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Compressive Sensing for MIMO Urban Radar
Published in Moeness Amin, Compressive Sensing for Urban Radar, 2017
Yao Yu, Athina Petropulu, Rabinder N. Madan
A MIMO radar system consists of multiple transmit and receive antennas, and is advantageous in two different configurations, namely, widely separated antennas [1–3] and collocated antennas [4–6]. In the widely separated scenario, the transmit antennas are located far apart from each other relative to their distances to the target. The MIMO radar system transmits independent probing signals from its antennas that follow independent paths and thus each target return carries independent information about the target. Joint processing of the target returns results in diversity gain, which enables the MIMO radar to achieve improved target parameters estimation. In the collocated scenario, transmit and receive antennas are located close to each other relative to the target so that all antennas view the same aspect of the target. In this configuration, the phase differences induced by transmit and receive antennas can be exploited to form a long virtual array, with the number of elements equal to the product of the numbers of transmit and receive antennas (nodes). As a result, the MIMO radars enable superior resolution in terms of direction of arrival (DOA) estimation and parameter identification.
HFSS simulation of RCS based on MIMO system
Published in Jimmy C.M. Kao, Wen-Pei Sung, Civil, Architecture and Environmental Engineering, 2017
Nowadays, it’s expected to alleviate the impact for targets RCS with the variation of the observation angle (Mark, 2014). Comparing with the traditional radar, MIMO radar has the advantage of parallel multi-channel access to information, so it has broad application prospect. MIMO radar uses the transmitted and received signals simultaneously. As a result, many signals can be separated among the time domain, spatial domain and polarization domain. It has the advantages of higher processing dimension, making full use of scattered and received apertures, and higher angular resolution. MIMO radar uses irrelevance of echo signal raised by spatial diversity of target scattering, keeps average energy received of echo wave approximate to constant and the air target RCS smoothly, improves the target RCS fluctuation, and increases the detection performance and spatial resolution.
Introduction
Published in Wen-Qin Wang, Multi-Antenna Synthetic Aperture Radar, 2017
In multistatic MIMO SAR systems, two or more waveforms are transmitted by one or more antennas. MIMO using multiple antennas at both sides of the wireless link (see Figure 1.5) was a technique used in wireless communications to increase data throughout and link performance without additional bandwidth or transmit power [152]. It is known that, wireless communication concerns mainly the transmitted information across a wireless link and it is not necessary to estimate the channel parameters. In contrast, for radar, while it is possible to perform communication parasitically, the estimation of channel information is of primary interest. In MIMO communication systems, if the transmitter has the channel knowledge, the informed transmitter can then adapt its strategy to improve performance. Hence, similar to MIMO communication, the idea of MIMO radar has also drawn considerable attention in recent years [244]. The essence of the MIMO radar concept is to employ multiple antennas for transmitting several orthogonal waveforms and multiple antennas for receiving the echoes reflected by the target. The enabling concept for MIMO radar, e.g., the transmission of multiple orthogonal waveforms from different antennas, is usually referred to as waveform diversity [316, 444].
A modified spatial smoothing-based Nystrom approach for coherent target in bistatic MIMO radar
Published in International Journal of Electronics, 2020
Evans Baidoo, Jurong Hu, Lei Zhan
The demanding requirements of a radar system for location precision and resolutions of targets have aroused the studies of the multiple-input multiple-output (MIMO) radar over the past decade (Haimovich, Blum, & Cimini, 2008). A critical characteristic of the MIMO radar is its ability to simultaneously emit orthogonal waveforms using multiple antennas and accept same the reflected signals using multiple receive antennas. From theoretical research, MIMO radar has presented clear advantages in the suppression of noise, robust to fading effect, boost parameter identifiability, enhance spatial resolution among others (Augusto, Antonio De, & Huang, 2016; Bekkerman & Tabrikian, 2006; Cui, Li, & Rangaswamy, 2014), over the conventional-phased array radar. A major issue of concern in parameter identification which has attracted numerous research is the angle estimation of target (Chen, Zheng, Wang, Li, & Wu, 2017; Wen, Xiong, Su, & Zhang, 2017; Xu, Li, & Stoica, 2008; Zhang, Yin, Chen, Gao, & Ansari, 2012).
Low complexity 3D-OMP algorithms for DOD DOA and Doppler frequency estimation in bistatic MIMO radar
Published in International Journal of Electronics, 2019
Wengen Tang, Hong Jiang, Shuaixuan Pang
Multiple-input multiple-output (MIMO) radar is an emerging radar system employing multiple elements at both the transmitting and receiving antennas (Fishler, Haimovich, Blum, Chizhik, & Cimini, 2004). Parameter identification in MIMO radar has drawn much attention in target localization and imaging (Li, Stoica, Xu, & Roberts, 2007). For moving targets, joint estimation of the direction-of-departure (DOD), direction-of-arrival (DOA) and Doppler frequency is an important issue in bistatic MIMO radar. Numerous literatures have investigated the problem based on subspace methods, such as MUSIC, ESPRIT and Propagator Method (Li, 2013; Li & Qiu, 2013; Xu, Zhang, Xu, Zeng, & Yao, 2015; Xu, Zhang, Zhang, & Xu, 2014; Yunhe, 2010). More recently, the idea of using compressed sensing (CS) in MIMO radar has become a hot field (Liu, Zhang, Tang, Zhang, & Zhu, 2015; Rossi, Haimovich, & Eldar, 2014; Rossi et al., 2014; Shahbazi, Abbasfar, & Jabbarian-Jahromi, 2017; Yu, Petropulu, & Poor, 2010). Compared with the subspace-based methods, the CS-based methods own advantages in their application to the case of small samples and high correlations of sources. The orthogonal matching pursuit (OMP) (Tropp & Gilbert, 2007) is a promising algorithm to solve CS problem, which can obtain fast convergence speed and relatively high reconstruction quality and is suitable for long signals.
Angle estimation in MIMO radar using a new sparse representation approach
Published in International Journal of Electronics, 2019
Baobao Liu, Ercan Engin. Kuruoglu, Junying Zhang, Fulvio Gini, Tao Xue, Wenying Lei
Multiple-input multiple-output (MIMO) radar exploits multiple transmitters to simultaneously propagate diverse waveforms and thus uses multiple receivers to receive the reflected signals from targets (Haimovich, Blum, Chizhik, Ciminim, & Valenzuela, 2004). It received a lot of attentions (Chen, Chen, & Qian, 2008; Chen, Gu, & Su, 2008; Gao, Zhang, Feng, Wang, & Xu, 2009, Haimovich, Blum, & Cimini, 2008; Hassanien & Vorobyov, 2011; Li & Li, 2012; Li & Stoic, 2007) owing to the potential advantages of MIMO radar such as powerful sensitivity beneficial to detect slowly moving targets, better parameter identifiability, more degree of freedom (DOF) and better angular resolution over conventional phased-array radar (Haimovich et al., 2004, 2008; Li & Stoic, 2007). Generally, according to the configuration of the transmitting array and receiving array, MIMO radar can be divided into two types. One is called statistical MIMO radar, whose transmitting elements and receiving elements are widely spaced. Another is called co-located MIMO radar (Li & Stoic, 2007) including monostatic MIMO radar and bistatic MIMO radar, in which transmitting elements and receiving elements are closely spaced. The co-located MIMO radar can acquire unambiguous angle estimation since it can provide virtual beneficial aperture, which is bigger than real aperture. In this paper, we focus on monostatic co-located MIMO radar for direction of arrival (DOA) estimation of multiple targets, which is one of most significant aspects in array signal processing fields (Chen et al., 2008, 2008; Gao et al., 2009; Hassanien & Vorobyov, 2011; Li & Li, 2012).