sensors Article Compressive Sensing-Based Radar Imaging and Subcarrier Allocation for Joint MIMO OFDM Radar and Communication System SeongJun Hwang 1, Jiho Seo 1, Jaehyun Park 1,*, Hyungju Kim 2 and Byung Jang Jeong 2 1 Division of Smart Robot Convergence and Application Engineering, Department of Electronic Engineering, Pukyong National University, Busan 48513, Korea;
[email protected] (S.H.);
[email protected] (J.S.) 2 Radio & Satellite Research Division, Communication & Media Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea;
[email protected] (H.K.);
[email protected] (B.J.J.) * Correspondence:
[email protected] Abstract: In this paper, a joint multiple-input multiple-output (MIMO OFDM) radar and communication (RadCom) system is proposed, in which orthogonal frequency division multiplexing (OFDM) waveforms carrying data to be transmitted to the information receiver are exploited to get high-resolution radar images at the RadCom platform. Specifically, to get two-dimensional (i.e., range and azimuth angle) radar images with high resolution, a compressive sensing-based imaging algorithm is proposed that is applicable to the signal received through multiple receive antennas. Because both the radar imaging performance (i.e., the mean square error of the radar image) and the communication performance (i.e., the achievable rate) are affected by the subcarrier allocation across multiple transmit antennas, by Citation: Hwang, S.; Seo, J.; Park, J.; analyzing both radar imaging and communication performances, we also propose a subcarrier allocation Kim, H.; Jeong, B.J. Compressive strategy such that a high achievable rate is obtained without sacrificing the radar imaging performance. Sensing-Based Radar Imaging and Subcarrier Allocation for Joint MIMO OFDM Radar and Communication Keywords: MIMO OFDM radar and communication; subcarrier allocation strategy; Bayesian match- System.