Knowledge-aided detection of range-spread target in distributed 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.

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.

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.

We study diversity and interference mitigation with an emphasis on the interaction between the transmitter-receiver (Tx-Rx) pairs in a two-user, two-cell system in which the performance at each Rx depends on the actions of the both desired and the interfering Txs. We will propose two techniques. The first scheme is a greedy or competitive algorithm in which each Tx-Rx pair employs an orthogonal space-time (ST) block code combined with a multimode beamformer (a generalized ST code) to minimize the error probability to its own Rx till an equilibrium is reached. The second scheme is a cooperative algorithm, also based on a generalized ST code in which each Tx-Rx pair minimizes its error probability at its desired Rx but cooperates by limiting the amount of interference to the second pair's Rx. We will model and characterize the equilibrium of the competitive scheme (called the Nash equlibrium). We will then show that by using the cooperative algorithm, it is possible to find Pareto dominant actions where both Tx-Rx pairs either equal or improve their performance when compared with the NE actions. We will analyze the conditions for Pareto dominance of the cooperative scheme and finally demonstrate the performance improvements of both schemes over the single Tx-Rx pair generalized ST code.

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.

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

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.

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.

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.

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.

In this paper, we consider the problem of power control for sum-rate maximization on multiple interfering links [transmitter-receiver pairs (Tx-RX)] under a sum power constraint. We consider a single-frequency network, where all pairs are operating in the same frequency band, thereby creating interference with each other. We study the power-allocation problem for sum-rate maximization with and without quality-of-service (QoS) requirements on individual links. When the objective is only sum-rate maximization without QoS guarantees, we develop an analytic solution to decide optimal power allocation for a two-TX-RX-pair problem. We also develop a low-complexity iterative algorithm for a three-TX-RX-pair problem. For a generic N > 3 TX-RX pair problem, we develop two low-complexity suboptimal power-allocation algorithms. The first algorithm is based on the idea of making clusters of two or three TX-RX pairs and then leveraging the power-allocation results obtained for the twoand three-TX-RX-pair problems. The second algorithm is developed by using a high signal-to-interference-plus-noise ratio (SINR) approximation, and this algorithm can be also implemented in a distributed manner by individual TXs. We then consider the same problem but with additional QoS guarantees for individual links. We again develop an analytic solution for the two-TX-RX-pair problem and a distributed algorithm for N > 2 TX-RX pairs.

As it is not suffered by the well-known noise uncertainty problem of energy detection (ED), generalized likelihood ratio test (GLRT) based spectrum sensing schemes for a single secondary user node have recently been extensively addressed. In this paper, we extend the existing research to an optimal GLRT based cooperative spectrum sensing scheme in a heterogeneous network scenario, where each node can be equipped with different number of antennas and have different signal processing capacity, such as sampling rate. By considering the discrepancy of different secondary user nodes' sensing reliability due to different hardware configuration, we demonstrate that the optimal cooperative spectrum sensing scheme based on GLRT is a linear combination of each node's local test statistic. Moreover, the local test statistic is the logarithm of the arithmetic-to-geometric mean ratio of the eigenvalues associated with the sample covariance matrix. Furthermore, the optimal combining coefficient is a simple function of the numbers of the antennas and the received signal samples at each secondary user node, which can be intrinsically provided by each secondary user node without any prior information. Numerical results show that the proposed GLRT based cooperative spectrum sensing scheme performs better than the practical ED based scheme, i.e., equal gain combination based ED, especially under the case where the sensing reliability discrepancy of each secondary user node is significant.

The problem considered in this paper is the detection of targets in a multispectral image. One of the difficulties encountered in this problem is the fact that the abundances of the observed signals are unknown. The generalized likelihood ratio test (GLRT) is often used in detection problems such as this one. The GLRT replaces the unknown parameters, in this case the signal abundances, with maximum likelihood estimates (MLEs) of those parameters. In general, the GLRT is not an optimal test. It is argued that for the signal model in this paper, constrained least squares (CLS) estimates of the unknown parameters are more appropriate than MLEs. A hypothesis test called the constrained multirank signal detector (CMSD) is derived using CLS estimates of the signal abundances. The performance of this test is calculated and is compared to the performance of the GLRT derived for the same signal model.