Performance Optimization and Parameters Estimation for MIMO-OFDM Dual-functional Communication-radar Systems

Abstract: In dual-functional communication-radar systems, common radio frequency (RF) signals are used for both communication and detection. For better compatibility with existing communication systems, we adopt multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) signals as integrated signals and investigate the estimation performance of MIMO-OFDM signals. We first analyze the Cramer-Rao lower bound (CRLB) of parameters estimation. Then, transmit powers over different subcarriers are optimized to achieve the best tradeoff between transmission rate and estimation performance. Finally, we propose a more accurate estimation method which utilizes canonical polyadic decomposition (CPD) of three-order tensor to obtain the parameter matrices. Due to the characteristic of the column structure of the parameter matrices, we just need to use DFT / IDFT to recover the parameters of multiple targets. The simulation results show that the estimation method based on tensor can achieve performance close to CRLB and the estimation performance can be improved by optimizing the transmit powers.