Adaptive selection method for generalized likelihood ratio test

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)

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).

This paper addresses jamming attack detection issue in a millimeter Wave (mmWave) massive MIMO system under noise uncertainty. Specifically, we apply the generalized likelihood ratio test (GLRT) to develop a one-step GLRT scheme for detecting jamming attack under the homogeneous and partially homogeneous noise environments, and exploit training data to estimate the unknown noise statistical information and replace it with resulting estimation in deriving GLRT procedure to obtain adaptive GLRT detection scheme. We also design a two-step GLRT scheme, where we first assume the unknown noise statistical information is known, and derive GLRT based on test data, and then replace it by the sample covariance matrix based on training data only to achieve a fully adaptive jamming attack detector. With the help of statistical signal subspace analysis and composite hypothesis testing theories, we further examine the statistical distributions of the jamming attack detection schemes and present the closed-form expressions of false alarm and detection probabilities for the proposed schemes under different noise environments. Finally, we implement extensive simulations to validate the theoretical results and evaluate the detection efficiency under various parameters.

The problem of tracking the direction of arrivals (DOA) and the direction of departure (DOD) of multiple moving targets is considered in bistatic multiple input multiple output (MIMO) radar. A low complexity angle tracking algorithm in bistatic MIMO radar is proposed. The proposed algorithm derived the difference between previous and current covariance matrices. And the relationship between the different of covariance matrices and the different of target angle is derived. The performance of the proposed algorithm is improved by averaging the covariance matrix elements. Last DOD and DOA are got by least square method. This algorithm avoids the decomposition of the covariance matrix and reduces the computational complexity, which realizes DOA and DOD of the adjacent moment automatic association. The simulation results demonstrate effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar.

Multiple Input Multiple Output (MIMO) radar is a new radar technique. It has many advantages over conventional phased radar in many ways such as anti-intercept of radar signal, low velocity target detection, and resolution. The signal transmit matrix of MIMO radar has much information such as target angle. In this paper, we analyze the signal model for MIMO radar. And then we present a new method to estimate the target angle from the matrix. At last, we show the efficiency by some simulations.

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.

This work addresses the problem of adaptive multiple-input multiple-output (MIMO) radar detector in heterogeneous clutter. We first derive the generalized likelihood ratio test (GLRT) based on the two-step design procedure. Then, considering with the Bayesian framework and the prior knowledge about the clutter, we adopt the Maximum A Posteriori (MAP) estimator to the clutter covariance matrix and extend the knowledge-aided Bayesian technique to MIMO radar detection. Finally, various simulation results and comparison with respect to other conventional technique are presented to demonstrate the effectiveness of the knowledge aided Bayesian technique, especially in presence of a small amount of secondary data. (4 pages)

The colocated multiple input multiple output (MIMO) radar can simultaneously obtain the multi-dimensional information of target for its diversity advantage. Combining the idea of MIMO radar and multiple pulse repetition frequency (PRF), this paper uses multiple orthogonal signals with different medium PRF simultaneously to obtain the target's range and velocity information of different PRF through matched filtering at the receiver. In the simulations, four orthogonal signals at four different PRF are transmitted to obtain the range and velocity information from the total echo signal. All of the results show the feasibility of this model.

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 paper, we consider moving target detection using a distributed multiple-input multiple-output (MIMO) radar on stationary platforms in nonhomogeneous clutter environments. Our study is motivated by the fact that the multistatic transmit-receive configuration in a distributed MIMO radar causes nonstationary clutter. Specifically, the clutter power for the same test cell may vary significantly from one transmit-receive pair to another, due to azimuth-selective backscattering of the clutter. To account for these issues, a new nonhomogeneous clutter model, where the clutter resides in a low-rank subspace with different subspace coefficients (and hence different clutter power) for different transmit-receive pair, is introduced and the relation to a general clutter model is discussed. Following the proposed clutter model, we develop a generalized-likelihood ratio test (GLRT) for moving target detection in distributed MIMO radar. The GLRT is shown to be a constant false alarm rate (CFAR) detector, and the test statistic is a central and noncentral Beta variable under the null and alternative hypotheses, respectively. Simulations are provided to demonstrate the performance of the proposed GLRT in comparison with several existing techniques.

In this paper, we deal with the problem of simultaneously detecting and tracking multiple targets using polarimetric multiple input multiple output (MIMO) radars. The problem is formulated in a Bayesian framework by modeling the collection of states as a random finite set. First, we propose a multiple sensor Multi-Bernoulli (MS-MeMber) filter based track-before-detect (TBD) algorithm suitable for both MIMO radars and polarimetric MIMO radars. Then the sequential Monte Carlo (SMC) implementations are performed to prove the effectiveness of the proposed algorithm. Simulation results show that the polarization diversity can be exploited to enhance the detecting and tracking performance of MIMO radars.

Signal fusion-based radar target detection algorithms are studied in this article for spatial diversity multiple input multiple output (MIMO) radar based on the generalised likelihood ratio test (GLRT) algorithm, under the assumption that covariance matrices of clutter signals received by different radar sites are different. Two signal fusion-based target detection algorithms are proposed, which can be applied in signal fusion networks (SFNs) with typical structures, at a low communication and computation cost. Simulation experiments in several scenarios indicate that two proposed signal fusion algorithms have better detection performances than decision fusion algorithms.

Formulation of the problem. The problem of spatial processing of signals in virtual receiving channels of the antenna array of a vehicular MIMO (Multiple Input Multiple Output) radar is considered. The virtual antenna array is formed by increasing the antenna resolution in the horizontal plane. Traditional short chirp signals with FMCW modulation are used as probing pulses. The problem of interference suppression in the azimuthal plane is considered. The case is also investigated when two cars fall into the main beam of the antenna array (AA) of the radar, and therefore it is necessary to use azimuth super-resolution methods. In modern vehicles, these options are necessary for collision avoidance systems and driver assistance systems. Goal. To develop two methods of spatial digital signal processing for narrowband interference suppression and target direction finding using a MIMO autoradio receiver antenna array. Results. An analytical expression for the received signal under conditions of Doppler frequency shift and short probing chirp pulses in an automobile MIMO radar is obtained. A MIMO radar structure with an encoder on the transmitting side that does not require a decoder on the receiving side is proposed. Practical significance. The results of numerical modeling of spatial suppression of narrow-band interference by the power vector method are presented. The results of numerical modeling of the accuracy of measuring the azimuth of a moving vehicle by the super-resolution minimal polynomial method are presented. The results of a full-scale experiment are shown, which confirmed the possibility of effective direction finding of two closely located vehicles in the case of a short sample of the input process.

MIMO radar can provide enhanced spatial resolution and achieve better target detection performance. Hence the parameter identifiability of the system can be significantly improved. In this paper, the issue of direction of arrival (DOA) estimation in multiple-input multiple-output (MIMO) radar is discussed, and several algorithms for DOA tracking based on monostatic MIMO radar in impulsive noise background are proposed and studied. These tracking algorithms are based on the concept of robust covariation (ROC), fractional lower order moment (FLOM), fractional lower order cyclic (FLOC) and similar normalization. The transmit and receive arrays of a monostatic narrowband MIMO radar are assumed to be close to each other and eigenstructure based MUSIC algorithm at the receiver is used to identify the DOA's of the targets of interest. Simulation results verify the usefulness of the proposed algorithms. The comparison of these four tracking algorithms show that the algorithm based on the concept of similar normalization has better performance than the others.