A Low-Complexity Near-Field Imaging Method for Multistatic Radar Systems Based on Receiver-Domain Decomposition

Area of Science:

• Radar Imaging
• Signal Processing
• Computational Electromagnetics

Background:

• Near-field multistatic radar imaging presents significant computational challenges due to nonlinear matched-filter operators.
• Sparse, large-aperture receiver configurations exacerbate these computational burdens.

Purpose of the Study:

• To develop a computationally efficient method for static-target near-field multistatic radar imaging.
• To mitigate the computational burden associated with traditional brute-force image formation.

Main Methods:

• Formulated a static-target model with a known signal envelope.
• Reduced the estimation problem to a coherent spatial matched filter using time-compressed data.
• Developed a receiver-domain decomposition approximation involving subaperture processing, coarse-grid imaging, phase correction, and interpolation.

Main Results:

• The proposed approximation significantly reduces computational complexity compared to brute-force methods.
• Phase correction was shown to smooth low-resolution block images, enabling accurate interpolation.
• Derived interpolation-based error bounds and analyzed computational complexity.

Conclusions:

• The receiver-domain decomposition offers a viable and accurate approximation for near-field multistatic radar imaging under specific conditions.
• Identified factors influencing the accuracy of the approximation, including phase compensation, coarse-grid resolution, and target characteristics.