Effects of transmitting correlated waveforms for co‐located multi‐input multi‐output radar with target detection and localisation

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.

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.

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.

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.

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

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.

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.

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.

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 examine the moving range-spread target in distributed multi-input multi-output (MIMO) radar. More precisely, a knowledge-aided (KA) model for nonhomogenous disturbance is proposed. We model the covariance matrices for different transmit-receive (Tx-Rx) pairs as random matrices, which share the same matrix structure but with different power levels for the non-homogeneous clutter powers across different Tx-Rx pairs. Two detectors are proposed. The first is a KA generalized likelihood ratio test (GLRT) without range training. The second is an ad-doc KA GLRT with range training. Computer simulations and analyses show the performance improvements offered by the proposed detectors in heterogeneous scenarios.

For target localization with radar, time of arrival (TOA), angle of arrival (AOA) and frequency of arrival (FOA) informations can be used in cooperation. In general, TOA information is enough to localize a target. Moreover, if the target velocity should be estimated, the target velocity is able to estimated by using doppler frequency. In this paper, the Cramer-Rao Bound (CRB) is shown for Multi Input, Multi Output (MIMO) radar with widely separated antennas case. CRB's are compared for different targets and transmitter-receiver configurations.

Orthogonal radar waveforms originating from spatially distributed transmitters (TXs) usually arrive at a receiver (RX) in non-orthogonal forms, as they propagate through different paths with distinct delays and Doppler frequencies in distributed multi-input multi-output (MIMO) radar. Non-orthogonal waveforms complicate the composition of the target and clutter returns, making it more challenging to separate one from the other. In this paper, we consider joint target detection and clutter mitigation in distributed MIMO radar. We first present a general signal model for distributed MIMO radar in cluttered environments. Next, we propose three families of detection solutions, including non-coherent detectors that require no phase estimation and are relatively simple to implement, coherent detectors that offer enhanced detection performance given accurate phase information, and hybrid detectors that are a compromise of the former two, requiring only local phase coherence but no explicit phase estimation. In addition, approximate solutions are proposed in each category with further complexity reduction, using high clutter-to-noise (CNR) approximation or convex relaxation. Simulation results are presented to demonstrate the performance of the proposed detectors, which outperform their earlier counterparts that neglect the presence of clutter.

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.

In this paper, a new space-time coding configuration for target direction finding by active arrays called multi-input multi-output (MIMO) arrays is proposed. Multiple mutually uncorrelated (orthogonal) probing signals are transmitted by MIMO array whose steering vectors are the convolution of transmit and receive steering vectors of conventional phased array, which is essentially equivalent to extending the array aperture by additional virtual sensors. These virtual sensors can be used to form narrower beams with lower sidelobes because MIMO array beam pattern is the product of the transmit beam pattern and the receive beam pattern. Minimum variance distortionless response (MVDR) method for DOA is studied and simulations and experiments show that the MIMO array can achieve better performance in target detection and angular resolution over conventional phased arrays.