterion with rank constraint for the communication subsystem and rank minimization criterion with l 0 norm constraint for the radar, respectively. To deal with the non-convex nature of the optimization problem, we relax the problem by using nuclear norm. A generalized likelihood ratio test for target detection and a maximum likelihood estimator for target direction parameter are derived. The performance of the proposed IA based precoder-decoder design is analyzed using the GLRT detector and the ML estimator for MIMO radar. Analytical and simulation results show that radar detection performance is similar to the interference-free case with desired diversity order if the decoder matrix is semi-unitary and IA is perfect.

We propose the use of compressive sensing (CS) in the context of a multi-input multioutput (MIMO) radar system that is implemented by a small scale network. Each receive node compressively samples the incoming signal, and forwards a small number of samples to a fusion center. At the fusion center, all received data are jointly processed to extract information on the potential targets via the CS approach. Since CS-based MIMO radar would require many fewer measurements than conventional MIMO radar for reliable target detection, there would be power savings during the data transmission to the fusion center, which would prolong the life of the wireless network. First, we propose a direction of arrival (DOA)-Doppler estimation approach. Assuming that the targets are sparsely located in the DOA-Doppler space, based on the samples forwarded by the receive nodes, the fusion center formulates an ℓ1-optimization problem, the solution of which yields the target DOA-Doppler information. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than required by conventional approaches. Second, we propose the use of step frequency to CS-based MIMO radar, which enables high range resolution, while transmitting narrowband pulses. For slowly moving targets, a novel approach is proposed that achieves significant complexity reduction by successively estimating angle-range and Doppler in a decoupled fashion and by employing initial estimates to further reduce the search space. Numerical results show that the achieved complexity reduction does not hurt resolution. Finally, we investigate optimal designs for the measurement matrix that is used to linearly compress the received signal. One optimality criterion amounts to decorrelating the bases that span the sparse space of the incoming signal and simultaneously enhancing signal-to-interference ratio (SIR). Another criterion targets SIRimprovement only. It is shown via simulations that, in certain cases, the measurement matrices obtained based on the aforementioned criteria can improve detection accuracy as compared to the typically used Gaussian random measurement matrix.

This letter discusses the problem of direction of departure (DOD) and direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar, and derives a reduced-dimension multiple signal classification (MUSIC) algorithm therein. The proposed algorithm, which only requires one-dimension search, can avoid the high computational cost within two-dimension MUSIC (2D-MUSIC) algorithm. We illustrate that the algorithm has better performance ESPRIT algorithm, and has very close performance to 2D-MUSIC algorithm. Further our algorithm requires no pair matching. Simulation results verify the usefulness of our algorithm.

This paper presents an approach for jointly estimating target locations and uncertain multipath propagation channel parameters with a multi-input multi-output (MIMO) radar system. The multipath channel is modelled with geometrical features such as flat planes and point reflectors, whose locations are uncertain. The practical issue of data association between radar returns and multipath modes in the presence of missed detections and false alarms is solved via a multihypothesis data association algorithm. Simulation results demonstrate enhanced target tracking performance in the presence of multipath even when the direct path is occasionally unobservable. (6 pages)

Robust channel estimation in time-varying channels is used to guarantee the quality of communication services, especially for Vehicle-to-Everything (V2X) scenarios. To improve the channel estimation accuracy and reduce the pilot overhead, multi-input multi-output (MIMO) radar is deployed to assist millimeter wave (mmWave) channel estimation. In this paper, we propose a MIMO radar aided channel estimation scheme using deep learning (DL) for the uplink mmWave multiuser (MU)-MIMO communications. To allocate pilot resources reasonably, we design a transmission frame structure of joint radar module and communication module, which divides the estimation scheme into two stages, i.e., the arrival/departure (AoA/AoDs) estimation stage and the gain estimation stage. In view of the imperfections of array elements in practice, we propose an AoA/AoDs estimation algorithm based on subspace reconstruction in the AoA/AoDs estimation stage named two-step angle estimation (TSAE) algorithm. In the gain estimation stage, a DL based channel gain estimator is designed. An autoencoder combined with residual structure named residual denoising autoencoder (RDAE) is proposed to eliminate the noise on wireless signals, which is passed into the least square (LS) estimation module to obtain gains. Simulation results demonstrate that the MIMO radar aided and DL-based channel estimator provides the efficient estimation performance of the high-mobility mmWave channel with fewer training resources.

This paper considers target detection in distributed multi-input multi-output (MIMO) radar with non-orthogonal waveforms in non-homogenous clutter. We first present a general signal model for distributed MIMO radar in cluttered environments. To cope with the non-homogenous clutter and possible clutter bandwidth mismatch, the covariance matrix of the disturbance (clutter and noise) signal is modeled as a random matrix following an inverse complex Wishart distribution. Then, we propose three Bayesian detectors, including a non-coherent detector, a coherent detector, and a hybrid detector. The latter is a compromise of the former two, as it forsakes phase estimation needed by the coherent detector, but requires the samples within a coherent processing interval (CPI) to maintain phase coherence that is unnecessary for the non-coherent detector. Simulation results are presented to illustrate the performance of these Bayesian detectors and their non-Bayesian counterparts in non-homogeneous clutter when the clutter bandwidth is known exactly and, respectively, with uncertainty.

Making full use of the virtual aperture expansion property with the sparse array multi-input multi-output (MIMO) radar, we can achieve positive contributions for parameter estimation. We focus on the design of the sparse array MIMO radar and the optimization of conventional algorithms. According to the joint subspace-based method and compressive sensing theory, we study the analysis and design of the sparse arrays, direction of arrival estimation, and joint angle estimation for improved performance and low complexity, aiming to establish systematic parameter estimation theory for the sparse array MIMO radar. Combining the aspects of array optimization design, mixed target parameter estimation, nonideal factor suppression, and distributed platform construction, the future development trend of the sparse array MIMO radar is discussed.