In this article, to get a high-resolution radar image with distributed frequency modulated continuous waveform multiple-input–multiple-output (FMCW MIMO) radar, Bayesian matching pursuit (BMP)-based imaging methods are proposed, in which the received signals at the distributed FMCW MIMO radars are reformulated in terms of the (azimuth, range) patches in the image region of interest and the maximum a posterior (MAP) estimator that can estimate the azimuth angles and ranges of multiple targets is then derived. For a single FMCW MIMO radar, we first propose the BMP-based imaging, in which the support vector (indicating the presence of targets in the radar image patches) and the associated coefficients (the reflection coefficients of the associated targets) are iteratively updated. Because MAP estimation needs the combinatorial search over all possible candidates to find the nonzero elements in the support vector, an approximated MAP estimation method is newly proposed. In addition, by extending the BMP-based imaging for a single FMCW MIMO radar, we develop a new distributed BMP-based imaging method when multiple FMCW MIMO radars are spatially distributed. Through the simulation, we confirm that the proposed BMP-based radar imaging methods outperform the conventional backprojection method or the orthogonal matching pursuit-based method with slightly increased computational complexity.

To estimate range and angle information of multiple targets, FMCW MIMO radars have been exploited with 2D MUSIC algorithms. To improve estimation accuracy, received signals from multiple FMCW MIMO radars are collected at the data fusion center and processed coherently, which increases data communication overhead and implementation complexity. To resolve them, we propose the distributed 2D MUSIC algorithm with coordinate transformation, in which 2D MUSIC algorithm is operated with respect to the reference radar’s coordinate at each radar in a distributed way. Rather than forwarding the raw data of received signal to the fusion center, each radar performs 2D MUSIC with its own received signal in the transformed coordinates. Accordingly, the distributed radars do not need to report all their measured signals to the data fusion center, but they forward their local cost function values of 2D MUSIC for the radar image region of interest. The data fusion center can then estimate the range and angle information of targets jointly from the aggregated cost function. By applying the proposed scheme to the experimentally measured data, its performance is verified in the real environment test.

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 analyzing both radar imaging and communication performances, we also propose a subcarrier allocation strategy such that a high achievable rate is obtained without sacrificing the radar imaging performance.