Random Time Division Multiplexing Based MIMO Radar Processing with Tensor Completion Approach

Abstract

Automotive radar pursues low cost and high performance, and especially hopes to improve the angular resolution under the condition of a limited number of multiple-input–multiple-output (MIMO) radar channels. Conventional time division multiplexing (TDM) MIMO technology has a limited ability to improve the angular resolution without increasing the number of channels. In this paper, a random time division multiplexing MIMO radar is proposed. First, the non-uniform linear array (NULA) and random time division transmission mechanism are combined in the MIMO system, and then a three-order sparse receiving tensor of a range-virtual aperture-pulse sequence is obtained during echo receiving. Next, this sparse three-order receiving tensor is recovered by using tensor completion technology. Finally, the range, velocity and angle measurements are completed for the recovered three-order receiving tensor signals. The effectiveness of this method is verified via simulations.

A Dual Load-Modulated Doherty Power Amplifier Design Method for Improving Power Back-Off Efficiency

“…Multiple-input multiple-output (MIMO) technologies are widely used in 5G mobile radio and applications in IoT. In recent years, they have also been widely used in sensors and wireless communication applications [ 1 , 2 ]. With the rapid development of wireless communication technology, amplitude modulation and high-order modulation modes, such as OFDM modulation, are often used in modern communication systems, which often result in the signal having a higher peak-to-average power ratio (PAPR) [ 3 , 4 , 5 ].…”