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An Expanded Trilinear Model-Based Angle Estimation Algorithm for MIMO radar with Small Number of Transmit/Receive Antennas

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2016年第5期25卷 1650043-1650043页

作者： Li, Shu Lv, Weihua Zhang, Xiaofei Xu, Dazhuan Nanjing Univ Aeronaut & Astronaut Jiangsu Key Lab Internet Things & Control Technol Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Jiangsu Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Jiangsu Peoples R China

In this paper, we address the problem of angle estimation in a bistatic multiple-input multiple-output (MIMO) radar which exploits nonuniform linear array at both the transmitter and the receiver with small number of antennas. It is demonstrated that the conventional trilinear decomposition-based angle estimation algorithm can identify only a comparatively small number of targets under this condition. In order to increase the number of identifiable targets, we derive an expanded trilinear decomposition-based angle estimation algorithm for MIMO radar, which can expand the size of the trilinear model. The proposed algorithm not only has the advantages of not requiring spectral peak searching, nor additional pair matching and being suitable for nonuniform arrays, but also identifies more targets than the conventional trilinear decomposition-based angle estimation algorithm under the same conditions. Moreover, the angle estimation performance of the proposed algorithm is better than that of the conventional trilinear decomposition-based angle estimation algorithm and the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Simulation results illustrate the effectiveness and improvement of the proposed algorithm.

关键词： Angle estimation multiple-input multiple-output radar trilinear model

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Parametric Wald test for target detection with distributed MIMO radar in partially mixing homogeneous and non-homogeneous environments

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IET radar SONAR AND NAVIGATION 2022年第3期16卷 470-486页

作者： Zhu, Lingxiao Wen, Gongjian Liang, Yuanyuan Luo, Dengsanlang Song, Haibo Natl Univ Def Technol Coll Elect Sci Natl Key Lab Sci & Technol ATR Changsha Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China

This study focusses on the target detection problem in the distributed multiple-input multiple-output (MIMO) radar. The observation environment is assumed to be partially homogeneous in one transmitter-receiver path and simultaneously non-homogeneous for different paths. By characterising the disturbance signals with a series of auto-regressive (AR) processes, two parametric detectors based on the Wald test are developed. The first one utilises an iterative scheme to estimate the unknown parameters that were involved in the derivation, and the second one obtains these estimations sequentially. One of the most attractive features of the two distributed MIMO radar detectors is that they can be used in the presence/absence of the training data. More specifically, they can take advantage of the training data to boost the detection performances effectively even if the training data are very limited. The simulation results of several scenarios demonstrate the remarkable detection performances of authors' proposed methods compared with that of the competitors. Furthermore, the amplitude estimation performances of the two detectors are evaluated via the corresponding Cramer-Rao bound (CRB).

关键词： amplitude estimation performances MIMO communication unknown parameters auto-regressive processes disturbance signals MIMO radar parametric Wald test iterative methods nonhomogeneous environments multiple-input multiple-output radar target detection problem parametric detectors observation environment amplitude estimation training data iterative scheme object detection regression analysis transmitter-receiver path distributed MIMO radar detectors radar detection remarkable detection performances

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A sparse representation scheme for angle estimation in monostatic MIMO radar

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SIGNAL PROCESSING 2014年 104卷 258-263页

作者： Wang, Xianpeng Wang, Wei Liu, Jing Li, Xin Wang, Junxiang Harbin Engn Univ Coll Automat Harbin Peoples R China

In this paper, the problem of direction of arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar is addressed, and a sparse representation scheme for DOA estimation is proposed. Firstly, the reduced-dimensional transformation matrix and SVD-technique are utilized to reduce the computational complexity of the sparse signal reconstruction. Then the coefficients of the reduced-dimensional Capon (RD-Capon) spectrum are exploited to design a weight matrix for reweighting l(1) norm constraint minimization to enhance the sparsity of the solution, and the DOA can be estimated by finding the non-zero rows in the recovered matrix. The angle estimation performance of the proposed method is better than RD-ESPRIT and RD-Capon algorithms. Furthermore, the proposed method works well for coherence targets without any decorrelation procedure, and has low sensitivity to the priori information of the target number. Simulation results verify the effectiveness of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： multiple-input multiple-output radar Direction of arrival estimation Sparse representation Reduced-dimensional Capon

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Joint range and angle estimation for an integrated system combining MIMO radar with OFDM communication

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2019年第2期30卷 661-687页

作者： Liu, Yongjun Liao, Guisheng Yang, Zhiwei Xu, Jingwei Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

To perform the integration of radar and communication in waveform, we design an integrated system combining multiple-input multiple-output radar with orthogonal frequency division multiplexing (OFDM) communication. In this system, each antenna transmits the integrated waveform with a nonoverlapping block sub-frequency band. The utilized waveform is a variation of the classical OFDM communication waveform. In order to sufficiently exploit the entire system bandwidth and array aperture, a joint time and space processing approach is proposed, and hence the range and angle estimations with high resolution are obtained, whereas the range and angle are coupled. Moreover, the loss in processing gain and the Cramer-Rao bounds of range and angle estimates based on integrated waveform are derived, respectively. Theoretical analysis validates that the designed system is capable of implementing the radar and communication functions simultaneously. Finally, numerical results are presented to verify the effectiveness of the proposed approach.

关键词： Range and angle estimation Integration of radar and communication multiple-input multiple-output radar Orthogonal frequency division multiplexing Cramer-Rao bound

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Research on main-lobe deceptive jamming against FDA-MIMO radar

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IET radar SONAR AND NAVIGATION 2021年第6期15卷 641-654页

作者： Zhao, Yingjian Tian, Bo Wang, Chunyang Gong, Jian Tan, Ming Zhou, Changlin Air Force Engn Univ Air & Missile Def Coll Xian Peoples R China Natl Univ Def Technol Coll Informat & Commun Wuhan Peoples R China

A multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) produces a range-angle-dependent steering vector, which can suppress the deceptive jamming in joint range-angle domain. This in turn complicates the task of radar jamming. With the emergence and development of digital radio frequency memory (DRFM), radar electronic jamming technique has entered the age of coherent jamming. Based on the analysis of the superiority of the FDA-MIMO radar in cancelling the deceptive jammers, a novel range gate pull-off method via DRFM against FDA-MIMO radar is proposed. Compared with the traditional deceptive jamming methods, the proposed method cannot be suppressed due to the mismatch in range. In particular, even the anti-jamming methods based on power domain cease to be effective. The effectiveness of the proposed scheme in jamming the tracking performance of the FDA-MIMO radar is demonstrated via computer simulations.

关键词： jamming radar signal processing Electromagnetic compatibility and interference coherent jamming range-angle-dependent steering vector digital radio FDA-MIMO radar anti-jamming methods joint range-angle domain radar jamming multiple-input multiple-output radar radar equipment, systems and applications traditional deceptive jamming methods Signal processing and detection main-lobe deceptive jamming MIMO radar radar electronic jamming technique

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Taylor polynomial expansion based waveform correlation cancellation for bistatic MIMO radar localization

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SIGNAL PROCESSING 2012年第6期92卷 1404-1410页

作者： Dang, Bo Li, Jun Liao, Guisheng Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

A Taylor polynomial expansion (TPE) based waveform decorrelation algorithm with low complexity is proposed to cancel the effects of auto-correlation and cross-correlation of transmit waveform of multiple-input multiple-output (MIMO) radar in the receiver. Cayley-Hamilton theorem is first used to expand the direct matrix inversion to the form of polynomial matrix. And then, Taylor series is used to simplify the calculation of the polynomial matrix. Furthermore, a simplified method is proposed to select the normalized factor by exploiting the special properties of signal correlation matrix, where the convergence performance for the approximation error of the TPE decorrelator is also analyzed. Finally, the performance of waveform correlation cancellation is verified by applying it to bistatic MIMO radar multi-target localization. Simulation results demonstrate the effectiveness of the proposed algorithm. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Taylor polynomial expansion Waveform decorrelation multiple-input multiple-output radar Low complexity

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Fourth-order cumulants-based sparse representation approach for DOA estimation in MIMO radar with unknown mutual coupling

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SIGNAL PROCESSING 2016年 128卷 123-130页

作者： Liu, Jing Zhou, Weidong Wang, Xianpeng Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China

In this paper, a sparse representation approach based on fourth-order cumulants (FOC) is proposed for direction of arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar with unknown mutual coupling. For applying the sparse representation theory successfully, exploiting the special banded symmetric Toeplitz structure of mutual coupling matrices (MCM) in both transmit array and receive array, the unknown MCM in received data can be turned into a diagonal one to eliminate the mutual coupling. Then based on the new received data, a reduced dimensional transformation matrix is formulated, and the proposed method further constructs a FOC matrix with special formation, which reduce the computational complexity of sparse signal reconstruction. Finally a reweighted l(1)-norm constraint minimization sparse representation framework is designed, and the DOAs can be obtained by finding the non-zero rows in the recovered matrix. Owing to utilizing the fourth-order cumulants and reweighted sparse representation framework, compared with ESPRIT-Like, FOC-MUSIC and l(1)-SVD algorithms, the proposed method performs well in both white and colored Gaussian noise conditions, meanwhile it has higher angular resolution and better angle estimation performance. Simulation results verify the effectiveness and advantages of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multiple-input multiple-output radar Direction of arrival estimation Sparse representation Mutual coupling Fourth-order cumulants

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Target Localization in Asynchronous Distributed MIMO radar Systems With a Cooperative Target

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第6期21卷 4098-4113页

作者： Wen, Gongjian Song, Haibo Gao, Feng Liang, Yuanyuan Zhu, Lingxiao Natl Univ Def Technol Coll Elect Sci Natl Key Lab Sci & Technol ATR Changsha 410073 Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Hunan First Normal Univ Elect Informat Sch Changsha 410205 Peoples R China

In general, target localization in distributed multiple-input multiple-output (MIMO) radar systems requires synchronization among the sensors, which may not be feasible when the sensors are widely separated. In this paper, the target localization problem is addressed under asynchronous distributed MIMO radar systems in the presence of clock and frequency offsets with the aid of a cooperative target, whose position and velocity are known with measurement errors. Specifically, the influence of the cooperative target position and velocity errors on the unknown target localization accuracy is analyzed through the Cramer-Rao lower bound (CRLB) derivation firstly. Then two methods, namely, the offset estimation based localization method and the offset elimination based localization method, are proposed to estimate the unknown target location. The former estimates the clock and frequency offsets firstly and then locates the unknown target based on the offset estimates, and the latter achieves the unknown target location estimate based on the differential measurements of the two targets. Through the theoretical derivation and numerical simulations, the two proposed methods are demonstrated to be approximately unbiased and can attain the CRLB under mild noise. Moreover, the simulation results show that the proposed methods enjoy higher localization accuracy than the state-of-the-art algorithms.

关键词： Location awareness Sensors Clocks Transmitters Receivers MIMO radar Noise measurement Target localization time delay Doppler shift multiple-input multiple-output radar

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A novel unitary PARAFAC method for DOD and DOA estimation in bistatic MIMO radar

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SIGNAL PROCESSING 2017年 138卷 273-279页

作者： Xu, Baoqing Zhao, Yongbo Cheng, Zengfei Li, Hui Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Xian 710071 Peoples R China

In this paper, a novel unitary parallel factor (U-PARAFAC) algorithm of estimating direction-of-departure (DOD) and direction-of-arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar is proposed. A real-valued tensor signal model is constructed by applying the traditional forward-backward averaging technique. Subsequently, the fact that the real-valued tensor follows a PARAFAC model is proved, thus the subspace-based high-order singular value decomposition (HOSVD) method can be avoided in the subsequent solving process. Furthermore, directly operating the real-valued loading factors instead of the signal subspace, traditional unitary ESPRIT (U-ESPRIT) method is firstly extended to the real-valued PARAFAC model. The new algorithm, which exploits the multidimensional structure and does not require the estimation of signal subspace, having good performance especially at low signal-to-noise ratio (SNR). More attractively, compared with classical tensor methods such as the PARAFAC algorithm and the unitary tensor-ESPRIT algorithm, the U-PARAFAC algorithm still performs well without sacrificing array aperture when targets are highly correlated or closely spaced. Additional angle pair-matching is not required. Simulation results verify the effectiveness of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.

关键词： multiple-input multiple-output radar Angle estimation Real-valued tensor Parallel factor decomposition

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Angle estimation in MIMO radar using a new sparse representation approach

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INTERNATIONAL JOURNAL OF ELECTRONICS 2019年第11期106卷 1694-1709页

作者： Liu, Baobao Kuruoglu, Ercan Engin Zhang, Junying Gini, Fulvio Xue, Tao Lei, Wenying Xian Polytech Univ Sch Comp Sci Xian 710048 Shaanxi Peoples R China CNR Ist Sci & Tecnol Informaz A Faedo Pisa Italy Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China Univ Pisa Dipartimento Ingn Informaz Pisa Italy China Acad Space Technol Inst Nav & Intrasatellite Link Technol Beijing Peoples R China

Existing direction of arrival (DOA) estimation methods in multiple-input multiple-out (MIMO) radar systems will encounter the performance degradation in the cases of few snapshots, low signal-to-noise ratio (SNR), closely spaced targets, or strongly correlated sources. To improve it, this paper develops a new sparse representation-based DOA estimation method. The main contributions are as follows: i) we construct a new real-valued double weighted -norm minimisation model;ii) we derive an improved reduced-dimension technique to enhance estimation accuracy;and iii) we design optimal and sparse weights carefully to improve the corresponding estimation accuracy. Finally, the effectiveness and theoretical analysis of the presented approach are verified by extensive numerical simulations, which proves that the new algorithm performs well at low SNR and with a small number of snapshots as well as at the coherent source case.

关键词： multiple-input multiple-output radar direction of arrival (DOA) unitary transformation sparse representation subspace fitting

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