Resolution analysis of microwave radar coincidence imaging for multiple imaging points

The azimuth super-resolution capability analysis of a Microwave Radar Coincidence Imaging (MRCI) system in complex scenes presents significant theoretical challenges. To address this, in this paper, we propose an Eigen Subspace Characterization (ESC) method for analyzing the azimuth resolution of an MRCI system, particularly in scenarios involving multiple imaging points within a coherent area. The ESC method is implemented by first constructing the eigen subspace from the reference signal matrix of the MRCI system using the Singular Value Decomposition (SVD) method. Within this eigen subspace, differences between reference signals are effectively characterized by the Euclidean distance between their corresponding vectors. Then, a criterion for resolving adjacent imaging points is established, whereby they become resolvable when the Euclidean distance between their reference signal vectors in the eigen subspace exceeds the noise vector norm (i.e., the noise power). Finally, the relationship between resolution and system parameters, including the signal-to-noise ratio (SNR), the number of imaging points, and the deployment of the transmitting array, is derived analytically. The proposed ESC method is validated through a series of simulations, providing theoretical insights for resolution analysis of the MRCI system across various imaging environments and requirements.