scholarly journals Codesign of Beam Pattern and Sparse Frequency Waveforms for MIMO Radar

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Chaoyun Mai ◽  
Songtao Lu ◽  
Jinping Sun ◽  
Guohua Wang
Keyword(s):  
Objective Function ◽  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Beam Pattern ◽  
Pattern Design ◽  
Input Multiple Output ◽  
Radar Waveforms ◽  
Transmitted Waveform

Multiple-input multiple-output (MIMO) radar takes the advantages of high degrees of freedom for beam pattern design and waveform optimization, because each antenna in centralized MIMO radar system can transmit different signal waveforms. When continuous band is divided into several pieces, sparse frequency radar waveforms play an important role due to the special pattern of the sparse spectrum. In this paper, we start from the covariance matrix of the transmitted waveform and extend the concept of sparse frequency design to the study of MIMO radar beam pattern. With this idea in mind, we first solve the problem of semidefinite constraint by optimization tools and get the desired covariance matrix of the ideal beam pattern. Then, we use the acquired covariance matrix and generalize the objective function by adding the constraint of both constant modulus of the signals and corresponding spectrum. Finally, we solve the objective function by the cyclic algorithm and obtain the sparse frequency MIMO radar waveforms with desired beam pattern. The simulation results verify the effectiveness of this method.

scholarly journals Passive MIMO Radar Detection with Unknown Colored Gaussian Noise

2021 ◽  
Vol 13 (14) ◽  
pp. 2708
Author(s):  
Yongjun Liu ◽  
Guisheng Liao ◽  
Haichuan Li ◽  
Shengqi Zhu ◽  
Yachao Li ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Gaussian Noise ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Practical Case ◽  
Ratio Test ◽  
Training Samples ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Colored Gaussian Noise

The target detection of the passive multiple-input multiple-output (MIMO) radar that is comprised of multiple illuminators of opportunity and multiple receivers is investigated in this paper. In the passive MIMO radar, the transmitted signals of illuminators of opportunity are totally unknown, and the received signals are contaminated by the colored Gaussian noise with an unknown covariance matrix. The generalized likelihood ratio test (GLRT) is explored for the passive MIMO radar when the channel coefficients are also unknown, and the closed-form GLRT is derived. Compared with the GLRT with unknown transmitted signals and channel coefficients but a known covariance matrix, the proposed method is applicable for a more practical case whenthe covariance matrix of colored noise is unknown, although it has higher computational complexity. Moreover, the proposed GLRT can achieve similar performance as the GLRT with the known covariance matrix when the number of training samples is large enough. Finally, the effectiveness of the proposed GLRT is verified by several numerical examples.

scholarly journals Robust Range Ambiguous Deceptive Target Suppression Based on Covariance Matrix Reconstruction

2021 ◽  
Vol 13 (12) ◽  
pp. 2346
Author(s):  
Zhuang Xie ◽  
Jiahua Zhu ◽  
Chongyi Fan ◽  
Xiaotao Huang ◽  
Jian Wang
Keyword(s):  
Covariance Matrix ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Matrix Reconstruction ◽  
Multiple Input ◽  
Weighting Vector ◽  
Input Multiple Output ◽  
Noise Covariance ◽  
Dominant Eigenvector ◽  
Covariance Matrix Reconstruction

When the deceptive targets are in the ambiguious range bin but are received at the same range gate with the desired target by the array, the traditional multiple-input multiple-output (MIMO) radar is not able to discriminate between them. Based on the unique range-dependent beampattern of the frequency diverse array (FDA)-MIMO radar, we propose a novel robust mainlobe deceptive target suppression method based on covariance matrix reconstruction to form nulls at the frequency points of the transmit–receive domain where deceptive targets are located. First, the proposed method collects the deceptive targets and noise information in the transmit–receive frequency domain to reconstruct the jammer-noise covariance matrix (JNCM). Then, the covariance matrix of the desired target is constructed in the desired target region, which is assumed to already be known. The transmit–receive steering vector (SV) of the desired target is estimated to be the dominant eigenvector of the desired target covariance matrix. Finally, the weighting vector of the receive beamformer is calculated by combining the reconstructed JNCM and the estimated desired target SV. By implementing the weighting vector at the receiving end, the deceptive targets can be effectively suppressed. The simulation results demonstrate that the proposed method is robust to SV mismatches and provides a signal-to-jamming-plus-noise ratio (SJNR) output that is close to the optimal.

scholarly journals DOA Estimation of a Novel Generalized Nested MIMO Radar with High Degrees of Freedom and Hole-Free Difference Coarray

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yule Zhang ◽  
Guoping Hu ◽  
Hao Zhou ◽  
Mingming Zhu ◽  
Fei Zhang
Keyword(s):  
Degrees Of Freedom ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Music Algorithm ◽  
Array Configuration ◽  
Matrix Reconstruction ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Array Aperture

A novel generalized nested multiple-input multiple-output (MIMO) radar for direction of arrival (DOA) estimation is proposed in this paper. The proposed structure utilizes the extended two-level nested array (ENA) as transmitter and receiver and adjusts the interelement spacing of the receiver with an expanding factor. By optimizing the array element configuration, we can obtain the best number of elements of the transmitter and receiver and the attainable degrees of freedom (DOF). Compared with the existing nested MIMO radar, the proposed MIMO array configuration not only has closed-form expressions for sensors’ positions and the number of maximum DOF, but also significantly improves the array aperture. It is verified that the sum-difference coarray (SDCA) of the proposed nested MIMO radar can get higher DOF without holes. MUSIC algorithm based on Toeplitz matrix reconstruction is employed to prove the rationality and superiority of the proposed MIMO structure.

scholarly journals Cognitive MIMO Frequency Diverse Array Radar with High LPI Performance

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ling Huang ◽  
Kuandong Gao ◽  
Zhiming He ◽  
Jingye Cai
Keyword(s):  
Optimization Problem ◽  
Target Location ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Beam Pattern ◽  
Multiple Input ◽  
Convex Problem ◽  
Input Multiple Output ◽  
Frequency Diverse Array ◽  
Low Probability

Frequency diverse array (FDA) has its unique advantage in realizing low probability of intercept (LPI) technology for its dependent beam pattern. In this paper, we proposed a cognitive radar based on the frequency diverse array multiple-input multiple-output (MIMO). To implement LPI of FDA MIMO transmit signals, a scheme for array weighting design is proposed, which is to minimize the energy of the target location and maximize the energy of the receiver. This is based on the range dependent characteristics of the frequency diverse array transmit beam pattern. To realize the objective problem, the algorithm is proposed as follows: the second-order nonconvex optimization problem is converted into a convex problem and solved by the bisection method and convex optimization. To get the information of target, the FDA MIMO radar is proposed to estimate the target parameters. Simulation results show that the proposed approach is effective in decreasing the detection probability of radar with lossless detection performance of the receive signal.

Target Localization for Distributed Multiple-Input Multiple-Output Radar Using Capon Algorithm with Diagonal Loading

2012 ◽  
Vol 229-231 ◽  
pp. 1599-1604
Author(s):  
Jin Li Chen ◽  
Jia Qiang Li ◽  
Yan Ping Zhu
Keyword(s):  
High Resolution ◽  
Covariance Matrix ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Target Localization ◽  
Diagonal Loading ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Simulation Results ◽  
High Level

The distributed multiple-input multiple-output (MIMO) radar can achieve the high- resolution capabilities of target localization by coherent processing, far exceeding the bandwidth-dependent resolution of traditional radar. The conventional beam former synchronizing the phase across the widely separated transmitting and receiving antennas creates high level sidelobes that causes ambiguity in target localization. The Capon beam former with lower level sidelobes for target localization suffers from the irreversible of the covariance matrix when the numbers of transmitting and receiving antennas increase. Thus, the Capon algorithm with diagonal loading is applied to distributed MIMO radar for target localization with lower level sidelobes. Simulation results are presented to verify the effectiveness of the proposed method.

scholarly journals Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study

2021 ◽  
Vol 13 (15) ◽  
pp. 2964
Author(s):  
Fangqing Wen ◽  
Junpeng Shi ◽  
Xinhai Wang ◽  
Lin Wang
Keyword(s):  
Numerical Study ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Phase Errors ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Angle Pair ◽  
Parallel Factor ◽  
Parafac Decomposition

Ideal transmitting and receiving (Tx/Rx) array response is always desirable in multiple-input multiple-output (MIMO) radar. In practice, nevertheless, Tx/Rx arrays may be susceptible to unknown gain-phase errors (GPE) and yield seriously decreased positioning accuracy. This paper focuses on the direction-of-departure (DOD) and direction-of-arrival (DOA) problem in bistatic MIMO radar with unknown gain-phase errors (GPE). A novel parallel factor (PARAFAC) estimator is proposed. The factor matrices containing DOD and DOA are firstly obtained via PARAFAC decomposition. One DOD-DOA pair estimation is then accomplished from the spectrum searching. Thereafter, the remainder DOD and DOA are achieved by the least squares technique with the previous estimated angle pair. The proposed estimator is analyzed in detail. It only requires one instrumental Tx/Rx sensor, and it outperforms the state-of-the-art algorithms. Numerical simulations verify the theoretical advantages.

scholarly journals A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hanwei Liu ◽  
Yongshun Zhang ◽  
Yiduo Guo ◽  
Qiang Wang ◽  
Yifeng Wu
Keyword(s):  
Sparse Representation ◽  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Time Spectrum ◽  
Weight Vector ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Multiple Measurement Vectors ◽  
Input Multiple Output ◽  
Temporally Correlated

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment.

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