Based on the array architecture of multiple transmitting/receiving antennas, Multi-Input Multi-Output (MIMO) radar provides a new mechanism for radar imaging technology. In order to explore the processing approach to ...Based on the array architecture of multiple transmitting/receiving antennas, Multi-Input Multi-Output (MIMO) radar provides a new mechanism for radar imaging technology. In order to explore the processing approach to this imaging mechanism, the two dimensional (2D) imaging model of MIMO radar is established first, and the spatial sampling ability is analyzed from the concept of spatial convolution of the antenna elements. The target spatial spectral filling format of MIMO radar with monochromatic transmitting signal is described. High-resolution imaging capability of MIMO radar is analyzed according to spatial spectral coverage and the corresponding imaging algorithm is presented. Finally, field imaging experiment is used to demonstrate the superior imaging performance of MIMO radar.展开更多
An antenna selection algorithm based on large-scale fading between the transmitter and receiver is proposed for the uplink receive antenna selection in distributed multiple-input multiple-output(D-MIMO) systems. By ut...An antenna selection algorithm based on large-scale fading between the transmitter and receiver is proposed for the uplink receive antenna selection in distributed multiple-input multiple-output(D-MIMO) systems. By utilizing the radio access units(RAU) selection based on large-scale fading,the proposed algorithm decreases enormously the computational complexity. Based on the characteristics of distributed systems,an improved particle swarm optimization(PSO) has been proposed for the antenna selection after the RAU selection. In order to apply the improved PSO algorithm better in antenna selection,a general form of channel capacity was transformed into a binary expression by analyzing the formula of channel capacity. The proposed algorithm can make full use of the advantages of D-MIMO systems,and achieve near-optimal performance in terms of channel capacity with low computational complexity.展开更多
文摘Based on the array architecture of multiple transmitting/receiving antennas, Multi-Input Multi-Output (MIMO) radar provides a new mechanism for radar imaging technology. In order to explore the processing approach to this imaging mechanism, the two dimensional (2D) imaging model of MIMO radar is established first, and the spatial sampling ability is analyzed from the concept of spatial convolution of the antenna elements. The target spatial spectral filling format of MIMO radar with monochromatic transmitting signal is described. High-resolution imaging capability of MIMO radar is analyzed according to spatial spectral coverage and the corresponding imaging algorithm is presented. Finally, field imaging experiment is used to demonstrate the superior imaging performance of MIMO radar.
基金Supported by the National Natural Science Foundation of China(No.61201086,61272495)the China Scholarship Council(No.201506375060)+1 种基金the Planned Science and Technology Project of Guangdong Province(No.2013B090500007) the Dongguan Project on the Integration of Industry,Education and Research(No.2014509102205)
文摘An antenna selection algorithm based on large-scale fading between the transmitter and receiver is proposed for the uplink receive antenna selection in distributed multiple-input multiple-output(D-MIMO) systems. By utilizing the radio access units(RAU) selection based on large-scale fading,the proposed algorithm decreases enormously the computational complexity. Based on the characteristics of distributed systems,an improved particle swarm optimization(PSO) has been proposed for the antenna selection after the RAU selection. In order to apply the improved PSO algorithm better in antenna selection,a general form of channel capacity was transformed into a binary expression by analyzing the formula of channel capacity. The proposed algorithm can make full use of the advantages of D-MIMO systems,and achieve near-optimal performance in terms of channel capacity with low computational complexity.
