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Low complexity closed-form covariance matrix and direct constant-envelope waveforms design for MIMO radar transmit beampattern

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ELECTRONICS LETTERS 2019年第21期55卷 1149-1151页

作者： Yu, Wenqi Cui, Bingyu Wuhan Elect Informat Inst Key Res Lab Wuhan 430019 Hubei Peoples R China

multiple-input multiple-output (MIMO) radar has many advantages over the phased-array radar due to the waveform diversity, one of which is the greater flexibility to design the transmit beampattern. In this Letter, a low-complexity transmit beampattern design method, considering constant envelope (CE) waveforms, is proposed for the uniform linear array MIMO radar. First, the authors parameterise the waveform covariance matrix as the integral form of continuous spatial variable, by exploiting the beamforming weight vector in the main lobe region, to derive the closed-form solution of the waveform covariance matrix that fulfils positive semi-definiteness and uniform elemental power constraints. The transmit beampattern designed by the proposed method has the spatial characteristics of low main lobe fluctuation and peak-to-sidelobe level, and further mathematical explanation is illuminated. Next, they directly use the parameterised expression of waveform covariance matrix, approximately transformed into a discrete form of the spatial variable based on the Riemann integral definition, to efficiently synthesise correlated CE waveforms. The proposed method can provide transmit beampattern performance comparable to existing methods with a lower computational cost and ensure the CE property of the waveforms. The performance of the proposed method is verified and analysed via numerical examples.

关键词： MIMO radar phased array radar radar signal processing array signal processing MIMO communication covariance matrices low complexity closed-form covariance matrix constant-envelope waveforms design MIMO radar transmit beampattern multiple-input multiple-output radar phased-array radar waveform diversity low-complexity transmit beampattern design method constant envelope waveforms uniform linear array MIMO radar waveform covariance matrix integral form closed-form solution uniform elemental power constraints low main lobe fluctuation correlated CE waveforms transmit beampattern performance

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multiple-Frequency CW radar and the Array Structure for Uncoupled Angle-Range Indication

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2018年第12期17卷 2203-2207页

作者： Chen, Baoxin Huang, Yong Chen, Xiaolong Wang, Guoqing Guan, Jian Naval Aviat Univ Yantai 264001 Peoples R China

The frequency diverse array (FDA) radar has drawn great attention due to its range-, angle-, and time-dependent beam-pattern. However, the FDA radar suffers ambiguities in determining target locations, i.e., angle and range of targets are coupled. In this letter, we find that the key to decoupling the target location lies in destroying the mutual dependence of angle and range. Hence, the multiple-frequency continuous wave (MFCW) radar is proposed. The range measurement is obtained by transmitting continuous waves with multiple frequencies and separating the multiple echoes by digital mixer in the receiving end. The range information exists in the phase difference among the multiple echoes, and this process can be seen as sampling in range by multiple frequencies. Then, if a receiving array is used for spatial sampling, an angle measurement is obtained and the target location is decoupled. Four modes of the MFCW radar are derived and the reason for the uncoupled angle-range indication in the beampattern is analyzed in detail. The effectiveness of the proposed scheme is verified by simulation results.

关键词： Array antenna frequency diverse array (FDA) multiple-frequency continuous wave (MFCW) radar multiple-input multiple-output radar

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Reduced-Dimension MUSIC for Angle and Array Gain-Phase Error Estimation in Bistatic MIMO radar

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IEEE COMMUNICATIONS LETTERS 2013年第3期17卷 443-446页

作者： Li, Jianfeng Zhang, Xiaofei Cao, Renzheng Zhou, Ming Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Jiangsu Peoples R China

In this letter, the problem of joint angle and array gain-phase error estimation for bistatic multiple-input multiple-output (MIMO) radar is investigated, and an algorithm for joint estimation with the characteristics of multiple signal classification (MUSIC) is proposed. Through reduced-dimension transform, the angles can be obtained via two one-dimensional peak searches, and the array gain-phase error can be estimated by utilizing the estimated angles. The proposed algorithm can achieve automatically paired estimations of the angles, and is suitable for non-uniform linear array. For the case of separately-located ca-librated sensors, the algorithm can also work well. Furthermore, it has better angle and array gain-phase error estimation performance than the ESPRIT-like algorithm. The simulation results verify the algorithmic effectiveness and robustness of our proposed algorithm.

关键词： multiple-input multiple-output radar gain-phase error reduced-dimension multiple signal classification

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Angle estimation in MIMO radar using reduced-dimension Capon

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ELECTRONICS LETTERS 2010年第12期46卷 860-U81页

作者： Zhang, X. Xu, D. Nanjing Univ Aeronaut & Astronaut Dept Elect Engn Nanjing 210016 Peoples R China

The problem of direction of departure and direction of arrival estimation for multiple-input multiple-output (MIMO) radar is discussed, and a reduced-dimension Capon algorithm is derived. The proposed algorithm which only requires one-dimension search, can avoid the high computational cost within the two-dimension Capon (2D-Capon) algorithm. It is illustrated that the algorithm has slightly better performance than the 2D-Capon algorithm. Simulation results verify the usefulness of the algorithm.

关键词： 2D Capon algorithm direction of arrival estimation multiple-input multiple-output radar direction of departure estimation angle estimation Signal processing and detection MIMO radar direction-of-arrival estimation radar equipment, systems and applications

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Joint polarisation and frequency diversity for deceptive jamming suppression in MIMO radar

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IET radar SONAR AND NAVIGATION 2019年第2期13卷 263-271页

作者： Zhang, Xinxun Cao, Ding Xu, Leilei Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Xian 710071 Shaanxi Peoples R China Air Force Early Warning Acad Wuhan 430019 Hubei Peoples R China

The joint exploitation of polarisation and frequency diversity is considered here as a way to improve the deceptive jamming suppression capability in multiple-input multiple-output (MIMO) radar. The authors explore a special polarisation and frequency diverse MIMO (PFD-MIMO) radar system here. In the PFD-MIMO radar system, polarisation and frequency diversity are employed in the transmit array, and two-dimensional vector sensors are adopted in the receive array to measure the horizontal and vertical components of the received signals. To fully explore the potential of the PFD-MIMO radar system, the transmitting polarisation and frequency increment are both optimised by maximising the output signal-to-interference-plus-noise ratio. The simulation results demonstrate that the PFD-MIMO radar is more robust and exhibits a better performance for deceptive jamming suppression than the frequency diverse array MIMO radar and polarimetric MIMO radar. Moreover, improved deceptive jamming suppression performance is achieved when the transmitting polarisation and frequency increment are both optimised.

关键词： radar polarimetry radar signal processing jamming MIMO radar MIMO communication radar detection interference suppression improved deceptive jamming suppression performance polarimetric MIMO radar frequency diverse array MIMO radar frequency increment transmitting polarisation PFD-MIMO radar system special polarisation multiple-input multiple-output radar deceptive jamming suppression capability frequency diversity

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Joint Range and Angle Estimation Using MIMO radar With Frequency Diverse Array

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第13期63卷 3396-3410页

作者： Xu, Jingwei Liao, Guisheng Zhu, Shengqi Huang, Lei So, Hing Cheung Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China Shenzhen Univ Coll Informat Engn Shenzhen 518052 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 852 Hong Kong Peoples R China

Phased array is widely used in radar systems with its beam steering fixed in one direction for all ranges. Therefore, the range of a target cannot be determined within a single pulse when range ambiguity exists. In this paper, an unambiguous approach for joint range and angle estimation is devised for multipleinput multiple-output (MIMO) radar with frequency diverse array (FDA). Unlike the traditional phased array, FDA is capable of employing a small frequency increment across the array elements. Because of the frequency increment, the transmit steering vector of the FDA-MIMO radar is a function of both range and angle. As a result, the FDA-MIMO radar is able to utilize degrees-of-freedom in the range-angle domains to jointly determine the range and angle parameters of the target. In addition, the Cramer-Rao bounds for range and angle are derived, and the coupling between these two parameters is analyzed. Numerical results are presented to validate the effectiveness of the proposed approach.

关键词： multiple-input multiple-output radar frequency diverse array range ambiguity joint range and angle estimation Cramer-Rao bound

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Bayesian Detection in Gaussian Clutter for FDA-MIMO radar

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第3期71卷 2655-2667页

作者： Huang, Bang Wang, Wen-Qin Basit, Abdul Gui, Ronghua Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China

This paper investigates the Bayesian detection problem for a moving target that is embedded in a homogeneous Gaussian clutter with an unknown but stochastic covariance matrix for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. First, we propose a Bayesian detector based on structured generalized likelihood ratio test (SGLRT), namely BSGLRT, criteria that requires no training data. Then, we present a detector based on the Bayesian unstructured generalized likelihood ratio test (BUGLRT) to reduce the three dimensions (range-angle-Doppler) search into one-dimension Doppler searches for low-complexity implementation in practical applications. Moreover, the robustness of the BSGLRT and BUGLRT detectors is also analyzed. Numerical results reveal that the proposed Bayesian detectors and estimators, i.e. BSGLRT and BUGLRT, outperform their non-Bayesian counterparts in Gaussian clutter with a small number of snapshots and/or low signal-to-clutter rate (SCR) for FDA-MIMO radar.

关键词： Bayes methods Detectors Covariance matrices Clutter Manganese Object detection radar clutter Bayesian detection frequency diverse array multiple-input multiple-output radar generalized likelihood ratio test maximum likelihood target detection

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A Bistatic MIMO radar Angle Estimation Method for Coherent Sources in Impulse Noise Background

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WIRELESS PERSONAL COMMUNICATIONS 2021年第4期116卷 3567-3576页

作者： Gong, Jian Guo, Yiduo Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China Air Force Engn Univ Xian 710051 Shaanxi Peoples R China Tong Fang Elect Sci & Technol Co Ltd Jiujiang 332007 Jiangxi Peoples R China

In order to solve the angle estimation problem of coherent sources in the impulse noise background, an algorithm based on infinite norm normalization preprocessing and sparse representation (INN-SR) is proposed. First, an infinite norm normalization preprocessing method is used to reduce the impact of the impact noise. Then the sparse decomposition method is used to construct the restoration dictionary to estimate the DOA and DOD. Finally, accurate matching of target DOA and DOD is achieved based on maximum likelihood method. The algorithm does not need to know the number of sources and can also work well for coherent sources.

关键词： Direction of arrival multiple-input multiple-output radar Sparse representation Impulse noise

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Robust low-range-sidelobe target synthesis for airborne FDMA-MIMO STAP radar

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IET radar SONAR AND NAVIGATION 2020年第10期14卷 1592-1602页

作者： Zhao, Yang Wu, Jianxin Suo, Zhiyong Liu, Xiaoyu Liang, Yi Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510275 Peoples R China

With the multi-function need for airborne space-time adaptive processing (STAP) radar, range synthesis is usually necessary for the follow-up function of target recognition. Utilising the excellent performance on orthogonality and bandwidth synthesis of the frequency division multiple access (FDMA) waveform and the longer accumulation time of multiple-input multiple-output (MIMO) radar, a robust low-sidelobe target synthesis method for airborne FDMA-MIMO STAP radar is developed. The spectra characteristics of target and clutter are first analysed. Then, the keystone transform is employed to mitigate velocity migration. To avoid gain fluctuation induced by multiple independent adaptive weight vectors, the robust STAP beamformer is developed. Finally, to decouple the range-angle coupling relationship resulted from the FDMA waveform, angle compensation is added in the robust STAP beamformer. Numerical examples are given to demonstrate the effectiveness of the presented method.

关键词： radar clutter space-time adaptive processing array signal processing airborne radar frequency division multiple access MIMO communication radar signal processing FDMA waveform range-angle coupling relationship robust STAP beamformer multiple independent adaptive weight vectors clutter low-sidelobe target synthesis method multiple-input multiple-output radar longer accumulation time frequency division multiple access waveform bandwidth synthesis orthogonality target recognition range synthesis airborne FDMA-MIMO STAP radar low-range-sidelobe target synthesis

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Deep MIMO radar target detector in Gaussian clutter

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IET radar SONAR AND NAVIGATION 2022年第10期16卷 1681-1695页

作者： Luo, Dongqi Ye, Zixuan Si, Binqiang Zhu, Jihong Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Beijing Informat Sci & Technol Univ Sch Instrumentat Sci & Optoelect Engn Beijing Peoples R China Tsinghua Univ Dept Precis Instrument Beijing Peoples R China

We focus on the target detection problem of the multiple-input multiple-output (MIMO) radar in clutter. Most of the existing MIMO radar detection works rely on the clutter models, which may limit their scopes of application. To address this issue, a deep learning based MIMO radar target detection framework is proposed in this paper, which utilises the powerful representation and discrimination capabilities of deep neural networks (DNNs) to improve the detection performance in a data-driven manner and does not require any prior information about the clutter distribution. In most classification problems, DNNs are trained with the cross entropy loss, which promotes DNNs to achieve higher accuracy. However, the main concern in the radar target detection problem is the probability of detection (PD) under a given probability of false alarm. In this framework, a novel loss is proposed to directly maximise the average PD of the DNN, or equivalently maximise the area under curve of the DNN. Under the DL-based MIMO radar target detection framework, a deep convolutional neural network (CNN) architecture is introduced, and an input feature of the received signal for the CNN based on the conventional generalised likelihood ratio test statistics is designed to further improve the detection performance. Finally, extensive simulation results are presented to validate the detection performance and the robustness of the proposed methods.

关键词： clutter models MIMO communication radar target detection problem deep neural networks deep learning (artificial intelligence) MIMO radar learning (artificial intelligence) MIMO radar target detection framework radar clutter image classification telecommunication computing DNNs clutter distribution multiple-input multiple-output radar convolutional neural nets entropy object detection deep convolutional neural network architecture deep learning feature extraction probability deep MIMO radar target detector existing MIMO radar detection works Gaussian clutter detection performance radar detection

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